A modern, powerful admin template built with AngularJS
Thank you for purchasing our theme. If you have any questions that are beyond the scope of this help file, please feel free to email us!
| Author: | Ndevr Studios | Author URL: | @Themeforest | Theme Version: | 1.1 | Created: | 30 Jul 2014 |
|---|---|---|---|---|---|---|---|
| Contact: |<EMAIL_ADDRESS>| Documentation Version: | 1.1 | Modified: | 11 Aug 2014 |
The easiest way to get Forza up and running is with the prebuilt
folder we've provided.
First you'll need an HTTP server. If you're on Windows, check out XAMPP.
Once you've installed an Apache server, just paste the prebuilt
in the htdocs
folder. Now visit http://localhost/prebuilt/
and Forza should be up and running!
Forza is built with the modern build flow that incorporates Bower and Grunt to automate the build process.
NOTE If you wish to skip building the project manually just use the prebuilt
folder instead that doesn't require this step.
NOTE we assume you already have Node/NPM installed on your system.
First install Grunt command line interface
npm install -g grunt-cli
The necessary package.json
and Gruntfile.js
are already there in the folder.
Once Grunt is installed, cd to the project folder and run:
npm install
This will install the necessary dependencies for running Grunt tasks.
Run the following command to install Bower globally:
npm install -g bower
Now you have Bower installed!
Now you should install the Bower packages required by Forza. To do this:
bower install
It'll install all the necessary packages for Forza in the app/bower_components
folder.
When you want to build the project for production, simply run
grunt
You may have to use sudo
if you lack permissions.
The theme is fluid layout with 12 columns. The left sidebar is removed from the grid and is fixed. The right sidebar appears on top of site and is also not a part of the grid. All content should go inside #page-content > #wrap.
The general template structure is the same throughout the template. Here is the general structure.
<!DOCTYPE html>

Standard naming convention
Wildcard routes look for a .html file with the name matching the URL segment after the #/
.
So, when you hit the URL #/layout-fixed
, Forza will make a request to the server for layout-fixed.html
partial view file located in app/views/
.
You can easily make a fixed/boxed layout by adding the fixed-layout
class to the body in the above code. You can change the width of the boxed layout and the background from the variables.less file.
In JavaScript, you can use the provided $global
service to turn on boxed layout with $global.set('layoutBoxed', true)
.
The body
needs a focusedform
class to turn on fullscreen mode.
To do this, turn on fullscreen
with $global.set('fullscreen', true)
.
The body
needs a layout-horizontal
class to use the horizontal layout.
To turn on horizontal layout programmatically, use $global.set('layoutHorizontal', true)
.
Left sidebar navigation controls
Forza has a unique and completely data-driven navigation system keeping true to the Angular methodology. Therefore, it's split between view
and model
parts.
First, the view
is set up to recursively render the menu items using the templates/nav_renderer.html
template snippet.

Note that the behavior is driven by the NavigationController
controller. This is defined in the app/scripts/controllers/Navigation.js
file.
Here's how menu items are built in JS:
$scope.menu = [ { label: 'Dashboard', iconClasses: 'fa fa-home', url: '#/', html: '4', }, { label: 'Layout Options', iconClasses: 'fa fa-cog', children: [ { label: 'Grids', url: '#/layout-grid' }, { label: 'Horizontal Navigation', url: '#/layout-horizontal' }, { label: 'Horizontal Navigation 2', url: '#/layout-horizontal2' }, { label: 'Fixed Boxed Layout', url: '#/layout-fixed' } ] } ... ]
Navigation heirarchies are defined through the children
property and thus can be extended to infinite depths!
General markup styles can for horizontal navigation can be achieved by using the horizontal-nav
class on the body as such
<!DOCTYPE html>

 <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".collapsenav"> Toggle navigation    </button>

 Dashboard
 ...

If you wish to have the large icon navigation simply add large-icons-nav
class to the #horizontal-navigation
div
Heads up! The theme is built using LESS. However you should precompile the files in CSS for improved performance in production. We recommend WinLESS
The locations and filenames of the stylesheets
All the .less files are located in assets/less/ directory of the admin theme and are called via styles.less file. Forza code and Bootstrap code are kept separate so that you can easily switch to a new Bootstrap version by overwriting the files at assets/less/bootstrap/.
The few files that have been heavily edited are:
| variables.less | Bootstrap's variables.less, with additional variables for Forza |
| navbar.less | Bootstrap's navbar.less file, heavily modified |
| layout.less | Handles the layout & left sidebar of the theme. |
| topnav.less | The topnav dropdowns |
| leftbar.less | The left sidebar navigation and search |
| rightbar.less | The right sidebar, widget area and chatbox |
| panels.less | Completely redone to support tabs |
| tiles.less | the Tiles component |
| apps.less | A few plugins used for the theme has their style modified to use with the theme. Those codes are available here. |
| forza-mixins.less | Mixins used in Forza and in some cases modification of bootstrap's mixins. |
| feeds.less | The CSS for the Feeds Panel and To Do |
| timeline.less | The CSS for Timeline apge |
| tabs.less | The CSS for the inline tabs and accordions |
| demo.less | Some styles used in displaying the demo, but no need for a fresh install |
The following files are modification on top of Bootstrap's codes
| extend-core.less | scaffolding, type, code, grid, tables, forms, buttons |
| extend-common.less | dropdown, list-groups, wells, close |
| extend-nav.less | navs, button-groups, breadcrumbs, paginations, pager |
| extend-popover.less | modals, tooltips, popovers |
| extend-misc.less | alerts, thumbnails, labels, badges, progressbars, input groups... |
A pre-compiled style.css is provided in case you are not comfortable with LESS. It is minified at styles.min.css
The classes and elements used in Forza
You can take a look at Bootstrap 3.2 Documentation for an extensive how to on how to use the theme and style the elements. Since the theme is based on top of BS 3.2, all the examples are valid albit styled differently
A large part of the theme can be customized with the included varialbles.less file
Changing the default
If you want to change the default color variation, simply comment out the current variation and uncomment any of the six other variation provided from variables.less
The location and filenames if the javascript files
The JavaScript files essential for the theme are located in the app/scripts
folder.
| app.js | Primary "entrypoint." It defines all dependencies and handles routing and temlpate behaviors. |
| controllers/*.js | Essential Angular controllers. Handles the basic behavior of the theme such as navigation. |
| shared/Directives.js | Defines all the common directives to be used throughout the webapp. |
| shared/Services.js | Common services used by the theme are defined here. |
| templates/templates.js | Cached templates for fast rendering. Cached version of files found in app/views/templates/*.html files.Note You must run grunt ngtemplates to see the changes made to html files.
|
| templates/overrides.js | Use this file to put all the template customizations. |
| app/assets/plugins/misc/less.js | The LESS pre-processor. It is removed for production and replaced by compiled CSS instead. |
$global
service
Forza comes with an Angular service named $global
which you can inject into any controller that wishes to change global template properties.
This hopes provide a singular entrypoint for all UI properties that may need to be changed without relying on nesting scopes.
For example, you use the following line to change into fullscreen layout and restore previous layout on scope destroy:
$global.set('fullscreen', true); $scope.$on('$destroy', function () { $global.set('fullscreen', false); });
Any other controllers listening for changes to service properties will be notified of this change.
Spice up your site with webfonts!

