Stereotype Threat
Today is the first time that I've ever heard of stereotype threat. After learning more about this phenomenon, I don't think that I've ever felt it to be honest. I also haven't given the topic much thought either.  I'm sure that quite a lots of peple experience stereotype threat everyday, but don't really talk about it. I think that if they were to discuss it to someone outside of their group, they would feel like they are reinforcing the negative stereotype even further. So the condition continues to be exacerbated while nothing is done to address the situation.
My advice would be to seek someone out who you think might be facing this problem and bring it up slowly. If you can find a few people, you guys can create a group to discuss these issues. This can be a safe place where others can voice their insecurities in a safe place without any fear of judgement.


Here are the things I'm working on as of ...
Cardinal Spirits
Cardinal makes delicious, award-winning spirits and canned cocktails. I founded Cardinal in 2013 with Jeff Wuslich and an amazing team. We started distilling in 2014, and sold our first bottle in 2015. Since then we've grown substantially every year.
Cardinal Copacking
We spun up a private label production and copacking division from Cardinal Spirits a few years ago, and it has grown quickly. We are one of the most sought-after copackers in the spirits industry. With our new facility expansion we can produce over 1MM bottles per year.
GasMedix
I began working with GasMedix as a marketing advisor in 2019. The founder (my Dad), Jim Quirk, has been in the medical gas industry since 1982, and our team specializes in providing all of a healthcare organization’s medical gas equipment needs.
Here are things I did before...
Alongside my friends Erik Nelson and Aaron Valdez, I created the web video collaborative Wreck & Salvage. We spent several years in the mid-to-late 2000's animating videos, and remixing videos from the internet, for the internet. 🔗 Wreck & Salvage
I worked with Marc Guyer, the founder of Cheddar (previously CheddarGetter) to market and sell Cheddar's online subscription billing software. I learned how to use Github, and Marc learned how to act in marketing videos. 🔗 Cheddar
Over two months after the BP oil spill began seeping into the Gulf of Mexico, the flow had not been stopped. I had an idea for how to compel greater action from BP. I created a successful Kickstarter campaign to buy 100 vuvuzelas and hire 100 vuvuzela players off Craigslist to play in front of BP's International Headquarters in London for an entire work day. 🔗 Vuvuzelas for BP
My internet footprint.


Note that the robot is a little bit under-optimized. Can you (the viewers) explain why?
Click to see a better, more optimized version of the robot.
And the best optimized robot is :
Let me know if you have any idea how the robot can be optimized further!


YouTube Channel Integration for Zendesk turns YouTube comments on videos from your channel and channel comments into Zendesk tickets in your Zendesk instance.
Stack Exchange Channel Integration for Zendesk turns Stack Exchange questions by tag into Zendesk tickets in your Zendesk instance.
Docker Hub Repositories Comments Integration for Zendesk turns Docker Hub comments on repositories into Zendesk tickets in your Zendesk instance.
Guide Comments Integration for Zendesk turns Guide Community or Knowledge Base comments into Zendesk tickets in your Zendesk instance.
It's implemented using a brand-new Channel Framework from Zendesk. The Channel framework lets us build integrations — two-way ticket-creation services — that bring customer communications from anywhere else into Zendesk. Examples could include ratings and reviews in Yelp, app stores, Amazon or Ebay; communities like Reddit or Quora; or messaging services like Skype, WeChat, and WhatsApp.
To use the YouTube Channel Integration, you'll need to install the app from Zendesk's App
Marketplace.
To install the YouTube Channel integration:
To use the Stack Exchange Channel Integration, you'll need to install the app from Zendesk's App
Marketplace.
To install the Stack Exchange Channel integration:
To use the Docker Hub Repository Comments Integration, you'll need to install the app from Zendesk's App
Marketplace.
To install the Docker Hub Repository Comments channel integration:
To use the Guide Comments Integration, you'll need to install the app from Zendesk's App
Marketplace.
To install the Guide Comments channel integration:
Most of the integration source code (at least for YouTube, Stack Exchange, Docker Hub) will be released publicly under MIT license and is published on GitHub.
Constributions will be more than welcome as soon as we release the current version of the application on GitHub. It's currently written in PHP and can be considered a very simple application (more of a prototype, in fact, so don't expect any fancy development practices there).
Currently the integration is hosted on single Digital Ocean container in Frankfurt, paid by the author. The design of Zendesk's channel framework implies that there is always some intermediary server between Zendesk and external service (e.g. YouTube) which is used to convert the API of external service (YouTube) to Zendesk-readable endpoints, there is no way to run the integration without intermediary server.
Yes, it's possible, but you'll need to have your own intermediary server with installed SSL certificate (I'll try to provide as much configuration information as I can).
There are no SLAs or any other reliability or security guarantees by developers of this integration, so you're using it on your own risk without any obligations or libility from author's or developers' sides. Still, we try to keep the service as reliable as possible and take care of security issues too (we use this integration ourselves, after all).
In theory there is a way for the integration to know everything which you pass to Zendesk from YouTube or other services. Having said that, the application has been designed in a way that it does not keep ANY information about your Zendesk instance or processed tickets/requests/etc. All the configuration information is kept in Zendesk and it's returned back to our integration every time it's needed - that protects us from data leak (no leak if we don't keep data) and secures the integration. The only time when we keep sensible information in application logs is when you explicitly enable "Send advanced debugging data to app developers" option in the configuration, so please don't enable it unless we try to troubleshoot some problems. We keep your name/email/instance address in MailChimp which is used for email communications with you - no sensible or confidential information (such as requests you pass via servers) is saved there.
Despite the fact that the developer of this integration works at JetBrains and initially this integration has been written for JetBrains Zendesk Instance, JetBrains cannot hold any liability for the use of this integration, will not take any responsibility and will not answer requests about it, it's not a product of JetBrains.
We might consider building a paid version of the integration (on top of the free one) which would then have additional features worth paying for, in addition to SLAs, guaranteed support and other goodies, but we want basic functionality to stay FREE of charge (at least for YouTube, Stack Exchange, Docker Hub) and some of it open sourced for the benefit of community (at least YouTube, Stack Exchange, Docker Hub). Currently there are no defined plans to build a paid version. Donations are welcomed though (see the next item).
You can donate to support the further development, maintenance and payment for the integration server, or alternatively donate to charity
We're going to improve this integration and add new features, and also develop some other channel integrations (for example, Stack Exchange / Stack Overflow Integration is on its way).
Mikhail Vink, Global Marketing Programs Manager at JetBrains. You can contact me via email<EMAIL_ADDRESS>You can subscribe to the news from the home page. You're also subscribed to the news if you use the integration - it's the only way we can actually contact you in case of problems or updates.


