Taking the Coursera Deep Learning Specialization, Sequence Models course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details.
Table of Contents
Recurrent Neural Networks
- Learn about recurrent neural networks.
- This type of model has been proven to perform well on temporal data (data involving time)
- Several variants:
- LSTM
- GRU
- Bidirectional RNN
Recurrent Neural Networks
Why sequence models
- In speech recognition, you are given an audio clip and asked to output a sentence.
- In music generation, you are trying to output a sequence of notes
- In sentiment classification, you are given a sentence and trying to determine rating, or analysis of the phrase (happy/sad, etc.)
- DNA sequence analysis; your DNA is represented by AGCT and you can use ML to label whether or not this sequence represents a protein
- Machine translation; one sentence to another sentence
- etc.
Notation
Motivating example. Named entity recognition.
x: Harry Potter and Herminone Granger invented a new spell.
y: [1, 1, 0, 1, 1, 0, 0, 0, 0] #is a word part of a person's name?
Index into the input/output positions is angle brackets. Index starting by 1.
$$ x^{<1>} = \text{Harry} $$
$$ y^{<3>} = 0 $$
Length of the input sequence is denoted by $T_x$. Length of the output sequence is denoted by $T_{y}$. These don’t have to be the same.
$$ T_{x} = 9 $$ $$ T_{y} = 9 $$
For multiple examples, use superscript round brackets.
For instance, the second training example, 3rd word would be represented by $x^{(2)<3>}$.
Might be useful to represent the words as a value.
Create a vocabulary dictionary where each word is laid out in an array from A to Z. (Dictionary sizes of up to 100,000 is not uncommon.) The word ‘a’ could be value 1. The word ‘and’ could be 367. This allows us to convert our sentence into a matrix of numbers.
 One-hot means only one value is set, everything else is 0.
Recurrent Neural Network Model
Why not a standard neural network? - Input and outputs can be different lengths in different examples. - Naive neural networks do not share features learned across different positions of text. - in a convolutional neural network, features are shared throughout the image, but this is less useful when ordering is important (ie: time)
Recurrent neural networks are networks where the activations calculated from the first word/sequence item are passed onto the second word/sequence item.
One weakness of this model (one directional recurrent neural network) is it doesn’t use the future sequence items to calculate the initial sequence item’s meaning.
Forward propagation steps:
$$ a^{<t>} = g(W_{aa}a^{<t-1>} + W_{ax}x^{<t>} + b_a) $$ $$ \hat{y}^{<t>} = g(W_{ya}a^{<t>} + b_y) $$
this can be simplified to:
$$ a^{<t>} = g(W_{a} [a^{<t-1>}, x^{<t>}] + b_a ) $$ $$ \hat{y}^{<t>} = g(W_{y}a^{<t>} + b_{y}) $$
Backpropagation through time
Forward propagation recall.
Loss function for a particular element in the sequence:
$$ \mathcal{L}^{<t>}(\hat{y}^{<t>}, y^{<t>}) = -y^{<t>} \log{\hat{y}^{<t>}} - (1 - y^{<t>}) \log{(1-\hat{y}^{<t>})} $$
Loss function for the entire sequence.
$$ \mathcal{L}(\hat{y}, y) = \sum\limits_{t=1}^{T_y} \mathcal{L}^{<t>}(\hat{y}^{<t>}, y^{<t>}) $$
Name for this is called Backpropagation through time.
Different types of RNNs
So far, the example shown had $T_x == T_y$. This is not always the case.
Inspired by: The Unreasonable Effectiveness of Recurrent Neural Networks
- Many to Many, where the input length and output length are the same length.
- Many to One, where for instance you are trying to determine the rating of a length of text (whether a sentence is happy or not for instance)
- One to one, a standard neural net (not really recurrant)
- One to Many, such as seeding a music generation neural network