Or alternatively
Loation of the icon fonts
Forza uses FontAwesome icon library as default.
The library is included as a less file in the main styles.less
If you want to separately include the FontAwesome library, you must include the CSS file from
<link href="assets/fonts/font-awesome/css/font-awesome.min.css" rel="stylesheet">
If you wish to, however, you can include the font set from the following CDN
<link href="//netdna.bootstrapcdn.com/font-awesome/4.0.1/css/font-awesome.min.css" rel="stylesheet">
To include the glyphicon iconset, you must similarly include the CSS file from the link below. Note that Font-Awesome is used primarily in Forza and you might need to modify Forza significantly to move to this iconset. However, you can use Glyphicon and Font-Awesome side-by-side
<link href="assets/fonts/glyphicons/css/glyphicons.css" rel="stylesheet">
Plugins used through Bower or found in assets/plugins folder. Explore the documentation of each plugins for your answers on how to use them.
Neither Ndevr Studios or Forza claim any credits or responsibility of the use of these plugins. Any support regarding the plugins should be directed to the makers of the plugin
| ng-flow | Powerful multifile uploader for AngularJS |
| ng-grid | Advanced Tables for AngularJS |
| angular-ui-tree | Flexible tree-views for AngularJS |
| charts-morrisjs | Creates beautiful charts in canvas |
| Prettify's code with highlighting | |
| form-autosize | Automatically adjusts text-size area |
| form-ckeditor | Advanced WYSIWYG Editor |
| form-fseditor | jQuery Fullscreen Editor plugin transforms textfields to customizable editors, either it can be used within a form or standalone. |
| form-colorpicker | Colorpicker |
| form-datrangeepicker | Selecting Date Range |
| form-inputmask | An inputmask helps the user with the input by ensuring a predefined format. |
| form-multiselect | Userfriendly drop-in replacement for standard select |
| form-parsley | Front-end UX-aware form-validation. |
| jQuery.pulsate | jQuery.pulsate.js adds a pulsating effect to elements. |
| Date Paginator | Adds date functions to bootstrap's pagination using jQuery |
| jCrop | jQuery Image Cropping Plugin |
| bootsto.js | Show your users a guided tour of what is what on a page. |
| Angular-xeditable | Create editable elements on your page |
| Bootstrap Datepicker | A datepicker for Twitter bootstrap |
| Bootbox | Create programmatic dialog boxes using Twitter’s Bootstrap modals without managing DOM elements |
| angular-ui-select2 |
Advanced select boxes for AngularJS |
| angular-toggle-switch | Toggle buttons for AngularJS |
| bootstrap-slider | Slider module for AngularJS |
| fullcalendar | Full sized Calendar plugin |
| gmaps |
Google Maps API made simple Please check |
| jquery-mousewheel | Crossbrowser mousewheel support. For input boxes. |
| jqueryui-timepicker | Based on the existing jQuery date picker, the timepicker will blend nicely with your form and use your selected jQuery UI theme. |
| jqvmap | Resizable and scalable vector maps |
| knob | Touch enabled jQuery dial |
| mixitup | A lightweight and powerful filter and sort plugin which provides beautiful animations |
| progress-skylo | Progressbar at the top of the page |
| sparklines | Generate inline charts |
| stepy | A jQuery wizard plugin |
| form-validation | This jQuery plugin makes simple clientside form validation easy, whilst still offering plenty of customization options. This plugin is needed if you are using the the stepy for form wizard validation. |
Some plugins listed requires special modifications to work with Bootstrap 3, and thus, the theme.
Add the following line of code after you call the .stepy()
plugin. This ensures that the back and next buttons are properly indented to the panel-footer
. jQuery.steppy.js has been lightly modified with classnames used in Bootstrap 3
$('.stepy-navigator').wrapInner('

');
Add these properties to the jQuery Validation plugin to make it work with Forza. Note that this is not the plugin demonstrated in the Form Validation demo of the site but just with the wizard. The jQuery Validator support is built in to the Wizard
$('#form').validate({ errorClass: "help-block", validClass: "help-block", highlight: function(element, errorClass,validClass) { $(element).closest('.form-group').addClass("has-error"); }, unhighlight: function(element, errorClass,validClass) { $(element).closest('.form-group').removeClass("has-error"); } });
Add these properties whenever you use Parsley
//Parsley Form Validation //While the JS is not usually required in Parsley, we will be modifying //the default classes so it plays well with Bootstrap $('#vform).parsley({ successClass: 'has-success', errorClass: 'has-error', errors: { classHandler: function(el) { return $(el).closest('.form-group'); }, errorsWrapper: '

', errorElem: '