Everything You Need To Become A Machine Learner
Everything You Need To Become A Machine Learner
This list of resources is specifically targeted at Web Developers and Data Scientists…. so do with it what you will…This list borrows heavily from multiple lists created by : sindresorhus
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
The goal of AI is to create computer models that exhibit “intelligent behaviors” like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.
Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”
- [📖] Delivering Happiness
- [📖] Good to Great: Why Some Companies Make the Leap…And Others Don’t
- [📖] Hello, Startup: A Programmer’s Guide to Building Products, Technologies, and Teams
- [📖] How Google Works
- [📖] Learn to Earn: A Beginner’s Guide to the Basics of Investing and Business
- [📖] Rework
- [📖] The Airbnb Story
- [📖] The Personal MBA
- [ ] Facebook: Digital marketing: get started
- [ ] Facebook: Digital marketing: go further
- [ ] Google Analytics for Beginners
- [ ] Moz: The Beginner’s Guide to SEO
- [ ] Smartly: Marketing Fundamentals
- [ ] Treehouse: SEO Basics
- [🅤]ꭏ App Monetization
- [🅤]ꭏ App Marketing
- [🅤]ꭏ How to Build a Startup
Natural language processing
Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.
Neural networks
Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.
In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.
Be familiar with how Machine Learning is applied at other companies
- [📰] How Facebook uses super-efficient AI models to detect hate speech
- [📰] Recent Advances in Google Translate
- [📰] Cannes: HowMachine Learning saves us $1.7M a year on document previews
- [📰] Machine Learning @ Monzo in 2020
- [📰] How image search works at Dropbox
- [ ] Real-world AI Case Studies
- [ ] Andrej Karpathy on AI at Tesla (Full Stack Deep Learning — August 2018)
- [ ] Jai Ranganathan at Data Science at Uber (Full Stack Deep Learning — August 2018)
-
[ ]
John Apostolopoulos of Cisco discusses “Machine Learning in
Networking”
0:48:44
-
[ ]
Joaquin Candela, Director of Applied Machine Learning ,
Facebook in conversation with Esteban Arcaute
0:52:27
-
[ ]
Eric Colson, Chief Algorithms Officer, Stitch Fix
0:53:57
-
[ ]
Claudia Perlich, Advisor to Dstillery and Adjunct Professor
NYU Stern School of Business
0:51:59
-
[ ]
Jeff Dean, Google Senior Fellow and SVP Google AI — Deep
Learning to Solve Challenging Problems
0:58:45
-
[ ]
James Parr, Director of Frontier Development Lab (NASA),
FDL Europe & CEO, Trillium Technologies
0:55:46
-
[ ]
Daphne Koller, Founder & CEO of Insitro — In
Conversation with Carlos Bustamante
0:49:29
-
[ ]
Eric Horvitz, Microsoft Research — AI in the Open World:
Advances, Aspirations, and Rough Edges
0:56:11
-
[ ]
Tony Jebara, Netflix — Machine Learning for Recommendation
and Personalization
0:55:20
- [💻] Analyzing Police Activity with pandas
- [💻] HR Analytics in Python: Predicting Employee Churn
- [💻] Predicting Customer Churn in Python
-
[📺 ]
How does YouTube recommend videos? — AI EXPLAINED!
0:33:53
-
[📺 ]
How does Google Translate’s AI work?
0:15:02
-
[📺 ]
Data Science in Finance
0:17:52
- [📺 ] The Age of AI
-
[ ]
How Far is Too Far? | The Age of A.I.
0:34:39
-
[ ]
Healed through A.I. | The Age of A.I.
0:39:55
-
[ ]
Using A.I. to build a better human | The Age of A.I.
0:44:27
-
[ ]
Love, art and stories: decoded | The Age of A.I.
0:38:57
-
[ ]
The ‘Space Architects’ of Mars | The Age of A.I.
0:30:10
-
[ ]
Will a robot take my job? | The Age of A.I.
0:36:14
-
[ ]
Saving the world one algorithm at a time | The Age of
A.I.
0:46:37
-
[ ]
How A.I. is searching for Aliens | The Age of A.I.
0:36:12
- [📺 ] Using Intent Data to Optimize the Self-Solve Experience
- [📺 ] Trillions of Questions, No Easy Answers: A (home) movie about how Google Search works
- [📺 ] Google Machine Learning System Design Mock Interview
- [📺 ] Netflix Machine Learning Mock Interview: Type-ahead Search
- [📺 ] Machine Learning design: Search engine for Q&A
- [📺 ] Engineering Systems for Real-Time Predictions @DoorDash
- [📺 ] How Gmail Uses Iterative Design, Machine Learning and AI to Create More Assistive Features
- [📺 ] Wayfair Data Science Explains It All: Human-in-the-loop Systems
- [📺 ] Leaving the lab: Building NLP applications that real people can use
- [📺 ] Machine Learning at Uber (Natural Language Processing Use Cases)
- [📺 ] Google Wave: Natural Language Processing
- [📺 ] Natural Language Understanding in Alexa
- [📺 ] The Machine Learning Behind Alexa’s AI Systems
- [📺 ] Ines Montani Keynote — Applied NLP Thinking
- [📺 ] Lecture 9: Lukas Biewald
- [📺 ] Lecture 13: Research Directions
- [📺 ] Lecture 14: Jeremy Howard
- [📺 ] Lecture 15: Richard Socher
-
[📺 ]
Machine learning across industries with Vicki Boykis
0:34:02
-
[📺 ]
Rachael Tatman — Conversational A.I. and Linguistics
0:36:51
-
[📺 ]
Nicolas Koumchatzky — Machine Learning in Production for
Self Driving Cars
0:44:56
-
[📺 ]
Brandon Rohrer — Machine Learning in Production for
Robots
0:34:31
- [📺 ] [CVPR’21 WAD] Keynote — Andrej Karpathy, Tesla
Be able to frame anMachine Learning problem
· [ ] AWS: Types of Machine Learning Solutions
· [📰] Apply Machine Learning to your Business
· [📰] Resilience and Vibrancy: The 2020 Data & AI Landscape
· [📰] Software 2.0
· [📰] Highlights from ICML 2020
· [📰] A Peek at Trends in Machine Learning
· [📰] How to deliver on Machine Learning projects
· [📰] Data Science as a Product
· [📰] Customer service is full of machine learning problems
· [📰] Choosing Problems in Data Science and Machine Learning
· [📰] Why finance is deploying natural language processing
· [📰] The Last 5 Years In Deep Learning
· [📰] Always start with a stupid model, no exceptions.
· [📰] Most impactful AI trends of 2018: the rise ofMachine Learning Engineering
· [📰] Building machine learning products: a problem well-defined is a problem half-solved.
· [📰] Simple considerations for simple people building fancy neural networks
· [📰] Maximizing Business Impact with Machine Learning
· [📖] AI Superpowers: China, Silicon Valley, and the New World Order
· [📖] A Human’s Guide to Machine Intelligence
· [📖] The Future Computed
· [📖] Machine Learning Yearning by Andrew Ng
· [📖] Prediction Machines: The Simple Economics of Artificial Intelligence
· [📖] Building Machine Learning Powered Applications: Going from Idea to Product
· [ ] Coursera: AI For Everyone
· [💻] Data Science for Everyone
· [💻] Machine Learning with the Experts: School Budgets
· [💻] Machine Learning for Everyone
· [💻] Data Science for Managers
· [ ] Facebook: Field Guide to Machine Learning
· [G] Introduction to Machine Learning Problem Framing
· [ ] Pluralsight: How to Think About Machine Learning Algorithms
· [ ] State of AI Report 2020
· [📺 ] Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019
· [📺 ] Hugging Face, Transformers | NLP Research and Open Source | Interview with Julien Chaumond
· [📺 ] Vincent Warmerdam — Playing by the Rules-Based-Systems | PyData Eindhoven 2020
Be familiar with data ethics
- [📰] How to Detect Bias in AI
- [ ] Netflix: Coded Bias
- [ ] Netflix: The Great Hack
- [ ] Netflix: The Social Dilemma
- [ ] Practical Data Ethics
- [ ] Lesson 1: Disinformation
- [ ] Lesson 2: Bias & Fairness
- [ ] Lesson 3: Ethical Foundations & Practical Tools
- [ ] Lesson 4: Privacy and surveillance
- [ ] Lesson 4 continued: Privacy and surveillance
- [ ] Lesson 5.1: The problem with metrics
- [ ] Lesson 5.2: Our Ecosystem, Venture Capital, & Hypergrowth
- [ ] Lesson 5.3: Losing the Forest for the Trees, guest lecture by Ali Alkhatib
- [ ] Lesson 6: Algorithmic Colonialism, and Next Steps
- [📺 ] Lecture 9: Ethics (Full Stack Deep Learning — Spring 2021) 1:04:50
- [📺 ] SE4AI: Ethics and Fairness 1:18:37
- [📺 ] SE4AI: Security 1:18:24
- [📺 ] SE4AI: Safety 1:17:37
Be able to import data from multiple sources
- [ ] Docs: Beautiful Soup Documentation
- [💻] Importing Data in Python (Part 2)
- [💻] Web Scraping in Python
Be able to setup data annotation efficiently
[📰] Create A Synthetic Image Dataset — The “What”, The “Why” and The “How”
- [📰] We need Synthetic Data
- [📰] Weak Supervision for Online Discussions
- [📰] Machine Learning Infrastructure Tools for Data Preparation
- [📰] Exploring the Role of Human Raters in Creating NLP Datasets
- [📰] Inter-Annotator Agreement (IAA)
- [📰] How to compute inter-rater reliability metrics (Cohen’s Kappa, Fleiss’s Kappa, Cronbach Alpha, Krippendorff Alpha, Scott’s Pi, Inter-class correlation) in Python
- [📺 ] Snorkel: Dark Data and Machine Learning — Christopher Ré
- [📺 ] Training a NER Model with Prodigy and Transfer Learning
- [📺 ] Training a New Entity Type with Prodigy — annotation powered by active learning
- [📺 ] ECCV 2020 WSL tutorial: 4. Human-in-the-loop annotations
- [📺 ] Active Learning: Why Smart Labeling is the Future of Data Annotation | Alectio
- [📺 ] Lecture 8: Data Management (Full Stack Deep Learning — Spring 2021) 0:59:42
- [📺 ] Lab 6: Data Labeling (Full Stack Deep Learning — Spring 2021) 0:05:06
- [📺 ] Lecture 6: Data Management
- [📺 ] SE4AI: Data Quality 1:07:15
- [📺 ] SE4AI: Data Programming and Intro to Big Data Processing 0:33:04
- [📺 ] SE4AI: Managing and Processing Large Datasets 1:21:27
Be able to manipulate data with Numpy
- [📰] A Visual Intro to NumPy and Data Representation
- [📰] Good practices with numpy random number generators
- [📰] NumPy Illustrated: The Visual Guide to NumPy
- [📰] NumPy Fundamentals for Data Science and Machine Learning
- [💻] Intro to Python for Data Science
- [ ] Pluralsight: Working with Multidimensional Data Using NumPy
Be able to manipulate data with Pandas
- [📰] Visualizing Pandas’ Pivoting and Reshaping Functions
- [📰] A Gentle Visual Intro to Data Analysis in Python Using Pandas
- [📰] Comprehensive Guide to Grouping and Aggregating with Pandas
- [📰] 8 Python Pandas Value_counts() tricks that make your work more efficient
- [💻] pandas Foundations
- [💻] Pandas Joins for Spreadsheet Users
- [💻] Manipulating DataFrames with pandas
- [💻] Merging DataFrames with pandas
- [💻] Data Manipulation with pandas
- [💻] Optimizing Python Code with pandas
- [💻] Streamlined Data Ingestion with pandas
- [💻] Analyzing Marketing Campaigns with pandas
- [ ] edX: Implementing Predictive Analytics with Spark in Azure HDInsight
- [📰] Modern Pandas
- [ ] Modern Pandas (Part 1)
- [ ] Modern Pandas (Part 2)
- [ ] Modern Pandas (Part 3)
- [ ] Modern Pandas (Part 4)
- [ ] Modern Pandas (Part 5)
- [ ] Modern Pandas (Part 6)
- [ ] Modern Pandas (Part 7)
- [ ] Modern Pandas (Part 8)
Be able to manipulate data in spreadsheets
- [💻] Spreadsheet basics
- [💻] Data Analysis with Spreadsheets
- [💻] Intermediate Spreadsheets for Data Science
- [💻] Pivot Tables with Spreadsheets
- [💻] Data Visualization in Spreadsheets
- [💻] Introduction to Statistics in Spreadsheets
- [💻] Conditional Formatting in Spreadsheets
- [💻] Marketing Analytics in Spreadsheets
- [💻] Error and Uncertainty in Spreadsheets
- [ ] edX: Analyzing and Visualizing Data with Excel
Be able to manipulate data in databases
- [ ] Codecademy: SQL Track
- [💻] Intro to SQL for Data Science
- [💻] Introduction to MongoDB in Python
- [💻] Intermediate SQL
- [💻] Exploratory Data Analysis in SQL
- [💻] Joining Data in PostgreSQL
- [💻] Querying with TransactSQL
- [💻] Introduction to Databases in Python
- [💻] Reporting in SQL
- [💻] Applying SQL to Real-World Problems
- [💻] Analyzing Business Data in SQL
- [💻] Data-Driven Decision Making in SQL
- [💻] Database Design
- [🅤]ꭏ SQL for Data Analysis
- [🅤]ꭏ Intro to relational database
- [🅤]ꭏ Database Systems Concepts & Design
Be able to use Linux
Resources:
Bash Proficiency In Under 15 Minutes
Cheat sheet and in-depth explanations located below main article contents… The UNIX shell program interprets user…
These Are The Bash Shell Commands That Stand Between Me And Insanity
I will not profess to be a bash shell wizard… but I have managed to scour some pretty helpful little scripts from Stack…
Bash Commands That Save Me Time and Frustration
Here’s a list of bash commands that stand between me and insanity.
Life Saving Bash Scripts Part 2
I am not saying they’re in any way special compared with other bash scripts… but when I consider that you can never…
What Are Bash Aliases And Why Should You Be Using Them!
A Bash alias is a method of supplementing or overriding Bash commands with new ones. Bash aliases make it easy for…
BASH CHEAT SHEET
My Bash Cheatsheet Index:
holy grail of learning bash
- [📰] Streamline your projects using Makefile
- [📰] Understand Linux Load Averages and Monitor Performance of Linux
- [📰] Command-line Tools can be 235x Faster than your Hadoop Cluster
- [ ] Calmcode: makefiles
- [ ] Calmcode: entr
- [ ] Codecademy: Learn the Command Line
- [💻] Introduction to Shell for Data Science
- [💻] Introduction to Bash Scripting
- [💻] Data Processing in Shell
- [ ] MIT: The Missing Semester of CS Education
- [ ] Lecture 1: Course Overview + The Shell (2020) 0:48:16
- [ ] Lecture 2: Shell Tools and Scripting (2020) 0:48:55
- [ ] Lecture 3: Editors (vim) (2020) 0:48:26
- [ ] Lecture 4: Data Wrangling (2020) 0:50:03
- [ ] Lecture 5: Command-line Environment (2020) 0:56:06
- [ ] Lecture 6: Version Control (git) (2020) 1:24:59
- [ ] Lecture 7: Debugging and Profiling (2020) 0:54:13
- [ ] Lecture 8: Metaprogramming (2020) 0:49:52
- [ ] Lecture 9: Security and Cryptography (2020) 1:00:59
- [ ] Lecture 10: Potpourri (2020) 0:57:54
- [ ] Lecture 11: Q&A (2020) 0:53:52
- [ ] Thoughtbot: Mastering the Shell
- [ ] Thoughtbot: tmux
- [🅤]ꭏ Linux Command Line Basics
- [🅤]ꭏ Shell Workshop
- [🅤]ꭏ Configuring Linux Web Servers
- [ ] Web Bos: Command Line Power User
- [📺 ] GNU Parallel
Be able to perform feature selection and engineering
- [📰] Tips for Advanced Feature Engineering
- [📰] Preparing data for a machine learning model
- [📰] Feature selection for a machine learning model