- Many to Many, where the input length and output length are different. Network is broken into two parts, an encoder and a decoder.
Language model and sequence generation
What is language modelling? How does a machine tell the difference between The apple and pair salad
, and The apple and pear salad
?
Language modeler estimates the probability of a sequence of words. Training set requires a large corpus of english text.
Turn a sentence into a token. Turn a sentence into ‘one hot vectors’. Another common thing to do is to model the end of sentences. <EOS> token
The RNN model is trying to determine the next item in the sequence given all of the items provided in the sequence earlier.
Sampling novel sequences
Word level language model
Character level language model - no unknown word token - more computationally expensive and more difficult to capture longer term patterms
Vanishing gradients with RNNs
One of the problems of the basic RNN is vanishing gradient problem.
Consider the following sentences: - The cat, which already ate ten apples and three pears, was full. - The cats, which already ate ten apples and three pears, were full.
How do you capture the long term dependency of cat -> was
and cats -> were
? The stuff in the middle can be arbitraily long. Difficult for an item in the sequence to be influenced by values much earlier/later in the sequence.
Apply gradient clipping if your gradient starts to explode. Gradient Clipping
Gated Recurrent Unit (GRU)
Improvement to RNN to help capture long term dependencies. For reference, this is the basic recurrent neural network unit.
GRU (simplified) has a memory cell.
$$ c = \text{memory cell} $$ $$ c^{<t>} = a^{<t>} $$ $$ \tilde{c}^{<t>} = \tanh({W_c [c^{<t-1>}, x^{<t>}] + b_c}) $$ - The candidate new memory cell value $$ \Gamma_u = \sigma({W_u [c^{<t-1>}, x^{<t>}] + b_u}) $$ - Determine if this should be updated or not? The ‘u’ stands for update. Capital Gamma stands for Gate. $$ c^{<t>} = \Gamma_u * \tilde{c}^{<t>} + (1 - \Gamma_u) * c^{<t-1>} $$ - The new memory cell value
- Helps the neural network learn very long term dependencies because Gamma is either close to 0 or close to 1.
There is an additional ‘gate’, which takes the relevance into question to determine whether or not to update the memory cell.
Long Short Term Memory (LSTM)
LSTM Functions
$$ \tilde{c}^{<t>} = \tanh{(w_c [a^{<t-1>}, x^{<t>}] + b_c)} $$ $$ \Gamma_u = \sigma(w_u [a^{<t-1>}, x^{<t>}] + b_u)$$ $$ \Gamma_f = \sigma(w_f [a^{<t-1>}, x^{<t>}] + b_f)$$ $$ \Gamma_o = \sigma(w_o [a^{<t-1>}, x^{<t>}] + b_o)$$ $$ c^{<t>} = \Gamma_u * \tilde{c}^{<t>} + \Gamma_f * c^{<t-1>} $$ $$ a^{<t>} = \Gamma_o * c^{<t>} $$
The LSTM is similar to GRU, but there are a few notable differences.
- LSTM has three gates. An Update Gate, a Forget Gate, and an Output Gate.
- LSTM does not equate $a^{<t>} == c^{<t>}$.
There isn’t a widespread consensus as to when to use a GRU and when to use an LSTM. Neither algorithm is universally superior. GRU is computationally simplier. LSTM is more powerful and flexible.
Bidirectional RNN
Bidirectional RNNS allow you to take information from both earlier and later in the sequence.
Forward propagation is run once from the sequence starting from the beginning to end. Simultaneously, forward propagation is run once from the sequence starting from the end going to the beginning.
The activation function $g$ is applied on the two blocks at each sequence item.
Disadvantage of this is the computation is now doubled. Also need to calculate the entire sequence before you can make predictions. When you are doing speech processing, you have to wait until the person stops talking before you can make a prediction.
Deep RNNs
Added notation, square bracket superscript represents layer number.
Recurrent Neural Networks can be stacked on top of one another. Three layers is usually plenty enough.