' } });
Always show navigation arrows instead of when just hovering
Find and change the first instance of #sidebar:hover li.hasChild>a:before, #sidebar:hover li.hasChild.open>a:before
in the file layout.less and remove all :hover
references
Leverage LESS Mixins
Create as many different colors schemes of panels or buttons as you want!
Simply change and add the variables in panels.less file:
.panel-color { .panel-variated(@heading-text-color, @heading-bg-color, @heading-border); }For Buttons in buttons.less:
.btn-color { .btn-pseudo-states(@btn-default-color, @btn-default-bg, @btn-default-border); }For Alternative Buttons:
.btn-color-alt { .btn-alt-states(@border); }For alerts in alerts.less:
.alert-color { .alert-variant(@background; @border; @text-color); }For labels in labels.less:
.label-color { .label-variant(@color); }For progressbars in progress-bars.less:
.progress-color { .progress-bar-variant(@color); }
8 and below
For supporting IE8 and below, please include the following in your header

Respond.js, which makes Bootstrap IE8 compatible, is inherently incompatible with LESS.js because of how they work (learn more). If you want your site to function properly in IE8 we strongly recommend that you compile your LESS files into a CSS file - it also improves performance!
The included assets/js/placeholdr.js adds drop-in HTML5 placeholder support for input boxes. However, it must be executed after jQuery
LESS is a CSS Pre-Processor. In simple terms, LESS allows you to write CSS in a smarter way by combining functions, mixins, operations and more. This means you write more concise style information and can reuse things like colors and styles more easily.
More to the point, all the .less files are in assets/less directory of the theme. Browsers cannot read less files as is, which is why we need to compile it into css. There are many applications which can serve this purpse (WinLESS, SimpLESS) or you can also use javascript to read less directly (slower, we recommend compiling into css first in production environment).
| Version | Date | Info |
| v1.1 | 11th August 2014 |
CSS optimization bugs removed Grunt flow updated Lazy loading added Progress loader added on page transitions Gallery updated Documentation updated Minor bugfixes General optimizations |
| v1.0 | 30th July 2014 | Initial Release |
Please feel free to email with your questions at<EMAIL_ADDRESS>or post in the item discussion page in Themeforest
We will be making more admin themes in the future, so if you have any suggestions we are open all ears! And if you can send us links or screenshots of your implimentation that would be really helpful in developing better themes in the future
Once again, thank you so much for purchasing this theme! We request you to rate the theme 5-stars if you like it, and if you don't, just shoot us an email or post your question in the Item Discussion section before you vote so we can solve your issues regarding the theme first.


Dayn Augustson
Professional Development
last updated: Oct 14th, 2018
Career Buddy: David Sanderson
My Links
Pro Dev Activities
My Learning Style
Visual
25% Auditory
35% Visual
40% Tactile
Quantivative
- When I first got into Electronics I worked out of very small electronic company that employed 3 people including myself.
I was made shop manger after a couple of years.
During my tenure I was able to help build the business from working out of a garage to a 5,000-foot facility that employed 15 people.
- My second job in Electronics I was put in charge of a tech department for a software company that had been losing money for years.
In my first year I was able to turn the department around, so it was making money for the company.
- In my last job I was part of a team that was able to build a full blown three bay tester from design to taking useable data within 6 months.
We were also able to do it for under 1 million dollars.
Our competitor had been working on the same project for 3 years and had spend 5 million on the project and they were never able to produce a tester that could take data.
Quantivative
- I can be very stubborn. The upshot of being stubborn is I don’t give up on problems very easily.
The down side is I sometimes I don’t really hear someone ideas when they are better than mine.
- I like to mentor people. Helping others move forward in their careers is very rewarding.
I have had to work late to finish a project because I spend several hours of my day mentoring people instead of getting my work done.
- I am a hard worker. Being a hard worker has helped me get where I am today and will help me get to my next goals in my career.
I am really bad at work life balance which can hurt my social/family life.
Whole Brain Paragraph
My girlfriend was building a score board for her Derby club.
I was asked to help when they could not get the hardware to work (she is a software person has not had to work with hardware in many years).
I redesigned the PCB boards, wiring harnesses, and grounding scheme.
While doing the electronics you have to use logic to solve the issues you also have to very creative.
For example, doing the board layouts, you have to use parts in a way for the board to work, but you also want to lay it out in a way that some one that does not work with electronics can fix problem that might come up. Same with wiring harnesses.
You want to run/build the cables so that if they need to be fixed and person with out any electrical experiences can do so.
Golden Circle
- Why: I want to build/design quietly products (website, apps, data acquisition) that I can be proud of.
- How: I don’t just want my customers to have a “web presence” I want them to be excited to show off their website.
- What: How can I help them achieve this with their website.
Elevator Pitch
Version 1
Hi my name is Dayn.
I build websites that customers can be proud to show off to the world.
I believe a website is an extension of that companies’ ideas and beliefs, and I want to create an experience that a customer is excited to show off.
How do you feel about your website?
Version 2
Visual Speaks
Be a Team player: “Talent Wins Games, but Teamwork Win Championships" -Michael Jordan.Sep 18, 2017
Be loyal to oneself: “A lot of the conflict you have in your life exists simply because you're not living in alignment; you're not be being true to yourself.”
― Steve Maraboli
Weekly Reflections
week 1
First week is in the books and it was what I expected. A lot of highs (hey, I solved a problem, I belong here) and a lot of lows (I am so lost, maybe I don’t belong here).
I am very lucky and have a lot of great people in my cohort.
Everyone is so willing to help each other out.
I have learned a lot this week and I can’t wait for Monday to start.