- [📰] Learning from imbalanced data
- [📰] Hacker’s Guide to Data Preparation for Machine Learning
- [📰] Practical Guide to Handling Imbalanced Datasets
- [💻] Analyzing Social Media Data in Python
- [💻] Dimensionality Reduction in Python
- [💻] Preprocessing for Machine Learning in Python
- [💻] Data Types for Data Science
- [💻] Cleaning Data in Python
- [💻] Feature Engineering for Machine Learning in Python
- [💻] Importing & Managing Financial Data in Python
- [💻] Manipulating Time Series Data in Python
- [💻] Working with Geospatial Data in Python
- [💻] Analyzing IoT Data in Python
- [💻] Dealing with Missing Data in Python
- [💻] Exploratory Data Analysis in Python
- [ ] edX: Data Science Essentials
- [🅤]ꭏ Creating an Analytical Dataset
- [📺 ] AppliedMachine Learning 2020–04 — Preprocessing 1:07:40
- [📺 ] AppliedMachine Learning 2020–11 — Model Inspection and Feature Selection 1:15:15
Be able to experiment in a notebook
- [📰] Securely storing configuration credentials in a Jupyter Notebook
- [📰] Automatically Reload Modules with %autoreload
- [ ] Calmcode: ipywidgets
- [ ] Documentation: Jupyter Lab
- [ ] Pluralsight: Getting Started with Jupyter Notebook and Python
- [📺 ] William Horton — A Brief History of Jupyter Notebooks
- [📺 ] I Like Notebooks
- [📺 ] I don’t like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)
- [📺 ] Ryan Herr — After model.fit, before you deploy| JupyterCon 2020
- [📺 ] nbdev live coding with Hamel Husain
- [📺 ] How to Use JupyterLab
Be able to visualize data
- [📰] Creating a Catchier Word Cloud Presentation
- [📰] Effectively Using Matplotlib
- [💻] Introduction to Data Visualization with Python
- [💻] Introduction to Seaborn
- [💻] Introduction to Matplotlib
- [💻] Intermediate Data Visualization with Seaborn
- [💻] Visualizing Time Series Data in Python
- [💻] Improving Your Data Visualizations in Python
- [💻] Visualizing Geospatial Data in Python
- [💻] Interactive Data Visualization with Bokeh
- [📺 ] AppliedMachine Learning 2020–02 Visualization and matplotlib 1:07:30
Be able to model problems mathematically
- [ ] 3Blue1Brown: Essence of Calculus
- [ ] The Essence of Calculus, Chapter 1 0:17:04
- [ ] The paradox of the derivative | Essence of calculus, chapter 2 0:17:57
- [ ] Derivative formulas through geometry | Essence of calculus, chapter 3 0:18:43
- [ ] Visualizing the chain rule and product rule | Essence of calculus, chapter 4 0:16:52
- [ ] What’s so special about Euler’s number e? | Essence of calculus, chapter 5 0:13:50
- [ ] Implicit differentiation, what’s going on here? | Essence of calculus, chapter 6 0:15:33
- [ ] Limits, L’Hôpital’s rule, and epsilon delta definitions | Essence of calculus, chapter 7 0:18:26
- [ ] Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8 0:20:46
- [ ] What does area have to do with slope? | Essence of calculus, chapter 9 0:12:39
- [ ] Higher order derivatives | Essence of calculus, chapter 10 0:05:38
- [ ] Taylor series | Essence of calculus, chapter 11 0:22:19
- [ ] What they won’t teach you in calculus 0:16:22
- [ ] 3Blue1Brown: Essence of linear algebra
- [ ] Vectors, what even are they? | Essence of linear algebra, chapter 1 0:09:52
- [ ] Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2 0:09:59
- [ ] Linear transformations and matrices | Essence of linear algebra, chapter 3 0:10:58
- [ ] Matrix multiplication as composition | Essence of linear algebra, chapter 4 0:10:03
- [ ] Three-dimensional linear transformations | Essence of linear algebra, chapter 5 0:04:46
- [ ] The determinant | Essence of linear algebra, chapter 6 0:10:03
- [ ] Inverse matrices, column space and null space | Essence of linear algebra, chapter 7 0:12:08
- [ ] Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8 0:04:27
- [ ] Dot products and duality | Essence of linear algebra, chapter 9 0:14:11
- [ ] Cross products | Essence of linear algebra, Chapter 10 0:08:53
- [ ] Cross products in the light of linear transformations | Essence of linear algebra chapter 11 0:13:10
- [ ] Cramer’s rule, explained geometrically | Essence of linear algebra, chapter 12 0:12:12
- [ ] Change of basis | Essence of linear algebra, chapter 13 0:12:50
- [ ] Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14 0:17:15
- [ ] Abstract vector spaces | Essence of linear algebra, chapter 15 0:16:46
- [ ] 3Blue1Brown: Neural networks
- [ ] But what is a Neural Network? | Deep learning, chapter 1 0:19:13
- [ ] Gradient descent, how neural networks learn | Deep learning, chapter 2 0:21:01
- [ ] What is backpropagation really doing? | Deep learning, chapter 3 0:13:54
- [ ] Backpropagation calculus | Deep learning, chapter 4 0:10:17
- [📰] A Visual Tour of Backpropagation
- [📰] Entropy, Cross Entropy, and KL Divergence
- [📰] Interview Guide to Probability Distributions
- [📰] Introduction to Linear Algebra for Applied Machine Learning with Python
- [📰] Entropy of a probability distribution — in layman’s terms
- [📰] KL Divergence — in layman’s terms
- [📰] Probability Distributions
- [📰] Relearning Matrices as Linear Functions
- [📰] You Could Have Come Up With Eigenvectors — Here’s How
- [📰] PageRank — How Eigenvectors Power the Algorithm Behind Google Search
- [📰] Interactive Visualization of Why Eigenvectors Matter
- [📰] Cross-Entropy and KL Divergence
- [📰] Why Randomness Is Information?
- [📰] Basic Probability Theory
- [📰] Math You Need to Succeed InMachine Learning Interviews
- [📖] Basics of Linear Algebra for Machine Learning
- [💻] Introduction to Statistics in Python
- [💻] Foundations of Probability in Python
- [💻] Statistical Thinking in Python (Part 1)
- [💻] Statistical Thinking in Python (Part 2)
- [💻] Statistical Simulation in Python
- [ ] edX: Essential Statistics for Data Analysis using Excel
- [ ] Computational Linear Algebra for Coders
- [ ] Khan Academy: Precalculus
- [ ] Khan Academy: Probability
- [ ] Khan Academy: Differential Calculus
- [ ] Khan Academy: Multivariable Calculus
- [ ] Khan Academy: Linear Algebra
- [ ] MIT: 18.06 Linear Algebra (Professor Strang)
- [ ] 1. The Geometry of Linear Equations 0:39:49
- [ ] 2. Elimination with Matrices. 0:47:41
- [ ] 3. Multiplication and Inverse Matrices 0:46:48
- [ ] 4. Factorization into A = LU 0:48:05
- [ ] 5. Transposes, Permutations, Spaces R^n 0:47:41
- [ ] 6. Column Space and Nullspace 0:46:01
- [ ] 9. Independence, Basis, and Dimension 0:50:14
- [ ] 10. The Four Fundamental Subspaces 0:49:20
- [ ] 11. Matrix Spaces; Rank 1; Small World Graphs 0:45:55
- [ ] 14. Orthogonal Vectors and Subspaces 0:49:47
- [ ] 15. Projections onto Subspaces 0:48:51
- [ ] 16. Projection Matrices and Least Squares 0:48:05
- [ ] 17. Orthogonal Matrices and Gram-Schmidt 0:49:09
- [ ] 21. Eigenvalues and Eigenvectors 0:51:22
- [ ] 22. Diagonalization and Powers of A 0:51:50
- [ ] 24. Markov Matrices; Fourier Series 0:51:11
- [ ] 25. Symmetric Matrices and Positive Definiteness 0:43:52
- [ ] 27. Positive Definite Matrices and Minima 0:50:40
- [ ] 29. Singular Value Decomposition 0:40:28
- [ ] 30. Linear Transformations and Their Matrices 0:49:27
- [ ] 31. Change of Basis; Image Compression 0:50:13
- [ ] 33. Left and Right Inverses; Pseudoinverse 0:41:52
- [ ] StatQuest: Statistics Fundamentals
- [ ] StatQuest: Histograms, Clearly Explained 0:03:42
- [ ] StatQuest: What is a statistical distribution? 0:05:14
- [ ] StatQuest: The Normal Distribution, Clearly Explained!!! 0:05:12
- [ ] Statistics Fundamentals: Population Parameters 0:14:31
- [ ] Statistics Fundamentals: The Mean, Variance and Standard Deviation 0:14:22
- [ ] StatQuest: What is a statistical model? 0:03:45
- [ ] StatQuest: Sampling A Distribution 0:03:48
- [ ] Hypothesis Testing and The Null Hypothesis 0:14:40
- [ ] Alternative Hypotheses: Main Ideas!!! 0:09:49
- [ ] p-values: What they are and how to interpret them 0:11:22
- [ ] How to calculate p-values 0:25:15
- [ ] p-hacking: What it is and how to avoid it! 0:13:44
- [ ] Statistical Power, Clearly Explained!!! 0:08:19
- [ ] Power Analysis, Clearly Explained!!! 0:16:44
- [ ] Covariance and Correlation Part 1: Covariance 0:22:23
- [ ] Covariance and Correlation Part 2: Pearson’s Correlation 0:19:13
- [ ] StatQuest: R-squared explained 0:11:01
- [ ] The Central Limit Theorem 0:07:35
- [ ] StatQuickie: Standard Deviation vs Standard Error 0:02:52
- [ ] StatQuest: The standard error 0:11:43
- [ ] StatQuest: Technical and Biological Replicates 0:05:27
- [ ] StatQuest — Sample Size and Effective Sample Size, Clearly Explained 0:06:32
- [ ] Bar Charts Are Better than Pie Charts 0:01:45
- [ ] StatQuest: Boxplots, Clearly Explained 0:02:33
- [ ] StatQuest: Logs (logarithms), clearly explained 0:15:37
- [ ] StatQuest: Confidence Intervals 0:06:41
- [ ] StatQuickie: Thresholds for Significance 0:06:40
- [ ] StatQuickie: Which t test to use 0:05:10
- [ ] StatQuest: One or Two Tailed P-Values 0:07:05
- [ ] The Binomial Distribution and Test, Clearly Explained!!! 0:15:46
- [ ] StatQuest: Quantiles and Percentiles, Clearly Explained!!! 0:06:30
- [ ] StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained 0:06:55
- [ ] StatQuest: Quantile Normalization 0:04:51
- [ ] StatQuest: Probability vs Likelihood 0:05:01
- [ ] StatQuest: Maximum Likelihood, clearly explained!!! 0:06:12
- [ ] Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0 0:09:39
- [ ] Why Dividing By N Underestimates the Variance 0:17:14
- [ ] Maximum Likelihood for the Binomial Distribution, Clearly Explained!!! 0:11:24
- [ ] Maximum Likelihood For the Normal Distribution, step-by-step! 0:19:50
- [ ] StatQuest: Odds and Log(Odds), Clearly Explained!!! 0:11:30
- [ ] StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 0:16:20
- [ ] Live 2020–04–20!!! Expected Values 0:33:00
- [🅤]ꭏ Eigenvectors and Eigenvalues
- [🅤]ꭏ Linear Algebra Refresher
- [🅤]ꭏ Statistics
- [🅤]ꭏ Intro to Descriptive Statistics
- [🅤]ꭏ Intro to Inferential Statistics
Be able to setup project structure
- [📰] pydantic
- [📰] Organizing machine learning projects: project management guidelines
- [📰] Logging and Debugging in Machine Learning — How to use Python debugger and the logging module to find errors in your AI application
- [📰] Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation
- [📰] Configuring Google Colab Like A Pro
- [📰] Stop using print, start using loguru in Python
- [📰] Hypermodern Python
- [📰] Hypermodern Python Chapter 2: Testing
- [📰] Hypermodern Python Chapter 3: Linting
- [📰] Hypermodern Python Chapter 4: Typing
- [📰] Hypermodern Python Chapter 5: Documentation
- [📰] Hypermodern Python Chapter 6: CI/CD
- [📰] Push and pull: when and why to update your dependencies
- [📰] Reproducible and upgradable Conda environments: dependency management with conda-lock
- [📰] Options for packaging your Python code: Wheels, Conda, Docker, and more
- [📰] Making model training scripts robust to spot interruptions
- [ ] Calmcode: logging
- [ ] Calmcode: tqdm
- [ ] Calmcode: virtualenv
- [ ] Coursera: Structuring Machine Learning Projects
- [ ] Doc: Python Lifecycle Training
- [💻] Introduction to Data Engineering
- [💻] Conda Essentials
- [💻] Conda for Building & Distributing Packages
- [💻] Software Engineering for Data Scientists in Python
- [💻] Designing Machine Learning Workflows in Python
- [💻] Object-Oriented Programming in Python
- [💻] Command Line Automation in Python
- [💻] Creating Robust Python Workflows
- [ ] Developing Python Packages
- [ ] Treehouse: Object Oriented Python
- [ ] Treehouse: Setup Local Python Environment
- [🅤]ꭏ Writing READMEs
- [📺 ] Lecture 1: Introduction to Deep Learning
- [📺 ] Lecture 2: Setting Up Machine Learning Projects
- [📺 ] Lecture 3: Introduction to the Text Recognizer Project
- [📺 ] Lecture 4: Infrastructure and Tooling
- [📺 ] Hydra configuration
- [📺 ] Continuous integration
- [📺 ] Data Engineering +Machine Learning + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16
- [📺 ] OO Design and Testing Patterns for Machine Learning with Chris Gerpheide
- [📺 ] Tutorial: Sebastian Witowski — Modern Python Developer’s Toolkit
- [📺 ] Lecture 13:Machine Learning Teams (Full Stack Deep Learning — Spring 2021) 0:58:13
- [📺 ] Lecture 5:Machine Learning Projects (Full Stack Deep Learning — Spring 2021) 1:13:14
- [📺 ] Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning — Spring 2021) 1:07:21
Be able to version control code
Basic Git Work Flow.levelup.gitconnected.com
Update: here’s a repo full of helpful repos:levelup.gitconnected.com
- [📰] Mastering Git Stash Workflow
- [📰] How to Become a Master of Git Tags
- [📰] How to track large files in Github / Bitbucket? Git LFS to the rescue
- [📰] Keep your git directory clean with git clean and git trash
- [ ] Codecademy: Learn Git
- [ ] Code School: Git Real
- [💻] Introduction to Git for Data Science
- [ ] Thoughtbot: Mastering Git
- [🅤]ꭏ GitHub & Collaboration
- [🅤]ꭏ How to Use Git and GitHub
- [🅤]ꭏ Version Control with Git
- [📺 ] 045 Introduction to Git LFS
- [📺 ] Git & Scripting
Be able to setup model validation
- [📰] Evaluating a machine learning model
- [📰] Validating your Machine Learning Model
- [📰] Measuring Performance: AUPRC and Average Precision
- [📰] Measuring Performance: AUC (AUROC)
- [📰] Measuring Performance: The Confusion Matrix
- [📰] Measuring Performance: Accuracy
- [📰] ROC Curves: Intuition Through Visualization
- [📰] Precision, Recall, Accuracy, and F1 Score for Multi-Label Classification
- [📰] The Complete Guide to AUC and Average Precision: Simulations and Visualizations
- [📰] Best Use of Train/Val/Test Splits, with Tips for Medical Data
- [📰] The correct way to evaluate online machine learning models
- [📰] Proxy Metrics
- [📺 ] Accuracy as a Failure
- [📺 ] AppliedMachine Learning 2020–09 — Model Evaluation and Metrics 1:18:23
- [📺 ] Machine Learning Fundamentals: Cross Validation 0:06:04
- [📺 ] Machine Learning Fundamentals: The Confusion Matrix 0:07:12