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Martin Polák
Just briefly...
...few more cents.
Integration flows (scenarios) are organized in routes. Every route has 1 FROM and 1...n TOs.
XML DSL: <from uri="ftp:server/folder"/> <to uri="activemq:queue:orders"/>or
Java DSL: from("ftp:server/folder") .to("activemq:queue:orders");
Simply said: Route is a path followed by the message.


--- title: Contact map: latitude: 38.930874 longitude: -76.972874 address: "2316 Rhode Island Ave NE, Washington DC 20018" zoom: 13 image: /images/barber-shop.jpg description: This is a page description ---


I was offered £24m to dump gospel music — Panam Percy Paul
by The Eagle OnlinePopular gospel musician, Dr. Panam Percy Paul, has revealed how he was offered £24 million in order to dump gospel music in 1995 in the United Kingdom.
Paul made this known when he appeared as guest on a Dove Television station programme: “Fathers.”
The gospel musician said the company came with mouth watering offer in order for him not to mention the name of Jesus in his music because he was the most influential Christian musician in the world at that time.
According to him, the company said: “We don’t want you to be signing about Jesus.
“We have conducted our research we discovered that all of gospel musicians in the world you are the most influential.”
Paul explained that holy spirit had warned him not to travel to the country ahead of that time, adding that immediately he declined the offer, God directed him to depart from United Kingdom.
Paul said immediately he came back from the trip, his bank in Nigeria (Allied Bank) closed down and he was down financially.
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-   Prise:  $180
-   In Stock:  by request
AdvancedServo 8-Motor
Product Features
Control up to 8 RC servo motors or actuators with this controller board. Connects to your computer via USB.
Product Description
The AdvancedServo 8-Motor allows you to control the position, velocity, and acceleration of up to 8 RC servo motors. It requires a 6-15VDC external power supply; its switching power supply allows the controller to efficiently operate from 6 to 15 VDC and can be used with a wide range of batteries. All our power supplies can be used with the controller.
The controller has a high resolution of 125 steps per degree; it measures the power consumption of each servo and its switching regulator protects the motors from overvoltage. It powers servo motors of up to 3.4 Amps.
The controller continuously measures the current consumed by each motor with an accuracy of ±10%.
The AdvancedServo connects directly to a computer’s USB port.
-   Prise:  $80
-   In Stock:  by request
AdvancedServo 1-Motor
Product Features
Control one RC servo motor or actuator with this controller board. Powered solely by your computer via USB.
Product Description
The AdvancedServo 1-Motor allows you to control the position, velocity, and acceleration of one RC servo motor. The 1066 is powered solely by the USB cable – no additional power source is required.
The controller has a high resolution of 125 steps per degree; it measures the power consumption of the servo, and powers servo motors of up to 450mA.
The AdvancedServo connects directly to a computer’s USB port.
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AdvancedServo Kit 4-Motor
Product Features
Everything you need to control 4 servo motors from your PC, with the ability to add another 4 RC servos of your choosing.
Product Description
1   -   AdvancedServo 8-Motor
4   -   Deluxe Servo
1 of:
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Basic Commands
The Linux kernel includes a firewall that can be configured using the iptables program. Using the iptables program
directly can be cumbersome and hard, so instead, we’ll use a front-end called ufw (The Uncomplicated Firewall). Install ufw using apt-get
Warnings
Security rules are applied to measure a role’s relative risk for exposure to threats such as unauthorized or malicious users, or
loss or theft of confidential or proprietary data.
Do not REBOOT any server
Self Explanatory ...
Disable the Windows Update service. Not in Service Manager - just stop it. The countdown is from the service, not Windows itself.
If you stop it - no more updates UNTIL the machine is manually restarted.
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1-800-895-4562. or submit a ticket at www.grxit.com/tickets


//
Random Solar Systems
My Interactive Media Development class spent a few weeks studying random numbers and different algorithms to create them. This program takes real-world star rarity and system
data and uses it to create dynamic solar systems. Potentially millions of unique systems can be created. It makes use of 3 key algorithms: directed random, Gaussian distribution,
and Perlin noise. Systems can have multiple stars and a variable number of planets with a random number of moons each. Moons orbit planets that orbit the center of mass of the system.
Multiple stars can generate in the same system, and their barycentric coordinates are calculated dynamically based on mass ratios. You can download an executeable here.
Alternatively, here is a short image gallery:
//
Features
Animated Perlin Noise
Stars are generated on the fly, and so are their textures! The pulsing plasma is made up of colored 3D perlin noise (using the z axis to vary with time).
Real-World Data
I based the star generation off of real-world stellar data. Masses, rarity, and solar radii are all relatively accurate based on the star class.
User Interaction
A dynamic view system makes panning and zooming easy! I built a view system from the ground up to let the user see whichever parts of the system they wanted to.
A Spaceship That Explores
What good is a system if there is no-one to explore it? By default, the view is locked on to a spaceship that moves randomly and uses Perlin noise to create realistic thrust and RCS exhaust.
Copyright © 2016 Daniel Timko