Assesment - Summer 2016
-
Introduction to Mathematics
Credit:5 - Result:58 - Grade:Pass
-
Introduction to Mathematics
Credit:5 - Result:58 - Grade:Pass
-
Introduction to Mathematics
Credit:5 - Result:58 - Grade:Pass
-
Introduction to Mathematics
Credit:5 - Result:58 - Grade:Pass


{% extends "users/register.html" %} {% load i18n %} {% block regcontent %}
{% blocktrans %} Validation process has failed. Please, try again. {% endblocktrans %}


Why you’ll love Antarctica
Preserved for peace and science, this ice-crowned continent rewards the traveler with inspiration, adventure and perspective. There’s nothing quite like seeing ice shimmering in all directions, reflecting sunlight through each season, or the blazing twinkle of a velvet blanket of stars in winter. Wildlife roams freely, with penguins, seals and seabirds unafraid of human visitors. Read More ››
What Antarctica is about
Antarctica consists of two main areas. East Antarctica (Greater Antarctica), and smaller West Antarctica (Lesser Antarctica) which also has the Antarctic Peninsula. West Antarctica is an extension of the Andes mountains stretching from South America. It is thought that if the ice sheet were removed, West Antarctica would actually be a collection of islands.
What Antarctica is about
Antarctica consists of two main areas. East Antarctica (Greater Antarctica), and smaller West Antarctica (Lesser Antarctica) which also has the Antarctic Peninsula. West Antarctica is an extension of the Andes mountains stretching from South America. It is thought that if the ice sheet were removed, West Antarctica would actually be a collection of islands.
Wildlife of Antarctica
The most abundant and best known animals from the southern continent. Penguins, whales seals, albatrosses, other seabirds and a range of invertebrates you may have not heard of such as krill which form the basis of the Antarctic food web.
King penguins are one of the species that people think of when the think of penguins. Along with Adelie penguins, and Emperor penguins they have come to represent the whole group. They are very upright, dignified and handsome birds and when they emerge from the sea pristine and glistening there can be no cleaner or more elegant creature around.
It would be far from a continuous smooth sheet though, as it is continuously moving. Glaciers, huge rivers of ice drain the interior of the continent and form ice shelves at the coasts. Where a glacier is moving, the ice cracks, breaks and is ruptured by the underlying rock and also by different streams meeting that move at different speeds. The ice sheet is therefore very dangerous in places as it is broken up by great crevasse fields with some cracks hundreds of feet deep and frequently covered by weak bridges formed of blown snow.
No trees or bushes at all
Vegetation is limited to about 350 species of mostly lichens, mosses, and algae. There are lush beds of such vegetation in some parts of the Antarctic Peninsula. Lichens have been discovered growing on isolated mountains within 475 km (295 mls) of the South Pole.


--- title: Contact layout: application active: contact ---
Find me on Instagram, Twitter, or LinkedIn, or send me an email:


Convert binary to ascii or the other way around!
Convert binary to ascii
Convert binary to ascii
Convert ascii to binary
Convert ascii to binary
How to use the converter:
To convert from binary to ascii:
Paste the binary into the left/upper textarea and click the button below. The result of the convertion will be displayed in right/lower textarea.
To convert from ascii to binary:
Paste the ascii into the right/lower textarea and click the button below. The result of the convertion will be displayed in left/upper textarea.


Paul McCartney to headline Glastonbury festival
London - Paul McCartney will take star billing at next year's Glastonbury when the legendary British music festival marks its 50th anniversary, organisers said.
Glastonbury co-organiser Emily Eavis said the former Beatle would headline the Pyramid Stage on 27 June - the traditional highlight of the event in Somerset, south-west England.
"Having Paul McCartney coming back to headline the Pyramid next year is an absolute dream come true," she said in a statement on the festival website on Monday.
"There really was no one that we wanted more for the 50th anniversary." McCartney previously headlined Glastonbury in 2004. This year's headliner was grime star Stormzy.
READ NEXT - NSFW: Paul McCartney shares The Beatles' sex secrets.
He dropped hints on his Twitter feed before the announcement by posting photographs of the composer Philip Glass, actress Emma Stone and the musician Chuck Berry - Glass-Stone-Berry.
All 135 000 tickets for the 50th anniversary festival went on sale last month and sold out in 34 minutes, organisers said.
Compiled by Alex Isaacs.


--- layout: post title: Mail server for development summary: Capture emails in Ubuntu development box. tags: [] ---
Capture emails in Ubuntu development box.
On Windows I use smtp4dev to capture email locally. Now I'm developing on a Ubuntu VM I want something similar.
From this answer on SO I can run the following:
sudo python -m smtpd -n -c DebuggingServer localhost:25


Mouse over this paragraph and see the cursor change to a pointer/hand.
Mouse over this paragraph and see the cursor change to a text selector (even when not over text).
Mouse over this paragraph and see the cursor change to a crosshair.
Mouse over this paragraph and see the cursor change to a wait cursor.
Mouse over this paragraph and see the cursor change to a help cursor.
Mouse over this paragraph and see the cursor change to a move cursor.
Mouse over this paragraph and see the cursor change to a north resize cursor.
Mouse over this paragraph and see the cursor change to a east resize cursor.
Mouse over this paragraph and see the cursor change to a south resize cursor.
Mouse over this paragraph and see the cursor change to a west resize cursor.
Mouse over this paragraph and see the cursor change to a north-east resize cursor.
Mouse over this paragraph and see the cursor change to a north-west resize cursor.
Mouse over this paragraph and see the cursor change to a south-east resize cursor.
Mouse over this paragraph and see the cursor change to a south-west resize cursor.
Mouse over this paragraph and see the cursor change to a custom cursor (works better in IE). Defaults to "pointer".
Mouse over this paragraph and see the cursor change to the default cursor (even when over text, links, etc).
Mouse over this paragraph and see the cursor change to the normal, variable, browser-selected ("auto") cursor - appropriate for the location of the cursor.
Mouse over this paragraph and see the cursor change to a progress cursor (requires CSS 2.1 compliant browser).