- [📺 ] Machine Learning Fundamentals: Sensitivity and Specificity 0:11:46
- [📺 ] Machine Learning Fundamentals: Bias and Variance 0:06:36
- [📺 ] ROC and AUC, Clearly Explained! 0:16:26
Be familiar with inner working of models
Bays theorem is super interesting and applicable ==> — [📰] Naive Bayes classification
- [📰] Linear regression
- [📰] Polynomial regression
- [📰] Logistic regression
- [📰] Decision trees
- [📰] K-nearest neighbors
- [📰] Support Vector Machines
- [📰] Random forests
- [📰] Boosted trees
- [📰] Hacker’s Guide to Fundamental Machine Learning Algorithms with Python
- [📰] Neural networks: activation functions
- [📰] Neural networks: training with backpropagation
- [📰] Neural Network from scratch-part 1
- [📰] Neural Network from scratch-part 2
- [📰] Perceptron to Deep-Neural-Network
- [📰] One-vs-Rest strategy for Multi-Class Classification
- [📰] Multi-class Classification — One-vs-All & One-vs-One
- [📰] One-vs-Rest and One-vs-One for Multi-Class Classification
- [📰] Deep Learning Algorithms — The Complete Guide
- [📰] Machine Learning Techniques Primer
- [ ] AWS: Understanding Neural Networks
- [📖] Grokking Deep Learning
- [📖] Make Your Own Neural Network
- [ ] Coursera: Neural Networks and Deep Learning
- [💻] Extreme Gradient Boosting with XGBoost
- [💻] Ensemble Methods in Python
- [ ] StatQuest: Machine Learning
- [ ] StatQuest: Fitting a line to data, aka least squares, aka linear regression. 0:09:21
- [ ] StatQuest: Linear Models Pt.1 — Linear Regression 0:27:26
- [ ] StatQuest: StatQuest: Linear Models Pt.2 — t-tests and ANOVA 0:11:37
- [ ] StatQuest: Odds and Log(Odds), Clearly Explained!!! 0:11:30
- [ ] StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 0:16:20
- [ ] StatQuest: Logistic Regression 0:08:47
- [ ] Logistic Regression Details Pt1: Coefficients 0:19:02
- [ ] Logistic Regression Details Pt 2: Maximum Likelihood 0:10:23
- [ ] Logistic Regression Details Pt 3: R-squared and p-value 0:15:25
- [ ] Saturated Models and Deviance 0:18:39
- [ ] Deviance Residuals 0:06:18
- [ ] Regularization Part 1: Ridge (L2) Regression 0:20:26
- [ ] Regularization Part 2: Lasso (L1) Regression 0:08:19
- [ ] Ridge vs Lasso Regression, Visualized!!! 0:09:05
- [ ] Regularization Part 3: Elastic Net Regression 0:05:19
- [ ] StatQuest: Principal Component Analysis (PCA), Step-by-Step 0:21:57
- [ ] StatQuest: PCA main ideas in only 5 minutes!!! 0:06:04
- [ ] StatQuest: PCA — Practical Tips 0:08:19
- [ ] StatQuest: PCA in Python 0:11:37
- [ ] StatQuest: Linear Discriminant Analysis (LDA) clearly explained. 0:15:12
- [ ] StatQuest: MDS and PCoA 0:08:18
- [ ] StatQuest: t-SNE, Clearly Explained 0:11:47
- [ ] StatQuest: Hierarchical Clustering 0:11:19
- [ ] StatQuest: K-means clustering 0:08:57
- [ ] StatQuest: K-nearest neighbors, Clearly Explained 0:05:30
- [ ] Naive Bayes, Clearly Explained!!! 0:15:12
- [ ] Gaussian Naive Bayes, Clearly Explained!!! 0:09:41
- [ ] StatQuest: Decision Trees 0:17:22
- [ ] StatQuest: Decision Trees, Part 2 — Feature Selection and Missing Data 0:05:16
- [ ] Regression Trees, Clearly Explained!!! 0:22:33
- [ ] How to Prune Regression Trees, Clearly Explained!!! 0:16:15
- [ ] StatQuest: Random Forests Part 1 — Building, Using and Evaluating 0:09:54
- [ ] StatQuest: Random Forests Part 2: Missing data and clustering 0:11:53
- [ ] The Chain Rule 0:18:23
- [ ] Gradient Descent, Step-by-Step 0:23:54
- [ ] Stochastic Gradient Descent, Clearly Explained!!! 0:10:53
- [ ] AdaBoost, Clearly Explained 0:20:54
- [⨊ ] Part 1: Regression Main Ideas 0:15:52
- [⨊ ] Part 2: Regression Details 0:26:45
- [⨊ ] Part 3: Classification 0:17:02
- [⨊ ] Part 4: Classification Details 0:36:59
- [⨊ ] Support Vector Machines, Clearly Explained!!! 0:20:32
- [ ] Support Vector Machines Part 2: The Polynomial Kernel 0:07:15
- [ ] Support Vector Machines Part 3: The Radial (RBF) Kernel 0:15:52
- [ ] XGBoost Part 1: Regression 0:25:46
- [ ] XGBoost Part 2: Classification 0:25:17
- [ ] XGBoost Part 3: Mathematical Details 0:27:24
- [ ] XGBoost Part 4: Crazy Cool Optimizations 0:24:27
- [ ] StatQuest: Fiitting a curve to data, aka lowess, aka loess 0:10:10
- [ ] Statistics Fundamentals: Population Parameters 0:14:31
- [ ] Principal Component Analysis (PCA) clearly explained (2015) 0:20:16
- [ ] Decision Trees in Python from Start to Finish 1:06:23
- [🅤]ꭏ Classification Models
- [📺 ] Neural Networks from Scratch in Python
- [ ] Neural Networks from Scratch — P.1 Intro and Neuron Code 0:16:59
- [ ] Neural Networks from Scratch — P.2 Coding a Layer 0:15:06
- [ ] Neural Networks from Scratch — P.3 The Dot Product 0:25:17
- [ ] Neural Networks from Scratch — P.4 Batches, Layers, and Objects 0:33:46
- [ ] Neural Networks from Scratch — P.5 Hidden Layer Activation Functions 0:40:05
- [📺 ] AppliedMachine Learning 2020–03 Supervised learning and model validation 1:12:00
- [📺 ] AppliedMachine Learning 2020–05 — Linear Models for Regression 1:06:54
- [📺 ] AppliedMachine Learning 2020–06 — Linear Models for Classification 1:07:50
- [📺 ] AppliedMachine Learning 2020–07 — Decision Trees and Random Forests 1:07:58
- [📺 ] AppliedMachine Learning 2020–08 — Gradient Boosting 1:02:12
- [📺 ] AppliedMachine Learning 2020–18 — Neural Networks 1:19:36
- [📺 ] AppliedMachine Learning 2020–12 — AutoML (plus some feature selection) 1:25:38
Be able to improve models
- [📰] Deep neural networks: preventing overfitting
- [📰] Normalizing your data (specifically, input and batch normalization)
- [📰] Batch Normalization
- [📰] Are Deep Neural Networks Dramatically Overfitted?
- [📰] In-layer normalization techniques for training very deep neural networks
- [📰] Label Smoothing Explained using Microsoft Excel
- [📰] Uncertainty Quantification Part 4: Leveraging Dropout in Neural Networks (CNNs)
- [📰] Simple Ways to Tackle Class Imbalance
-
[📺 ] AppliedMachine Learning 2020–10 — Calibration, Imbalanced
data
1:16:14
- [📺 ] Lecture 10: Troubleshooting Deep Neural Networks
Be familiar with fundamental Machine Learning concepts
- [📰] Connections: Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks
- [📰] MLE and MAP — in layman’s terms
- [ ] An overview of gradient descent optimization algorithms
- [📰] Optimization for Deep Learning Highlights in 2017
- [📰] Gradient descent
- [📰] Setting the learning rate of your neural network
- [📰] Cross-entropy for classification
- [📰] Dismantling Neural Networks to Understand the Inner Workings with Math and Pytorch
- [💻] AI Fundamentals
- [💻] Foundations of Predictive Analytics in Python (Part 1)
- [💻] Foundations of Predictive Analytics in Python (Part 2)
- [ ] Elements of AI
- [ ] edX: Principles of Machine Learning
[ ] edX: Data Science Essentials
[ 📺] Fast.ai: Deep Learning for Coder (2020)
[ 📺] Lesson 0
[📺 ] Lesson 1
[📺 ] Lesson 2
[📺 ] Lesson 3
[📺 ] Lesson 4
[ 📺] Lesson 5
[ 📺] Lesson 6
[ 📺] Lesson 7
[📺 ] Lesson 8
- [📺 ] Deep Double Descent
Implement models in scikit-learn
- [📰] Stacking made easy with Sklearn
- [📰] Curve Fitting With Python
- [📰] A Guide to Calibration Plots in Python
- [ ] Calmcode: human-learn
- [💻] Supervised Learning with scikit-learn
- [💻] Machine Learning with Tree-Based Models in Python
- [💻] Introduction to Linear Modeling in Python
- [💻] Linear Classifiers in Python
- [💻] Generalized Linear Models in Python
- [ ] Notebook: scikit-learn tips
- [ ] Pluralsight: Building Machine Learning Models in Python with scikit-learn
- [ ] Video: human learn
-
[📺 ] dabl: Automatic Machine Learning with a Human in the
Loop
00:25:43
- [📺 ] Multilabel and Multioutput Classification -Machine Learning with TensorFlow & scikit-learn on Python
- [📺 ] DABL: Automatic machine learning with a human in the loop- AI Latim American SumMIT Day 1
Be able to implement models in Tensorflow and Keras
- [ ] Coursera: Introduction to Tensorflow
- [ ] Coursera: Convolutional Neural Networks in TensorFlow
- [ ] Deeplizard: Keras — Python Deep Learning Neural Network API
- [📖] Deep Learning with Python (Page: 276)
- [💻] Deep Learning in Python
- [💻] Convolutional Neural Networks for Image Processing
- [💻] Introduction to TensorFlow in Python
- [💻] Introduction to Deep Learning with Keras
- [💻] Advanced Deep Learning with Keras
- [G] Machine Learning Crash Course
- [ ] Pluralsight: Deep Learning with Keras
- [🅤]ꭏ Intro to TensorFlow for Deep Learning
Be able to implement models in PyTorch
- [📰] Keeping Up with PyTorch Lightning and Hydra
- [📰] The One PyTorch Trick Which You Should Know
- [📰] How does automatic differentiation really work?
- [📰] 7 Tips To Maximize PyTorch Performance
- [📰] An introduction to PyTorch Lightning with comparisons to PyTorch
- [📰] Converting From Keras To PyTorch Lightning
- [📰] From PyTorch to PyTorch Lightning — A gentle introduction
- [📰] Introducing PyTorch Lightning Sharded: Train SOTA Models, With Half The Memory
- [📰] Sharded: A New Technique To Double The Size Of PyTorch Models
- [📰] Understanding Bidirectional RNN in PyTorch
- [📰] A developer-friendly guide to mixed precision training with PyTorch
- [📰] A developer-friendly guide to model pruning in PyTorch
- [📰] A developer-friendly guide to model quantization with PyTorch
- [📰] Tricks for training PyTorch models to convergence more quickly
- [📰] PyTorch Lightning Bolts — From Linear, Logistic Regression on TPUs to pre-trained GANs
- [📰] Scaling Logistic Regression Via Multi-GPU/TPU Training
- [📰] Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups
- [📰] PyTorch Lightning 0.9 — synced BatchNorm, DataModules and final API!
- [📰] PyTorch Lightning: Metrics
- [📰] PyTorch Multi-GPU Metrics Library and More in PyTorch Lightning 0.8.1
- [📰] Distributed model training in PyTorch using DistributedDataParallel
- [📰] Distributed model training in PyTorch using DistributedDataParallel
- [📰] EINSUM IS ALL YOU NEED — EINSTEIN SUMMATION IN DEEP LEARNING
- [📰] Faster Deep Learning Training with PyTorch — a 2021 Guide
- [📰] Fit More and Train Faster With ZeRO via DeepSpeed and FairScale
- [📰] PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA
- [📰] But what are PyTorch DataLoaders really?
- [📰] Using PyTorch + NumPy? You’re making a mistake.
- [📰] How Wadhwani AI Uses PyTorch To Empower Cotton Farmers
- [📰] Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health
- [📰] How to Build a Streaming DataLoader with PyTorch
- [📰] Transform your ML-model to Pytorch with Hummingbird
- [📰] PyTorch Loss Functions: The Ultimate Guide
- [📰] Pad pack sequences for Pytorch batch processing with DataLoader
- [📰] Model Parallelism
- [ ] Notebook: Tensor Arithmetic
- [ ] Notebook: Autograd
- [ ] Notebook: Optimization
- [ ] Notebook: Network modules
- [ ] Notebook: Datasets and Dataloaders
- [ ] Documentation: Pytorch Lightning
- [💻] Introduction to Deep Learning with PyTorch
- [ ] Deeplizard: Neural Network Programming — Deep Learning with PyTorch
- [🅤]ꭏ Intro to Deep Learning with PyTorch
- [📺 ] PyTorch Lightning 101
- [ ] Training a classification model on MNIST with PyTorch
- [ ] From PyTorch to PyTorch Lightning
- [ ] Lightning Data Modules
- [ ] PyTorch Dropout, Batch size and interactive debugging
- [ ] Episode 4: Implementing a PyTorch Trainer: PyTorch Lightning Trainer and callbacks under-the-hood
- [📺 ] SimCLR with PyTorch Lightning
-
[📺 ]
PyTorch Performance Tuning Guide
26:41:00
- [📺 ] Skin Cancer Detection with PyTorch
-
[ ]
[PART 1] Skin Cancer Detection with PyTorch
0:10:21
-
[ ]
[PART 2] Skin Cancer Detection with PyTorch
0:21:57
-
[ ]
[PART 3] Skin Cancer Detection with PyTorch
0:22:24
- [📺 ] Learn with Lightning
-
[📺 ]
PyTorch Tutorial — RNN & LSTM & GRU — Recurren
Neural Nets
00:15:51
- [📺 ] Pytorch Zero to All
-
[ ]
PyTorch Lecture 01: Overview
0:10:18
-
[ ]
PyTorch Lecture 02: Linear Model
0:12:52
-
[ ]
PyTorch Lecture 03: Gradient Descent
0:08:24
-
[ ]
PyTorch Lecture 04: Back-propagation and Autograd
0:15:25
-
[ ]
PyTorch Lecture 05: Linear Regression in the PyTorch way
0:11:50
-
[ ]
PyTorch Lecture 06: Logistic Regression
0:10:41
-
[ ]
PyTorch Lecture 07: Wide and Deep
0:10:37
-
[ ]
PyTorch Lecture 08: PyTorch DataLoader
0:06:41
-
[ ]
PyTorch Lecture 09: Softmax Classifier
0:18:47
-
[ ]
PyTorch Lecture 10: Basic CNN
0:15:52
-
[ ]
PyTorch Lecture 11: Advanced CNN
0:12:58
-
[ ]
PyTorch Lecture 12: RNN1 — Basics
0:28:47
-
[ ]
PyTorch Lecture 13: RNN 2 — Classification
0:17:22
- [ ] PyTorch Developer Day 2020 | Full Livestream
- [📺 ] Lightning Chat: How a Grandmaster Won a Kaggle Competition Using Pytorch Lightning
- [📺 ] Production Inference Deployment with PyTorch
- [📺 ] What is Automatic Differentiation?
- [📺 ] JAX: accelerated machine learning research via composable function transformations in Python
Be able to implement models using cloud services
- [ ] AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription
- [ ] AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker
- [ ] AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications
- [ ] AWS: Hands-on Rekognition: Automated Video Editing
- [ ] AWS: Introduction to Amazon Comprehend
- [ ] AWS: Introduction to Amazon Comprehend Medical
- [ ] AWS: Introduction to Amazon Elastic Inference
- [ ] AWS: Introduction to Amazon Forecast
- [ ] AWS: Introduction to Amazon Lex
- [ ] AWS: Introduction to Amazon Personalize
- [ ] AWS: Introduction to Amazon Polly
- [ ] AWS: Introduction to Amazon SageMaker Ground Truth
- [ ] AWS: Introduction to Amazon SageMaker Neo
- [ ] AWS: Introduction to Amazon Transcribe
- [ ] AWS: Introduction to Amazon Translate
- [ ] AWS: Introduction to AWS Marketplace — Machine Learning Category
- [ ] AWS: Machine Learning Exam Basics
- [ ] AWS: Neural Machine Translation with Sockeye
- [ ] AWS: Process Model: CRISP-DM on the AWS Stack
- [ ] AWS: Satellite Image Classification in SageMaker
- [💻] Introduction to AWS Boto in Python
- [ ] edX: Amazon SageMaker: Simplifying Machine Learning Application Development