plots.Rmd
The accesstocare
has 3 functions that return ggplot
/ggiraph
plots. They are primarily meant to keep all of the examples consistent and easier to change. They can also be used in an interactive R session, or in your own data product.
The included plots are:
atc_plot_hospitals()
returns a scatter plot comparing Hospital vs Population counts in a given county.atc_plot_us_map()
returns a “hexagon” map of the USA, which includes Hawaii, Alaska, and DC. It overlays data from the Access To Care analysis.atc_plot_state_map()
returns a plot with actual shape of the state, and highlights each county with a color. The color will depend on which variable is being used to plot.The output atc_plot_us_map()
function defaults to highlight the difference in the population count per state:
To display a different metric, pass the variable
argument. This example shows how to plot the number of counties from each state that are undeserved:
atc_plot_us_map("below")
The colors for the hospitals
and population
variables can be customized:
atc_plot_us_map("hospitals",
colors = list(high = "orange", low = "blue")
)
To try out the interactive version of the map, use ggiraph::girafe()
. And pass the map’s output as the ggobj
argument of that function:
ggiraph::girafe(ggobj = atc_plot_us_map())
The output of atc_plot_state_map()
defaults to displaying the shape of every individual county. The plot will display if the county has the appropriate number of hospitals, or if it has more, or if it has less than expected, based on linear model boundaries.
To view a given state’s results, pass the name as the state
argument of the function:
atc_plot_state_map("New York")
The status colors can be customized by passing the model_color
argument:
atc_plot_state_map("New York",
model_colors = list(above = "blue", below = "orange", ok = "white")
)
The hospitals
, and population
variables are also available for plotting. Pass the variable
argument to see:
atc_plot_state_map("New York",
variable = "population"
)
The colors of the continuous variables can also be customized using the color
argument:
atc_plot_state_map("New York",
variable = "population",
colors = list(low = "orange", high = "blue")
)
The display of more or less of the most populated cities can be controlled using the top_cities
argument.
atc_plot_state_map("New York", top_cities = 6)
To get a map of every county in the US, pass All US
to the state
argument:
atc_plot_state_map("All US", top_cities = 0)
To try out the interactive version of the map, use ggiraph::girafe()
. And pass the map’s output as the ggobj
argument of that function:
ggiraph::girafe(ggobj = atc_plot_state_map())
The atc_plot_hospitals()
function displays a scatter plot comparing Hospitals to Population for all the counties in the US.
Overlay the upper and lower bound of the linear model using show_model_results
:
atc_plot_hospitals(show_model_results = TRUE)
The bound colors can be customized by modifying the model_colors
argument:
atc_plot_hospitals(show_model_results = TRUE,
model_colors = list(above = "green", below = "orange")
)
To try out the interactive version of the map, use ggiraph::girafe()
. And pass the map’s output as the ggobj
argument of that function:
ggiraph::girafe(ggobj = atc_plot_hospitals())


Microsoft
Summer 2018 — I interned in the Azure Portal team, working on backend and deployment systems for Azure, Microsoft's cloud computing platform.
I also took part in OneWeek, Microsoft's flagship global hackathon and designed OneBoarding, a streamlined onboarding platform designed to empower all new hires.
01 OneBoarding
Problem Statement
As interns, we only have a limited amount of time over the summer to make a tangible impact. Currently, onboarding is frustrating.
We envision a more seamless experience – one that is efficient, accessible, and intuitive.
Issues: Wasted Resources & Opportunity
The new hire feels ready to make an impact faster because he/she feels part of the team and company.
The team doesn’t have to spend as much manpower and time onboarding the new employee.
Solution: Efficient, Inclusive Onboarding
We are creating a platform that will walk a new intern, vendor, or full-time employee through all the necessary steps to onboard to Microsoft first and their team second.
In essence, we are leveling the playing field – giving everyone the same opportunity to be successful in their day-to-day work and long-term career goals.
I envisioned, designed, and built the UI/UX mockups for the web portal in 48 hours.
Result
Demoed OneBoarding to Jeff Ramos, General Manager of Microsoft Garage, and CEO Satya Nadella's assistant.