05 Apr 2022 (Last Modified 03 Mar 2023)
While Listening to the LRB Podcast I heard a promotion for a guided tour through some Greek and Latin. The offer cost too much. You pay for access to the podcasts and a new version of Emily Wilson’s chosen editions. The idea, however, was attractive. This page chronicles my discovering the classics.
May 2022 I watched Eric Lutrell’s lecture. The YouTube lecture was interesting when he discussed the evolution of our conception of an epic, but his description of the story didn’t grab me. It was too allusive. I started to read Sophus Helle’s new translation. That book’s introduction is very interesting, but the story didn’t grab me as much. Next Stpes
Feb 2022: I began with Caroline Alexander’s translation of the Iliad and the Trojan War Podcast. This podcast covers the Epic Cycle up to the Odyssey. At the same time I read Song of Achilles by Madeline Miller. (I read Circe about 5 years ago when I had no deep understanding of the Epic Cycle and my frame of reference was the Wishbone episode.)
May 2022 I read Colleen McCullough’s The Song of Troy. It is a modern retelling in the sense that there is more realpolitik rather than humans following fate’s path. Achilles for example, has visions of his mother while he has seizures. The rift between Agamemmnon and Achilles is described as a ploy to lure Hector out of Troy rather than an clash for honor. I then read Claire Haywood’s Daughters of Sparta.
Jun 2022 I read Simone Weil’s monograph, Iliad or the Poem of Force (citation). I also started Richard Lattimore’s translation and associated commentary by Malcolm Wilcox.
Jul 2022 I’m fitfully reading Lattimore’s translation and Wilcox’s commentary. I started reading Pat Barker’s Silence of the Girls
Feb 2023 I finished
May 2023 I am still fitfully reading Lattimore’s translation and Wilcox’s commentary. I have also begun The War that Killed Achilles. The author, Caroline Alexander, wrote my favorite English translation of the Iliad.
Next Steps
Mar 2022: I read Emily Wilson’s translation. I found the Odyssey much easier to read than the Iliad. Perhaps it’s the subject material. Perhaps Emily Wilson’s translation.
Mar-Apr 2022: I read Shadi Bartch’s translation and listening to Susanna Braund’s Podcast from Stanford.I had read parts of Books I and II in Roma Aeterna and discussed it in Schola Latina. I didn’t appreciate the link between Dante going to Hell and Virgil going to the Underworld until I read the translation and listen to the podcast. The excerpts I read before didn’t cover the latter half of the Aeneid.
Sep 2022- I am reading John Dryden’s translation. I very much like its flow and his lexicon.
Next Steps
Apr 2022: After reading the Aeneid in English I decided to continue chronologically rather than spreading out geographically (e.g., to Indian Classical Literature). I chose Dante’s trilogy because (1) Dante invokes Virgil as his guide and (2) I understand the journey into the underworld and its context better after reading the Odyssey followed by the Aeneid. I began by reading Allen Mandelbaum’s translation. I stopped reading the book after a month. The writing didn’t grab me, although it flowed nicely. And, I dislike writing that is so allusive and referential that it is hard to get through a sentence without a commentary. To me, works of that type come off as obfuscatory or just plain showing off.
Jul 2022: I watched the Mythology & Fiction Explained summary.
Aug 2022: I started reading Alaistair Folwer’s annotated version of Paradise Lost. I came to this version because Harold Bloom recommended it in Take Arms Against A Sea of Troubles.
Summary from Mythology & Fiction Explained.


--- layout: post title: Just given peanuts to hold us over. Grounded until 5:15pm now. date: 2010-08-06 21:23:10.000000000 -07:00 type: post published: true status: publish categories: [] tags: [] meta: {} author: ---
Brendan G. Lim Director of Mobile Development
Intridea, Inc. | www.Intridea.com
p.<PHONE_NUMBER>
Sent via iPhone


Berserker's Call
Axe taunts nearby enemy units, forcing them to attack him, while he gains bonus armor during the duration.
| Mana Cost: | 80/90/100/110 |
| Cooldown: | 16/14/12/10 |
| ABILITY: | No Target |
| PIERCES SPELL IMMUNITY: | Yes |
| DAMAGE: | 0 / 0 / 0 / 0 |
| RADIUS: | 300 |
| BONUS ARMOR: | 40 |
| DURATION: | 2 / 2.4 / 2.8 / 3.2 |
Mogul Khan's warcry taunts opponents into engaging in an unconquerable battle with the Axe.
Battle Hunger
Enrages an enemy unit, causing it to be slowed and take damage over time until it kills another unit or the duration ends. Axe gains movement speed for each unit affected with Battle Hunger.
| Mana Cost: | 75 |
| Cooldown: | 20/15/10/5 |
| ABILITY: | Unit Target |
| AFFECTS: | Enemy Units |
| DAMAGE TYPE: | Magical |
| PIERCES SPELL IMMUNITY: | No |
| DAMAGE: | 16 / 24 / 32 / 40 |
| DURATION: | 10 |
| MOVEMENT SLOW: | 12% |
| SPEED BONUS: | 12% |
Ordinary heroes cannot withstand Mogul Khan's rage for battle, such that it injures them until it is satisfied.
Counter Helix
When attacked, Axe performs a helix counter attack, dealing damage to all nearby enemies.
| Cooldown: | 0.45/0.40/0.35/0.30 |
| ABILITY: | Passive |
| DAMAGE TYPE: | Physical |
| PIERCES SPELL IMMUNITY: | Yes |
| DAMAGE: | 100 / 135 / 170 / 205 |
| RADIUS: | 275 |
| CHANCE TO HELIX: | 20% |
Axe is the only reinforcement this army needs.
Culling Blade
Axe spots a weakness and strikes, instantly killing an enemy unit with low health, or dealing moderate damage otherwise. When an enemy hero is killed with Culling Blade, its cooldown is reset, and Axe and nearby allied units gain bonus movement speed. Upgradable by Aghanim's Scepter.
| Mana Cost: | 60/120/180 |
| Cooldown: | 75/65/55 |
| ABILITY: | Unit Target |
| AFFECTS: | Enemy Units |
| DAMAGE TYPE: | Magical |
| PIERCES SPELL IMMUNITY: | Yes |
| KILL THRESHOLD: | 250 / 325 / 400 |
| DAMAGE: | 150 / 250 / 300 |
| KILL MOVE SPEED BONUS: | 30% |
| KILL ATTACK SPEED BONUS: | 30 |
| KILL SPEED DURATION: | 6 |
| KILL SPEED RADIUS: | 900 |
| SCEPTER COOLDOWN: | 6 / 6 / 6 |
| SCEPTER KILL THRESHOLD: | 300 / 425 / 550 |
| SCEPTER SPEED DURATION: | 10 |
Mogul Khan is the embodiment of battle and fury, launching into a gruesome fatality against those who dare engage the Axe in combat.