Contains a small graphical application showing the utility of nearest neighbor over two dimensional data.


PICTURES: Plaxedes Wenyika with new boyfriend Jimmy Mhlanga
Songbird Plaxedes Wenyika has moved on from her broken marriage and has a new hottie on her arm to show for it!
The singer who had taken a hiatus from the music industry started hogging the headlines when her divorce battle with businessman Mike Joka was made public last year.
Plaxedes filed for divorce from the entrepreneur sighting irreconcilable differences and the matter went public when she dragged Joka to court demanding a hefty maintenance cheque.
However that is all water under the bridge now.
The singer posted a steaming photo of herself and her new shirtless lover Jimmy T Mhlanga proudly declaring him as her man crush Monday.
harare news


Fe2+/Fe3+ ratio
The formula is calculated based on the amount of cations and charge balance.
CalculateThis application converts weight percents to formula units for common rock forming minerals. We have two calculators that use different methods.
The formula is calculated based on the amount of cations and charge balance.
Calculate


--- layout: post title: Kinds of Truth and the Art of Biblical Narrative date: 2011-11-28 tags: - Exegesis - Narrative - Old Testament - Scripture meta: pvc_views: "22842" views: "136" dsq_thread_id: "753187060" author: jcuenod permalink: /content/kinds-of-truth-and-art-biblical-narrative/ excerpt: "Hebrew authors, it seems, have a propensity towards the prodigious use of direct speech but rarely reported speech and it seems clear that they were willing to summarise conversations into little speech bubble type reports which they recorded in direct speech.\r \r Read more and I'll try to explain a bit more about the license I think we often forget Hebrew authors are willing to take in their writing." hidden: false featured: false ---
I like logic because it makes sense. It has to. A is not not A. In other words if you tell me "A is not A", you're lying. The problem is, deceit actually can make sense but still be wrong. "A is B" could be true or false depending on what A and B are. But I'll stop boring you with the alphabet an get into the complicated stuff.
Story #1: And then Dave said something fabulous, "Genesis sounds like a fable to me."
Story #2: And then Dave said, "Genesis is merely mimetic myth"
Story #3: And Dave was an ocean of ideas saying, "Genesis is a metaphor".
Now, Dave has never said any of those things to me exactly. However, I do believe that he believes them and I don't think it would deceitful to put those words into his mouth. My thinking at this point is that we need to keep this in mind as we read Hebrew narrative (because obviously that's what I'm blogging about, not logical stuff as the introduction may have implied). While in our context it seems dodgy to put words into someone's mouth in a direct speech kind of way, we would certainly be willing to do it in reported speech: "Dave told me that didn't believe Genesis was literal" could summarise an enormous conversation (Paul probably didn't do a 2 minute sermon in Acts 17). Hebrew authors, it seems, have a propensity towards the prodigious use of direct speech but rarely reported speech and it seems clear that they were willing to summarise conversations into little speech bubble type reports which they recorded in direct speech.
In my stories I made Dave say stuff according to the rest of my story "fabulous" and "fable", "merely mimetic myth" and the metaphor thing. I used what was true and wrote it into my story and I don't think I was being deceitful.
The example that came to mind (beacuse I've been working on Jesus & the Gospels for the last week) was the centurion's confession on seeing Jesus die. This is my conclusion:
When Luke has the centurion saying, "surely this was a Righteous man" it is for a reason. When Mark has the centurion saying, "surely this was the Son of God" it is for a reason.
Neither are deceitful and neither are wrong. Both authors are using what was said to do something artistic - something evangelicals know nothing about.
Update [Sept 3rd 2012]: I recently read this article on patheos which talks about Thucydides writing about having to make up speech that fits the flow of history as he perceives it. It's an interesting read down a similar sort of line.