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Definition
DOM Based XSS (or as it is called in some texts, “type-0 XSS”) is an XSS attack wherein the attack payload is executed as a result of modifying the DOM “environment” in the victim’s browser used by the original client side script, so that the client side code runs in an “unexpected” manner. That is, the page itself (the HTTP response that is) does not change, but the client side code contained in the page executes differently due to the malicious modifications that have occurred in the DOM environment.
This is in contrast to other XSS attacks (stored or reflected), wherein the attack payload is placed in the response page (due to a server side flaw).
Please note research from David Wichers seeking to reclassify DOM XSS more strictly as CLIENT SIDE XSS.
Example
Suppose the following code is used to create a form to let the user choose his/her preferred language. A default language is also provided in the query string, as the parameter “default”.
…
Select your language:
<select><script>
document.write("<OPTION value=1>"+document.location.href.substring(document.location.href.indexOf("default=")+8)+"</OPTION>");
document.write("<OPTION value=2>English</OPTION>");
</script></select>
…
The page is invoked with a URL such as:
http://www.some.site/page.html?default=French
A DOM Based XSS attack against this page can be accomplished by sending the following URL to a victim:
http://www.some.site/page.html?default=<script>alert(document.cookie)</script>
When the victim clicks on this link, the browser sends a request for:
/page.html?default=<script>alert(document.cookie)</script>
to www.some.site. The server responds with the page containing the above Javascript code. The browser creates a DOM object for the page, in which the document.location object contains the string:
http://www.some.site/page.html?default=<script>alert(document.cookie)</script>
The original Javascript code in the page does not expect the default parameter to contain HTML markup, and as such it simply echoes it into the page (DOM) at runtime. The browser then renders the resulting page and executes the attacker’s script:
alert(document.cookie)
Note that the HTTP response sent from the server does not contain the attacker’s payload. This payload manifests itself at the client-side script at runtime, when a flawed script accesses the DOM variable document.location and assumes it is not malicious.


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prefuse.data.util.RowManager
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obvious.viz.Visualization (implements obvious.util.Adaptable)
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PrefuseObviousNetworkViz
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Moving Blankets , Moving Pads , Furniture Blankets , Furniture Pads
Mini quilted moving blankets have many uses, when full-size pads are too . Terms of Sale . Small moving blankets / furniture pads are machine washable.
http://www.cameronpackaging.com/kennel_pads.htmlFurniture Pads - Moving & Packing Supplies
Convenient furniture pads protect furniture from dirt and breakage. Find furniture moving pads and other moving and packing supplies at Penske Truck Rental.
http://www.pensketruckrental.com/moving-truck-rental/moving-and-storage/furniture-pads.html
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- Photo Credit Justice image by MVit from on stage speaker stands


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Our team of dedicated software craftsmen can help bring your ideas to life.
We provide specialist advice to help you start a new project, improve the quality of your software, and nurture productive and vibrant teams.
We run public and private training courses to help you master XP (Extreme Programming) practices and build well-crafted software.
We love to share what we know and are proud organisers of the London Software Craftsmanship Community.
We don't negotiate on quality. Software must deliver more value with time and not be a source of increasing pain and cost.
Successful software projects are a result of productive partnerships. Collaboration is at the heart of our development practices.
Software development is not just a job for us. It's our passion. We are proud of our work and we are constantly improving.
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Bootstrap Colorpicker 2.5.3 Examples
For more details check
official documentation
Simple input field
As a component
With custom options
Working with events
Change background color
Transparent color support
Horizontal mode
Inline mode
Aliased color palette
Disabled / enabled status
Inside a modal
Enable
Disable
Show modal


Charlie Cu's fascination with the visual arts began at a very young age. Through out his life he continually developed his love of the visual art mediums through sketching, drawing and painting. Charlie's fine arts disciple pushed him to pursue tattooing, where he decided to center his artistic vision and focus. He has been tattooing since 2005, taking his craft across the United States before settling in Atlanta, Georgia. Charlie’s passion and inspiration for a fine arts approach to the tattoo arts lies in the roots of Japanese and American traditional tattooing. Over the years, through his study of classical tattooing, he has grown to pursue a wide range of tattoo styles including black and grey realism, portraiture, and realism surrealism subject matters.