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The Greek portions of the text have been added by hand and they will require the standard "Symbol" font "symbol.ttf" to be installed in the system fonts folder. This is a standard Windows font, so should be present on most systems.
Project Gutenberg Editor's Note:The original html file with the passages in Greek in symbol.ttf font do not display in many browsers and with great distortion in IE6.


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---
layout: post
title: TITANIC SURVIVAL EXPLORATION
---
Project Overview: This project is aimed at creating decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger's features. Basically, a subset of the RMS Titanic passenger manifest is explored to determine which features best predict whether someone survived or did not survive.
Dataset Used
The dataset used in this project has:
Approach, Methodology and Conclusion
The technique applied in this project is a manual implementation of a simple machine learning model (a supervised learning), the decision tree .
A small introduction about Decision Trees:
A decision tree splits a set of data into smaller and smaller groups (called nodes), by one feature at a time. Each time a subset of the data is split, the predictions become more accurate if each of the resulting subgroups contain similar labels.
Model:
Some of the visualizations are shown below:
Implementation: Complete code can be found here.


@maintemplate("About | Triple X Archery") {
Use its simple interface to manage your archery scores while you're shooting.
The End-by-End entry option is designed with mobile access in mind.
Take 5 seconds to enter scores after each end, and view them all after the round is over.
Use the Completed Round entry option to record round scores after the fact.
Review each round in detail on an easy-to-read score sheet.
Receive updates on your friends' latest accomplishments.


{{ partial "header.html" . }}
{{ partial "head.html" . }}
err... nothing to see here!
{{ partial "footer.html" . }}


My First HTML page
T
his is my first HTML page.It is written in the text editor,saved to thehard drive and displayed on the browser.
Later it will be transferred to a Web-server for others on the network to see


The data you are working with is from yeast, the genes Gal1, Gal4, and Gal80 are all yeast transcription factors. Your expression experiments all involve some perturbation of these transcription factor genes.
Gal1, Gal4, and Gal80 are also represented in your interaction network, where they are labeled according to yeast locus tags: Gal1 corresponds to YBR020W, Gal4 to YPL248C, and Gal80 to YML051W.
GENE,COMMON,gal1RGexp,gal4RGexp,gal80Rexp,gal1RGsig,gal4RGsig,gal80Rsig YHR051W,COX6,-0.034,0.111,-0.304,3.75720e-01,1.56240e-02,7.91340e-06 YHR124W,NDT80,-0.090,0.007,-0.348,2.71460e-01,9.64330e-01,3.44760e-01 YKL181W,PRS1,-0.167,-0.233,0.112,6.27120e-03,7.89400e-04,1.44060e-01Note the following information about the file:
Now you should see expression values listed in new columns in the Node Table:
The Table Panel has the following features:


About
In the recent past, traditional countermeasures such as manually controlled inbound storage spaces or file-based storage systems have proven ineffective against internal attacks. Data classification is one of the most important and modern security measures followed by organizations to obtain DLP against internal attacks. With the rapid improvement of technology and the continuous nature of the information gathering process, an effective data classification system is required.
Most existing systems are based on text or image files, and most systems require expert knowledge to achieve maximum performance. In addition, organizations are experiencing serious problems due to the higher cost and consumption of resources in data prevention techniques. DocSpace is a single integrated system of adaptive classification of data that can classify text files and image files according to the level of sensitivity of the information.
The product is designed and developed under four main components which are functionally independent from each other.
The files will be classified using machine learning technologies based on their levels of significance with regard to the predefined parameters such as sensitivity depending on specifications such as Health Insurance Portability and Accountability Act, Payment Card Industry Data Security Standard and General Data Protection Regulation.
The high level architecture of DocSpace includes a common database and a front end application which leads to micoservice architecture. The front end application is developed using angular. Python is used to implement text classification, image to text conversion and the hashing mechanism under one service structure. Node is used to implement the file upload function and user management function under the other service structure. The whole system is connected to a common database. The integrated system allows the users to upload files using the Angular implementation
DocSpace is designed based on a complex mechanism which is advanced enough to be used in any industry domain where data classification is required. It can vary from industries which require lower level security implementations such as Education, Agriculture and Transportation to industries which require higher level security implementations such as Military, Banking and Finance. However the dataset which is to be used should be changed and tweaked based on the industry that the product will be used. For the development purpose we have trained the model based on the Banking and Finance industry which is one area where a solution similar to this is essential
The target user audiences of DocSpace can vary from small-scale organizations upto multi national large-scale companies that are handling confidential data regularly. Moreover one main objective of developing this product is to demotivate the employees who are engaged in internal data thefts for pay. It is believed that this solution will eventually motivate the industry experts to focus more on securing their organisational information while ensuring the trust and the convenience of the employees. This was the major reason to focus on innovation and commercialization equally during the development phase. As future work, the complete product can be modified as an online tool for the users to register and store the files based on the sensitivity and it would be significant if the system is implemented as a cloud-hosted software with an encryption channel per security level.
Why DocSpace?
DocSpace is designed and implemented based on the four main components. The components are independent from each other in a functional level leading to several advantages. The most specific advantage can be considered as Model Switching which is the ability to train the same model with an aid of a different dataset of the same type under a brief amount of time depending on the related audience and most importantly without affecting other components. This can be highlighted as one of the biggest loopholes in the prevailing solutions. With a feature like Model Switching, the value of the product can be raised quality wise and as well as commercially.
Moreover the fact that the product compliant with global data standards such as Health Insurance Portability and Accountability Act(HIPPA), Payment Card Industry Data Security Standard(PCIDSS) and General Data Protection Regulation(GDPR) is another advantage for the product to have a significant commercial value
Since DocSpace is implemented as a web hosted software, it is convenient for the users to work from any remote location. For the users who do not have enough technical knowledge, it is easy to work with the product because of the user adaptive system design and the user friendly interfaces. This product is designed considering the modern industrial trends such as work from home and bring your own device (BYOD) adding more value to the product
Nonetheless the most remarkable feature of the product is that this system can be modified and hosted as an online service which is able to function as a cloud storage that classifies files before storing This can be implemented as a paid service with several packages depending on the number of security levels and the storage space.
Features
DocSpace is a machine learning-based single integrated hybrid data classification system which is developed to perform text and image classification together, to re-classify the updated classified files and to prevent intentional and unintentional miss-behavior on the implemented system. The system is developed using High-Level Microservice Architecture therefore it is independently deployable. Following the global data standards is one main reason why DocSpace is a commercially valuable product. DocSpace is developed not only to classify data but also reclassify them if any changes or modification happens to either the text file or an image file. With the novel and smart technologies used in implementing DocSpace over 90% of accuracy is achieved. Throughout the design and development phase a lot of attention was given to reduce the resource consumption rate. Therefore, DocSpace is a cost-effective solution for any organization. The system is designed with user-friendly interfaces and comprehensive functions which are easier to handle without much IT knowledge to obtain the maximum security. Role-Based Access Control along with a token-based authentication is used to prevent the system and its data to maintain high-level authentication. This system has categorized the files into three classification levels namely Secret, Confidential, and private. The higher processing speed of DocSpace provides the ability to process the information quickly and without doing it consciously.
Industries
Schools
Schools contain almost all the data that a student will ever have. The Full Name, Address, Parents Names, Addresses and their Contact number, Email addresses. The School database will have all the information regarding the student and the family. This importance of data protection in schools is a forgotten fact. But it is important to secure the data in schools as well since it provides a whole package of data just up for grabs.
Financial Institutions
It is out of debate that the Financial Institutions need to have the data classified. Financial Institutions are dealing with the hard-earned money of the general public and they are even charging for that purpose. Therefore, it is a high responsibility of them and as well adherence to standard best practices to implement a data classification scheme. PCI DSS & GDPR instruct all Financial Institutions to utilize a data classification scheme to protect data.
Healthcare Organizations
There are vast amount of data circulating inside the healthcare organizations. There’s payment card information, personal identifiable information and data related to the health of individuals. These data should be confidential and there integrity should be ensured as well. Since all those other times its affecting the finances or the privacy, but here it can directly affect the health of the person. Even HIPAA emphasizes the need to implement a Data Classification Solution.
Military Organizations
Data classification itself has its origins from the Military Sector. Military organizations contain information which are crucial for a country’s and eventually the entire world’s security. Military organizations are places where the classification of information is highly in practice. Therefore, to ease their work and provide further security to information, data classification solution is the ideal solution.
Services
At DocSpace we focus on helping you transform technology into industries. From strategy to execution, our offerings include wide range of services to support organisations achieve their business automation goals.
We work proactively to ensure your stakeholders are achieving their desired outcomes, and help you maximize your value from DocSpace.
Whether you are focused on rapid development of internal business activities or a transformation of manual processes into automation, our experienced candidates can guide you from strategy to launch the best.
Developing engaging, high-impact content is often the biggest obstacle for enablement organizations. Our team of learning graduates can enable your authoring team or provide resources to augment your team.
We provide ongoing support to ensure a seamless experience for your program teams and end users.
Our Managed Services options provide trained and experienced platform capabilities to augment your team with minimal ramp-up time.
Motivation Quote
A word for your future..