Black Judge:
You have not even taken one step forward on the path of penance. As I said before, this is not the way to fame or fortune. Nobody, including us black judges, can force you to follow this path. If you are having doubts, leave now and never come back.


Sphero
Start
Stop
Start calibration
Stop calibration
Speed
Colours
Sensitivity
Join distance
Process every
lines


Simulating and estimating the mixture model
require(rblm)
require(knitr)
require(kableExtra)
options(knitr.table.format = "html")
The interacted model has the following equations:
\[ Pr[ Y_{it} \leq y | k,\ell ] = \Phi( \mu_{k\ell}, \sigma_{k\ell}) \]
set.seed(324313)
model = m2.mixt.new(nk=3,nf=5,fixb=T,stationary = T)
# simple complementatiry
model$A1 = seq(0.1,1,l=model$nf) %o% seq(0.1,1,l=model$nk)
model$A2 = model$A1
# small variance
model$S1[] = 0.2
model$S2[] = 0.2
# simple sorting
model$pk0[1,,] = exp(3*model$A1)/rowSums(exp(3*model$A1))
# setting the number of movers and stayers
model$NNs = array(300000/model$nf,model$nf)
model$NNm = model$nf*toeplitz(ceiling(seq(100,10,l=model$nf)))
# creating a simulated data set
ad = m2.mixt.simulate.sim(model,fsize = 40)
# plot firm size distribution
ggplot(ad$sdata[,.N,f1],aes(x=N)) + geom_histogram(binwidth=1) + theme_bw()
We start by extracting the measures that will be used to cluster
# constructing the measures, we use empirical CDF at each firm
ms = grouping.getMeasures(ad, # the data
"ecdf", # the type of moments to construct at the firm level
Nw=10, # number of point of support to use for the method
y_var = "y1") # variable to use to construct moments
## INFO [2018-12-19 23:35:31] processing 7500 firms
## INFO [2018-12-19 23:35:31] computing measures...
## INFO [2018-12-19 23:35:32] computing weights...
# classify into groups
grps = grouping.classify.once(ms, # input the measures to use
k = 5, # number of groups
nstart = 1000, # number of starting values for kmean
iter.max = 200, # max number of iterations for each kmean
step=250) # how often to show output
## INFO [2018-12-19 23:35:32] clustering T=41.004107, Nw=10 , measure=ecdf
## INFO [2018-12-19 23:35:32] running weigthed kmeans step=250 total=1000
## INFO [2018-12-19 23:35:32] nobs=7500 nmeasures=10
## INFO [2018-12-19 23:35:33] [25%] tot=844.071415 best=844.071415 <<<<
## INFO [2018-12-19 23:35:34] [50%] tot=844.071415 best=844.071415
## INFO [2018-12-19 23:35:35] [75%] tot=844.071415 best=844.071415
## INFO [2018-12-19 23:35:36] [100%] tot=844.071415 best=844.071415
## INFO [2018-12-19 23:35:36] k=5 WSS=844.071415 nstart=1000 nfrims=7500
# finally we append the results to adata
ad = grouping.append(ad,grps$best_cluster,drop=T)
# we can also check the classification
ggplot(ad$sdata[,.N,list(j1,j1true)],aes(x=j1true,y=j1,size=N)) + geom_point() + theme_bw() +
scale_x_continuous("true type") + scale_y_continuous("estimated group")
In the previous command we tell rblm that we want to use the firm specific empirical measure “ecdf” with 20 points of supports and that the dependent variable is “y1”. The firm identifier should be “f1”.
# we set control parameters for the EM
ctrl = em.control( nplot=10000, # how often to plot (either wages, or model versus model0)
ncat =2000, # how often to show step increases (large to keep output small)
model0 = model, # a model to compare estimates too (this is for testing)
fixb=TRUE, # impose fixed interactions over time
tol=1e-7, # tolerance to stop EM
est_rep=2, # number of starting values to use (we usually use 50)
est_nbest=1, # how many best liklihood to choose the best connected model from (we usually use 10)
sdata_subsample=0.1) # whether to sub-sample the stayers in estimation
res = m2.mixt.estimate.all(sim=ad, # the data
nk=model$nk, # number of worker types (we use here the same as in simulation)
ctrl=ctrl) # parameters to control the EM
## INFO [2018-12-19 23:35:49] [233][para0][final] lik=-1645.3951 dlik=9.7191e-08 liks=-1.6431e+03 likm=0.0000e+00
## INFO [2018-12-19 23:35:49] starting repetitions with 1 nodes
## INFO [2018-12-19 23:35:50] [ 31][paraf (1/2)][final] lik=-33198.5347 dlik=8.9609e-08 liks=-3.3194e+04 likm=0.0000e+00
## INFO [2018-12-19 23:36:09] [358][para1 (1/2)][final] lik=-1644.9184 dlik=9.2612e-08 liks=-1.6426e+03 likm=0.0000e+00
## INFO [2018-12-19 23:37:38] [2000][move1 (1/2)] lik=-208.8048 dlik=2.1403e-07 liks=-2.0761e+02 likm=0.0000e+00
## INFO [2018-12-19 23:37:38] [2000][move1 (1/2)][final] lik=-208.8048 dlik=2.1403e-07 liks=-2.0761e+02 likm=0.0000e+00
## INFO [2018-12-19 23:37:38] done with reptitions 1/2
## INFO [2018-12-19 23:37:39] [ 13][paraf (2/2)][final] lik=-80230.6618 dlik=9.7492e-08 liks=-8.0227e+04 likm=0.0000e+00
## INFO [2018-12-19 23:37:46] [186][para1 (2/2)][final] lik=-1799.6715 dlik=9.6416e-08 liks=-1.7975e+03 likm=0.0000e+00
## INFO [2018-12-19 23:39:14] [1980][move1 (2/2)][final] lik=-523.4160 dlik=9.9798e-08 liks=-5.2195e+02 likm=0.0000e+00
## INFO [2018-12-19 23:39:14] done with reptitions 2/2
## INFO [2018-12-19 23:39:14] drawing 0.100000 from the stayers
## INFO [2018-12-19 23:39:42] [1999][stayers] lik=1.5242e+03 inc=8.4762e-09 max-pchg=1.1677e-05 mean-pchg=1.5569e-06
## cor_kl cov_kl var_k var_l rsq
## 1 0.2416 0.178 0.229 0.593 0.6711
m2.mixt.wplot(res$model$A1)
m2.mixt.wplot(model$A1)
m2.mixt.pplot(res$model$pk0[1,,])
m2.mixt.pplot(model$pk0[1,,])


Saint Kristina:
Please, you must help me! You were sent by Priest Bandellos, weren't you? The Dark Elf who kidnapped me cast an evil spell and I can't move at all! I beg you. Please set me free!


The Angular CLI makes it easy to create an application that already works, right out of the box. It
already follows our best practices!
Generate components, routes, services and pipes with a simple command. The CLI will also create
simple test shells for all of these.
Easily test your app locally while developing.
Make your code really shine. Run your unittests or your end-to-end tests with the breeze of a
command. Execute the official Angular linter and run clang format.
Learn Angular
Powered by Google ©2010-2016. Code licensed under an MIT-style License. Documentation licensed under CC BY 4.0.


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Are you interested in being a GLG Social Impact Fellow?
The Fellows are young leaders of innovative nonprofits and social enterprises selected to receive pro bono access to the GLG Councils for a one year period. You can learn more at: www.GLGSocialImpact.com
Put your question inputs here! (yes/no, multiple choice, etc)
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- Have you ever ridden a moped?
- Do you believe in life after love?
- Has there ever been a time where you thought, "Jeezus, why don't they reboot Alf?"
- Is your favorite movie Die Hard?


Help Menu
The Help menu has only a few entries:
Help
Invokes a browser and brings up contextual help on fontforge web site.
If you define the environment variable BROWSER so that it contains the name of the browser you like then FontForge will use that, otherwise it will try to guess something appropriate by looking at your PATH. On CygWin systems browsers that work in the windows world (as opposed to the cygwin sub-system) must be specified by a full path spec.
FontForge will search for help in either /usr/share/doc/fontforge/ or on http://fontforge.sf.net/.
Overview
Invokes a browser to bring up a page describing basic concepts you should understand before working on fonts.
Index
Invokes a browser and brings up the index of help topicson the web page.
About
Will display the splash screen again.
License
Invokes a browser to display FontForge's license.


{% include 'taxbrain/includes/params/inputs/param.html' with param=params.EITC_rt%}
{% include 'taxbrain/includes/params/inputs/param.html' with param=params.EITC_prt %}
{% include 'taxbrain/includes/params/inputs/param.html' with param=params.EITC_ps %}
{% include 'taxbrain/includes/params/inputs/param.html' with param=params.EITC_c %}
{% include 'taxbrain/includes/params/inputs/param.html' with param=params.EITC_MinEligAge %}
{% include 'taxbrain/includes/params/inputs/param.html' with param=params.EITC_MaxEligAge %}
Additional child tax credit
{% include 'taxbrain/includes/params/inputs/param.html' with param=params.ACTC_rt %}
{% include 'taxbrain/includes/params/inputs/param.html' with param=params.ACTC_ChildNum %}
{% endblock %}
{% block next_section_shortname %}growth{% endblock %}


REPLAY(1) User Commands REPLAY(1) NAME scriptreplay - play back typescripts, using timing information SYNOPSIS scriptreplay [options] [-t] timingfile [typescript [divisor]] DESCRIPTION This program replays a typescript, using timing information to ensure that output happens at the same speed as it originally appeared when the script was recorded. The replay simply displays the information again; the programs that were run when the typescript was being recorded are not run again. Since the same information is simply being displayed, scriptreplay is only guaran‐ teed to work properly if run on the same type of terminal the typescript was recorded on. Otherwise, any escape characters in the typescript may be interpreted differently by the terminal to which scriptreplay is send‐ ing its output. The timing information is what script(1) outputs to standard error if it is run with the -t parameter. By default, the typescript to display is assumed to be named "type‐ script", but other filenames may be specified, as the second parameter or with option -s. If the third parameter is specified, it is used as a speed-up multiplier. For example, a speed-up of 2 makes scriptreplay go twice as fast, and a speed-up of 0.1 makes it go ten times slower than the original session. OPTIONS The first three options will overide old-style arguments. -t, --timing file File containing script timing output. -s, --typescript file File containing the script terminal output. -d, --divisor number Speed up the replay displaying this number of times. The argument is a floating point number. It's called divisor because it divides the timings by this factor. -V, --version Display version information and exit. -h, --help Display a help message and exit. EXAMPLE % script -t 2> timingfile Script started, file is typescript % ls <etc, etc> % exit Script done, file is typescript % scriptreplay timingfile SEE ALSO script(1) COPYRIGHT Copyright © 2008 James Youngman Copyright © 2008 Karel Zak This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Released under the GNU General Public License version 2 or later. AUTHOR The original scriptreplay program was written by Joey Hess<EMAIL_ADDRESS>The program was re-written in C by James Youngman<EMAIL_ADDRESS>and Karel Zak<EMAIL_ADDRESS>AVAILABILITY The scriptreplay command is part of the util-linux package and is avail‐ able from ftp://ftp.kernel.org/pub/linux/utils/util-linux/. 3rd Berkeley Distribution September 2001 REPLAY(1)


Home > Reference > Functions reference > Date functions > MonthNameJ
MonthNameJ Purpose Returns the name of the month of date in Japanese. Format MonthNameJ(date) Parameters date - any calendar date Data type returned text Originated in FileMaker Pro 6.0 or earlier Examples MonthNameJ(“6/6/2014”) returns Important To avoid errors when using dates, always use four-digit years. For more information about how FileMaker Pro handles two-digit dates, see Conversion of dates with two-digit years. Related topics Functions reference (category list) Functions reference (alphabetical list) About formulas About functions Defining calculation fields Using operators in formulas


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Scientific Name: Phascolarctos cinereus Average Length: 2-2.28 ft Average Lifespan: 13-8 years Habitat: Bushland
Koalas are herbovoires that climb trees to escape predators


{% extends 'wildlifecompliance/emails/base_email.html' %} {% block content %}
Your payment for your application has been received.
A copy of your invoice is attached to this email.
Application : {{ application.id }}
Link: {{url}}
Invoice: {{ invoice_url }}
{% endblock %}


The OG Google Pixel was left out of the November security update
Source: Android Central
What you need to know
- Google Pixel and Pixel XL left out of November 2019 security update list.
- Google originally slated October 2019 as the final security update.
- Phone and online support also ends.
The Android November 2019 update list is available and the Google Pixel and Google Pixel XL, launched in 2016, will not be getting the latest security patch. While Google has not officially terminated support for the original Google Pixel devices, a separate support page indicates that security updates will no longer be guaranteed after October 2019. That page essentially gives all Pixel devices a three-year deadline on guaranteed security updates as well as new versions of Android. The Google Pixel was no longer guaranteed Android version updates as of October 2018, though it did receive Android 10.
Google will no longer support the original Pixel and Pixel XL by telephone or online customer support channels. Like the security updates, phone and online support is only guaranteed for three years after the phone is released. Of note, because of the timing of the launch of Google's Preferred Care extended warranty program for its Pixel phones, Pixel 2 owners who opted for Preferred Care can get customer service support through April 2021.
Apple has recently launched iOS 13.2, its latest software version and security bundle, and it supports devices from the iPhone 6s, launched in September, 2015. Devices as old as the iPhone 5s from 2013 are still being supported by Apple software updates, though not the latest OS versions. Samsung offers quarterly security updates still for the Galaxy S7, and monthly updates are promised for the Galaxy S8 and newer devices. Those phones launched in Spring 2016 and 2017, respectively.
Newest Pixel
Google Pixel 4 XL
The newest Pixel hardware, software and security
The battery life was frustrating, and the gigabytes were low, but otherwise this is the Pixel to buy, and with the most recent security and OS for the next 3 years, it's an investment in your Pixel future.
We may earn a commission for purchases using our links. Learn more.


Date: 24 March Updated: 23 May 2016
What timebox and habits have I tried?
I have known about a technique similar to the pomodoro technique from a sports movement specialist. It is slightly diferent the way I have been practicing. It was suggested I work in one position for 50 minutes, then do something else for 10 minutes such as hanging out the washing or making a cup of tea. Then re-align the physical working position differently, so trying standing, kneeling, sitting cross-legged moving the laptop and the working environment. It's all about doing what kids do well - wriggle and move alot as being static at a desk is unnatural movement for long periods. It works on a mental scale as well, to create space. We are in an intense period of changing behaviours and creating new habits whilst overloading our brains. Despite some testing - headbanging on the desk hasn't worked as a good mental or physical re-algnment method yet!
What did I find interesting?
That the curriculum involves much more than thinking and understanding coding systems. How we work together and the environment fit with our persoanl skills and it all becomes a catalyst for those larger-scale projects we are heading towards. Using holistic thinking, noticing how we react to problems, and realising how we feel, realy does make a difference. It all affects the way we learn. It's all about collaborative coding as a cohort - it's been a good start so far.
What is working for me?
I am continually working on it...there is always a much better way. This is my repeat of this phase 0, I was moving around without interenet and without a dedicted work practice in place last time. Repeating this has enabled me to relax, and learn a at a slower pace, and actually enjoy it more. I am also trying to time-block and find larger slabs of time to try to get right into the work. Previously I my work has always fitted around the many family commitments, so often late at night and in the dead early hours of the morning. So we are trying to put i place different family routines to find some uniterrupted times for me to really get into the work.
What will I experiment further with?.
I always wondered what the term lizard brain mean't! We have a natural resistance not to stick our necks out. So avoid being afraid to do new stuff, and use any feedback and connections to go further.


Grace Millane murder: A trial that gripped a nation
by Doug FaulknerGuilty. The intake of breath, a sob from the dead woman's mother. A single word was all it took to bring to an end one of the most highly publicised murder cases in New Zealand's history.
Three weeks of evidence, hundreds of hours of painstaking police work and a year of grief for a family had built up to the moment 12 jurors agreed that a 27-year-old man had murdered Grace Millane.
What had started out as a missing person inquiry in December 2018 when a daughter, sister and friend failed to respond to 22nd birthday messages swiftly turned into a murder investigation.
Within days of her disappearance, police had identified a suspect, spoken to him and, unbeknownst to the killer, tracked his movements by trawling through CCTV evidence.
Before long police would find Ms Millane's body, which he had stuffed into a suitcase and buried in the mountainous Waitākere Ranges. There followed an outpouring of grief from a small nation unused to such crimes.
Prime Minister Jacinda Ardern issued an apology to Ms Millane's parents David and Gillian, saying "your daughter should have been safe here, she wasn't and I'm sorry for that".
Ms Millane, from Wickford, Essex, and the man who would go on to murder her made contact through a dating app and hit it off immediately.
She was in Auckland as part of a round-the-world trip, while he had been living there working in various sales jobs.
It was clear from the footage they enjoyed each other's company; they were close, they kissed. Ms Millane even messaged a friend back home to tell her how much she was connecting with him.
They returned to his hotel.
But after she left the lift, she was never seen alive again.
There he strangled her before taking pictures of her and watching pornography. He claimed she had died accidentally during consensual sex.
Despite his murder conviction, her killer still cannot be named. A court suppression order remains in place and is likely to do so beyond his sentencing on 21 February, in part because of the level of interest in the case.
Reporting on the trial has proved challenging; because the defendant could not be named, CCTV footage had to be blurred, and there were legal disputes over some pieces of evidence. Several witnesses also had their identities protected.
Because of the nature of the killer's defence, Ms Millane's parents have had to listen to claims about their daughter's sex life, with the details reported across New Zealand and around the world.
Graphic information, in particular regarding the night of her murder, was analysed as Mr and Mrs Millane watched in court.
At times, Mrs Millane would look at the floor or hold her head in her hands as the injuries inflicted on her daughter were described.
The prosecution accused the man of "eroticising" Ms Millane's death by taking intimate photographs of her body and looking up pornography while she lay dead in his room.

Experts, Tinder dates and ex-lovers were all brought to court to talk about the killer and his victim.
The defendant was portrayed as a serial dater; he even messaged and met up with a woman as Ms Millane's body lay in a suitcase.
It was the sexualising of the murder that brought the killer down.
The timeline of his Google searches and the naked pictures of Ms Millane were irreconcilable with his case, the prosecution said. Either Ms Millane was dead when they were taken, or he had searched for the Waitākere Ranges, where he buried her body, while she was still living - thus showing he planned to kill her.
The defence could only offer that they had been "random" drunk searches, and the Waitākere Ranges was perhaps somewhere the pair had planned to go for a day out.
The killer's right to use the "rough sex" defence, and some of the reporting of it, has angered Fiona Mackenzie, founder of the campaign group We Can't Consent To This.
Describing it as the "ultimate victim blaming", she said: "He gets to tell her story, he gets to tell the story of what she was like and how she asked for it.
"Families not only lose their loved one but these men [those who use such a defence] steal the public perception of them and destroy their reputation. It's appalling."


{% extends "admin_template.html" %} {% from "components/page-header.html" import page_header %} {% block service_page_title %} Uploads {% endblock %} {% block maincolumn_content %}
Upload a letter and GC Notify will print, pack and post it for you.


lightdm
sudo apt-get install lightdm lightdm-gtk-greeter
lightdm is a lightweight yet functional login manager. A loggin manager manages loggin attempts and prompts the user for credentials, lets the user select a session..
The below is totally unnecessary, but it is nice to having another option: I used to have a bad experience with a loggin manager suddenly refusing to work after an update and it left with a blank black screen. Although it was just a minor bug and I was easy to get slim to work again, there is a choice of not using any loggin manager.
 To work around it you need to do the following.
geany ~/.profile
We will add some lines into ~/.profile file in your home folder:
# startx after login
if [[ -z $DISPLAY ]] && [[ $(tty) = /dev/tty1 ]]; then
exec startx
fi
Now as soon as you have entered your login credential, the GUI will be launched.
Your operating system needs a way to notify you when an event happens (an email is received, your battery is low…).
sudo apt-get install xfce4-notifyd
Now, any program that needs desktop notification (Thunderbird, Transmission…) should have a tool for displaying their messages ( in a form of bubbles popping out on your desktop)
To configure how would the notification be like:
xfce4-notifyd-config
What gives you the driver is Alsa and what gives you multiple choice over output is PulseAudio. It is recommended to use both on your system.
sudo apt-get install alsa-base alsa-utils pulseaudio pavucontrol
You should get sound work immediately, if not, follow by entering this command:
alsactl init
In your terminal using the command Alsamixer to open the mixer and using SPACE to unmute all the channels.
alsamixer
Lacking windows fonts, some website and documents will not display nicely (Especially documents received from someone using Windows). To install Windows fonts:
sudo apt-get install ttf-mscorefonts-installer
These fonts are free by the way.
Printing screen/ screen capturing program: scrot
To use scrot, in a terminal window, type:
scrot -d 5 -c
It will take a screen shot after 5 seconds and display the countdown (-d for delaying and -c for displaying the countdown).
The captured image will be saved in your home folder.
This scrot command should be bound to the PRINT SCREEN key on your keyboard for convenience. I have configured that in the rc.xml file like this:
<keybind key="Print">
<action name="Execute">
<command>scrot -cd1</command>
</action>
</keybind>
To lock screen with light-locker:
Lock screen for me is must-have feature as I often worked at various places and my normal working section often takes too long to be able to always attend at my laptop.
I am choosing light-locker for its nature of lightweight and simple.
sudo apt-get install light-locker
Bind Windows + l these keys to the following command:
$ light-locker-command -l
in your rc.xml file. The entry will look like this:
<keybind key="W-l">
<action name="Execute">
<startupnotify>
<enabled>true</enabled>
<name>lock</name>
</startupnotify>
<command>light-locker-command -l</command>
</action>
</keybind>
Quickly launch a program with gmrun:
gmrun give you the ability to quickly launch a program or run a command by just using your keyboard. To make it is easier it will recommend you a list of commands that have already installed in your system based on what you have type:
To install:
Sudo apt-get install gmrun
In my my rc.xml file, the gmrun have been bound into “Windows key + R”. Press these combination keys and gmrun window should be ready.

Here is how the key binding entry look like in my rc.xml:
</keybind>
<keybind key="W-r">
<action name="Execute">
<startupnotify>
<enabled>true</enabled>
<name>gmrun</name>
</startupnotify>
<command>gmrun</command>
</action>
</keybind>


Costing Brexit:
the FT’s guide to the assumptions behind the forecasts
By Emily Cadman and Tom Pearson
Whilst drawing on essentially the same data, the variation between the estimates of what a Brexit vote would mean for the UK's economy is huge, nearly 6% of GDP.
In the medium term what will be the effect on Britain's GDP?


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Kangnam impresses Lee Sanghwa's parents on 'You Are My Destiny'
by sl278Lee Sanghwa's parents were impressed by their son-in-law Kangnam.
The newlywed couple was seen on the November 11th broadcast of 'You Are My Destiny' and they visited Lee Sanghwa's parents.
Kangnam made Lee Sanghwa's mother laugh by expressively complimenting her food. He also impressed them with the fact that he was a naturalized Korean citizen now. Kangnam further charmed them with cute and funny stories of Korean spelling mistakes he used to make while learning the language, which is part of the naturalization test.
Although Kangnam has submitted his application papers, he is preparing for the test that will make him a full citizen.


/**
* Dear 🐷🐷,
* I write some code to describe our stories.

// So I keep waiting and I have confidence that you will.
boolean isHesitate = true;
while (isHesitate) {
i.waitFor(u);
// I think it is an important decision
// and you should think it over.
isHesitate = u.thinkOver();
}
// After a romantic wedding, we will live happily ever after.
i.marry(u);
i.liveHappilyWith(u);
* Dear 🐷🐷,
* I write some code to describe our stories.

// So I keep waiting and I have confidence that you will.
boolean isHesitate = true;
while (isHesitate) {
i.waitFor(u);
// I think it is an important decision
// and you should think it over.
isHesitate = u.thinkOver();
}
// After a romantic wedding, we will live happily ever after.
i.marry(u);
i.liveHappilyWith(u);
Hs, I have fallen in love with you for
Love u forever and ever.
- Muyy


Every year, Global Youth Network for Peace recruits valuable Members, Volunteers , Experts and Workers who help its administrative team in serving humanity.
Use email, Facebook, Twitter, whatever you can to share the good news about our work.
No single individual or government can solve the challenges of peacebuilding especially with the sophisticated equipment and huge financial budget of terrorists all over the world. And with the growing rate of youth unemployment, human rights abuses among others, backing on the 17th goal of the global sustainable development goals, your partnership with us or support can go a long way.
Thank you for visiting this page, we are currently not hiring new staff, continue visiting the page for subsequent job advertisements that we will place here.
We are working to build a large-scale constituency of 2,000,000 peace advocates by 2030 around the world. We work with individuals, institutions and groups working for peace and development by taking actions to "Be the Peace" we are advocating for. To better serve the field, we also work with special focus to help strengthen and grow coalitions and alliances among like-minded groups and organizations.
Membership in this organization is also open to all those who care about, have an interest in, or wish to make contributions to help Global Youth Network for Peace foster a culture of peace among, between and within nations of the world.
Individual Membership at Global Youth Network for Peace is attained when a single individual secure a membership form at our membership directorate at the cost of $ 5 United States Dollars, complete and submit to the directorate. Our membership directorate does all validations and approve with an official letter confirming your membership status with us. Your data is then entered into our membership database.
At a membership orientation/inaugural ceremony, all persons that hold Individual Membership Category with us are presented with the following benefits:
Executive Membership at Global Youth Network for Peace is attained when a single individual member/professional or expert in a certain field in the society secure a membership form at our membership directorate at the cost of $ 10 United States Dollars, complete and submit to the directorate. Our membership directorate does all validations and approve with an official letter confirming your membership status with us. Your data is then entered into our membership database.
At a membership orientation/inaugural ceremony, all persons with Executive Membership Category with us are presented with the following benefits:
Corporate Membership into the Global Youth for Peace is highly restricted to the following group of individuals:
Family, professional body, local associations, community alliances, networks etc.
School, Faith-Based institution/religious bodies/ Community Based Organizations.
Government and corporate agencies, Civil Society Organizations etc.
Its membership is acquired when an association, alliance or network of people secure a membership form at our membership directorate at the cost of $ 20 United States Dollars, complete and submit to the directorate. Our membership directorate does all validations and approve with an official letter confirming your membership status with us. The association’s data is then entered into our membership database.
Membership benefits into Global Youth Network for Peace include all those above for executive membership and with the following additional status:
Every year, Global Youth Network for Peace recruits valuable volunteers who help its administrative team in serving humanity. Continue visiting this page for the next volunteer recruitment announcement.
Whether you are a university scholar, researcher, diplomat, practitioner, or policymaker; you have expertise in war, ethnic conflict, ethnic studies, sectarian conflict, racial conflict, race and ethnicity, tribal conflict, religious conflict, religious studies, theology, interfaith dialogue, pluralism, peace and conflict studies, conflict analysis and resolution, mediation, dialogue, conflict prevention, conflict management, conflict transformation, alternative dispute resolution, restorative justice, and peacebuilding, or related areas, disciplines and sectors; and you would like to offer your expert service, knowledge and skills to journalists, policymakers, governments, international institutions, regional, national and local organizations, individuals and groups. We invite you to secure and complete a form from our research and policy evaluation department and get listed on our expert portal as an expert in conflict resolution. This opportunity is also extended to a traditional ruler, leader of indigenous people, or faith leader, and your work in your community or congregation involves civil conflict resolution, mediation and dialogue, you are also invited to join our expert portal.
After submitting the completed Expert Service Provider form, your profile will be reviewed, and the information you provided will be verified. If found accurate and eligible for inclusion in the Expert Service Provider portal, your expert profile will be approved the same day and added on our website. If additional information is needed to determine your eligibility, we will contact you. If we could not determine your qualification to be an Expert in Conflict Resolution, your application will be rejected.


--- layout: post title: no title date: '2016-02-10T08:51:10+01:00' categories: - Storyboards - Sketchbooks - Horses picture_url: https://film-Storyboards.tumblr.com/post/139052435061 ---


Package com.opengamma.strata.product.swap
Entity objects describing a swap.
A swap takes place between two counterparties who agree to exchange streams of payments. In the simplest vanilla interest rate swap, there are two legs, one with a fixed rate and the other a floating rate. Many other more complex swaps can also be represented.
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Interface Summary Interface Description NotionalPaymentPeriod A period over which interest is accrued with a single payment calculated using a notional.RateCalculation The accrual calculation part of an interest rate swap leg.ScheduledSwapLeg A swap leg that defines dates using a schedule.SwapIndex A swap index.SwapLeg A single leg of a swap.SwapPaymentEvent A payment event, where a single payment is made between two counterparties.SwapPaymentPeriod A period over which interest is accrued with a single payment. -
Class Summary Class Description FixedRateCalculation Defines the calculation of a fixed rate swap leg.FixedRateCalculation.Builder The bean-builder forFixedRateCalculation
.FixedRateCalculation.Meta The meta-bean forFixedRateCalculation
.FixedRateStubCalculation Defines the rate applicable in the initial or final stub of a fixed swap leg.FixedRateStubCalculation.Meta The meta-bean forFixedRateStubCalculation
.FutureValueNotional A future value notional amount for a fixed swap leg.FutureValueNotional.Builder The bean-builder forFutureValueNotional
.FutureValueNotional.Meta The meta-bean forFutureValueNotional
.FxReset An FX rate conversion for the notional amount of a swap leg.FxReset.Meta The meta-bean forFxReset
.FxResetCalculation Defines the calculation of an FX rate conversion for the notional amount of a swap leg.FxResetCalculation.Builder The bean-builder forFxResetCalculation
.FxResetCalculation.Meta The meta-bean forFxResetCalculation
.FxResetNotionalExchange An exchange of notionals between two counterparties where FX reset applies.FxResetNotionalExchange.Meta The meta-bean forFxResetNotionalExchange
.IborRateCalculation Defines the calculation of a floating rate swap leg based on an Ibor index.IborRateCalculation.Builder The bean-builder forIborRateCalculation
.IborRateCalculation.Meta The meta-bean forIborRateCalculation
.IborRateStubCalculation Defines the rates applicable in the initial or final stub of an Ibor swap leg.IborRateStubCalculation.Builder The bean-builder forIborRateStubCalculation
.IborRateStubCalculation.Meta The meta-bean forIborRateStubCalculation
.ImmutableSwapIndex A swap index implementation based on an immutable set of rules.ImmutableSwapIndex.Builder The bean-builder forImmutableSwapIndex
.ImmutableSwapIndex.Meta The meta-bean forImmutableSwapIndex
.InflationRateCalculation Defines the calculation of a swap leg of a zero-coupon inflation coupon based on a price index.InflationRateCalculation.Builder The bean-builder forInflationRateCalculation
.InflationRateCalculation.Meta The meta-bean forInflationRateCalculation
.KnownAmountNotionalSwapPaymentPeriod A period within a swap that results in a known amount.KnownAmountNotionalSwapPaymentPeriod.Builder The bean-builder forKnownAmountNotionalSwapPaymentPeriod
.KnownAmountNotionalSwapPaymentPeriod.Meta The meta-bean forKnownAmountNotionalSwapPaymentPeriod
.KnownAmountSwapLeg A fixed swap leg defined in terms of known amounts.KnownAmountSwapLeg.Builder The bean-builder forKnownAmountSwapLeg
.KnownAmountSwapLeg.Meta The meta-bean forKnownAmountSwapLeg
.KnownAmountSwapPaymentPeriod A period within a swap that results in a known amount.KnownAmountSwapPaymentPeriod.Builder The bean-builder forKnownAmountSwapPaymentPeriod
.KnownAmountSwapPaymentPeriod.Meta The meta-bean forKnownAmountSwapPaymentPeriod
.NotionalExchange An exchange of notionals between two counterparties.NotionalExchange.Meta The meta-bean forNotionalExchange
.NotionalSchedule Defines the schedule of notional amounts.NotionalSchedule.Builder The bean-builder forNotionalSchedule
.NotionalSchedule.Meta The meta-bean forNotionalSchedule
.OvernightRateCalculation Defines the calculation of a floating rate swap leg based on an Overnight index.OvernightRateCalculation.Builder The bean-builder forOvernightRateCalculation
.OvernightRateCalculation.Meta The meta-bean forOvernightRateCalculation
.PaymentSchedule Defines the schedule of payment dates relative to the accrual periods.PaymentSchedule.Builder The bean-builder forPaymentSchedule
.PaymentSchedule.Meta The meta-bean forPaymentSchedule
.RateAccrualPeriod A period over which a fixed or floating rate is accrued.RateAccrualPeriod.Builder The bean-builder forRateAccrualPeriod
.RateAccrualPeriod.Meta The meta-bean forRateAccrualPeriod
.RateCalculationSwapLeg A rate swap leg defined using a parameterized schedule and calculation.RateCalculationSwapLeg.Builder The bean-builder forRateCalculationSwapLeg
.RateCalculationSwapLeg.Meta The meta-bean forRateCalculationSwapLeg
.RatePaymentPeriod A period over which a rate of interest is paid.RatePaymentPeriod.Builder The bean-builder forRatePaymentPeriod
.RatePaymentPeriod.Meta The meta-bean forRatePaymentPeriod
.RatePeriodSwapLeg A rate swap leg defined using payment and accrual periods.RatePeriodSwapLeg.Builder The bean-builder forRatePeriodSwapLeg
.RatePeriodSwapLeg.Meta The meta-bean forRatePeriodSwapLeg
.ResetSchedule Defines the schedule of fixing dates relative to the accrual periods.ResetSchedule.Builder The bean-builder forResetSchedule
.ResetSchedule.Meta The meta-bean forResetSchedule
.ResolvedSwap A rate swap, resolved for pricing.ResolvedSwap.Builder The bean-builder forResolvedSwap
.ResolvedSwap.Meta The meta-bean forResolvedSwap
.ResolvedSwapLeg A resolved swap leg, with dates calculated ready for pricing.ResolvedSwapLeg.Builder The bean-builder forResolvedSwapLeg
.ResolvedSwapLeg.Meta The meta-bean forResolvedSwapLeg
.ResolvedSwapTrade A trade in a rate swap, resolved for pricing.ResolvedSwapTrade.Builder The bean-builder forResolvedSwapTrade
.ResolvedSwapTrade.Meta The meta-bean forResolvedSwapTrade
.Swap A rate swap.Swap.Builder The bean-builder forSwap
.Swap.Meta The meta-bean forSwap
.SwapIndices Constants and implementations for standard swap indices.SwapTrade A trade in a rate swap.SwapTrade.Builder The bean-builder forSwapTrade
.SwapTrade.Meta The meta-bean forSwapTrade
. -
Enum Summary Enum Description CompoundingMethod A convention defining how to compound interest.FixedAccrualMethod The method of accruing interest on a notional amount using a fixed rate.FixingRelativeTo The base date that each rate fixing is made relative to.FxResetFixingRelativeTo The base date that each FX reset fixing is made relative to.IborRateResetMethod A convention defining how to process a floating rate reset schedule.NegativeRateMethod A convention defining how to handle a negative interest rate.OvernightAccrualMethod The method of accruing interest based on an Overnight index.PaymentRelativeTo The base date that each payment is made relative to.PriceIndexCalculationMethod Reference price index calculation method.SwapLegType The type of a swap leg.


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