--- title: "Tensor2Tensor presentation slides" layout: slides --- ## Gentle Introduction to Tensor2Tensor * Models * Modalities * Estimators * Datasets * Problems * Metrics --- ## Introduction * Training models is a very complex process and for large part similar in different projects. * Layers, optimizers, checkpointing. * TensorFlow offers many constructs on top of the basic functionality that make modeling easier. * `tf.contrib` module for volatile or experimental code. * Tensor2Tensor is a toolkit for sequence-to-sequence modeling. * Very flexible: machine translation, end-to-end ASR, image classification, language modeling, abstractive summarization. * Should be easy to get started—can even download the data automatically. * Maintained by Google Brain team. * Frequent contributions. * Bugs and incomplete documentation. --- ## Tensor2Tensor Models * Consist of three parts: input modality, model body, and output modality. * Modalities abstract away the type of source and target data (words, audio, images). * Input modality converts input into feature vectors, e.g. word embeddings. * Output modality converts activations into outputs, e.g. word IDs. * Output modality also implements the loss function. * The model body defines the architecture for low-dimensional input/output, i.e. everything between the modalities. --- ## Tensor2Tensor Models ![Input, body, and output of an encoder-decoder model](../assets/images/tensorflow-slides/tensor2tensor-modalities.png) --- ## Model Body * Base class for the model body is `T2TModel`. * The toolkit recognizes models that are registered using<EMAIL_ADDRESS>decorator (can be selected using `--model` flag). * Subclasses, e.g. `LSTMSeq2SeqAttention`, override `body()`. * `model_fn()` stacks the body function between given modalities. * During training returns the loss. * During decoding returns the output distribution for the last time step. --- ## Modalities * Base class for modalities is `Modality`. * Subclasses implement some of `bottom()`, `top()`, `loss()`. * Input modality provides the features, e.g. word embeddings, through `bottom()` method. * Output modality provides the logits through `top()` method. * During training `loss()` is used. --- ## Data Parallelism * `T2TModel` supports data parallelism. * The data that is moved between the model parts is represented as a list of tensors, one for each shard. * `expert_utils.Parallelism` is a helper class that calls a function on each element of a list and collects the results into a list. --- ## Estimator * `Estimator` class is a TensorFlow abstraction that combines a model with instructions how to use it. * Takes care of the training loop, variable initialization, checkpointing / restoring training, saving summaries for TensorBoard. * Three modes: * `TRAIN` estimates model parameters. * `PREDICT` predicts the output distribution using a trained model. * `EVAL` computes the loss and possible other evaluation metrics. * The user provides an input function `input_fn`, which the estimator uses to read the input data. When called, an input function returns two variables: * `features`, a mapping from feature name to an array of values, and * `labels`, an array of labels. --- ## Estimator Example * TensorFlow provides `Estimator` subclasses that implement some common models. * There are functions for creating `input_fn` from e.g. NumPy and pandas tables. * `feature_columns` defines what features to use and how to handle e.g. categorical data. * `model_dir` specifies a directory where checkpoints are created. * A checkpoint contains everything needed to continue training or evaluate the model. ```python feature_cols = [tf.feature_column.numeric_column("x", shape=[28, 28])] dnn = tf.estimator.DNNClassifier([32, 64, 32], feature_cols, model_dir, n_classes, dnn.train(input_fn=train_input_fn, steps=2000) accuracy = dnn_classifier.evaluate(input_fn=eval_input_fn)["accuracy"] ``` --- ## Dataset * `tf.data.Dataset` class represents a sequence of elements (for example input-output sentence pairs). * The data can be for example in tensors, text files, or binary files. * A new `Dataset` can be created by applying a transformation to another `Dataset`. * `shuffle(buffer_size)` randomizes the order of the elements. * `repeat()` iterates the dataset repeatedly. * `batch(batch_size)` always takes the next `batch_size` elements and creates a tensor with one more dimension. * `group_by_window(key_func, reduce_func, window_size)` creates batches of `window_size` elements with matching key. * `map(map_func)` performs the function `map_func` on the elements. --- ## Dataset Iterators * `tf.data.Iterator` is an interface for reading the sequence one element at a time. * Can be created from a `Dataset` using `make_one_shot_iterator()`. * Mimics Python iterator interface, which makes it confusing. * `get_next()` is called only once—it returns a `Tensor` that represents the next element. * The value of the Tensor changes on each evaluation of the graph. --- ## Input Function Example
images = tf.constant(["train/img1.png", "train/img2.png", "train/img3.png",
"train/img4.png", "train/img5.png", "train/img6.png"])
labels = tf.constant([0, 0, 0, 1, 1, 1])
def preprocess(image_path, label):
image_data = tf.read_file(image_path)
image = tf.image.decode_image(image_data, channels=3)
return image, label
def train_input_fn():
dataset = tf.data.Dataset.from_tensor_slices((images, labels))
dataset = dataset.map(preprocess)
dataset = dataset.shuffle(6)
dataset = dataset.repeat()
dataset = dataset.batch(2)
iter = dataset.make_one_shot_iterator()
return iter.get_next()
---
## Bucketing
* It would be efficient to have as little padding in mini-batches as possible.
* Tensor2Tensor uses `group_by_window()` to create mini-batches of sequences that
are similar in length.
```
def get_bucket_id(example):
seq_length = get_length(example)
cond = tf.logical_and(
tf.less_equal(buckets_min, seq_length),
tf.less(seq_length, buckets_max))
return tf.reduce_min(tf.where(cond))
def create_batch(bucket_id, dataset):
padded_shapes = dict(
[(name, [None] * len(shape))
for name, shape in dataset.output_shapes.items()])
return dataset.padded_batch(batch_size, padded_shapes)
dataset = dataset.apply(group_by_window(get_bucket_id,
create_batch,
batch_size))
```
---
## Bucketing
![Illustration of batching using group_by_window() dataset transformation](../assets/images/tensorflow-slides/group-by-window.png)
---
## TFRecord
* Before training a model with Tensor2Tensor, the dataset is converted into
TFRecord format (using t2t-datagen).
* The data is already in raw binary format and training will be faster.
* Words are converted to integer IDs, meaning that the TFRecord files are
created using a specific vocabulary.
* The data can be read and passed to an Estimator using `TFRecordDataset`.
---
## Custom Estimators
* Tensor2Tensor uses the base `Estimator` class, which takes `model_fn`,
hyperparameters, and configuration (e.g. model directory) in constructor.
* `model_fn` specifies how to compute model output, given the input and the mode.
* Such a function is given by `T2TModel` as the `estimator_model_fn()`
method.
* Returns a slightly different graph depending on the mode.
---
## Custom Estimators
* `model_fn` takes the following arguments:
* `features`: feature tensors returned by the `input_fn`
* `labels`: label tensor returned by the `input_fn`
* `mode`: `TRAIN`, `PREDICT`, or `EVAL`
* `params`: model hyperparameters
* Tensor2Tensor adds `hparams` and `decode_hparams`.
* `model_fn` returns an `EstimatorSpec` that describes the output of the model
using the input tensors:
* In `PREDICT` mode that includes the predicted probabilities.
* In `EVAL` mode that includes the loss and possibly a dictionary of
operations that are used to compute other evaluation metrics.
* In `TRAIN` mode that includes the loss and `train_op` that defines the
optimization step.
---
## Model Function Example
def model_fn(features, labels, mode, params):
hidden_activations = tf.layers.dense(features["inputs"], 256)
logits = tf.layers.dense(hidden_activations, params["num_classes"])
predicted_classes = tf.argmax(logits, 1)
# For PREDICT, the predicted classes and probabilities are needed.
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {"class_ids": predicted_classes[:, tf.newaxis],
"probabilities": tf.nn.softmax(logits),
"logits": logits}
return EstimatorSpec(mode, predictions=predictions)
# For TRAIN and EVAL, compute the loss.
loss = sparse_softmax_cross_entropy(labels, logits)
# For EVAL, compute the evaluation metrics.
if mode == tf.estimator.ModeKeys.EVAL:
accuracy = tf.metrics.accuracy(labels, predicted_classes)
metrics = {"accuracy": accuracy}
return EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)
# For TRAIN, return also the optimizer.
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
train_op = optimizer.minimize(loss)
return EstimatorSpec(mode, loss=loss, train_op=train_op)
---
## Estimator – Training
* `train_and_evaluate()` performs training, periodically evaluating the model
and possibly executing other *hooks*.
```python
train_spec = tf.estimator.TrainSpec(train_input_fn, max_steps, hooks)
eval_spec = tf.estimator.EvalSpec(eval_input_fn)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
```
* Recently changed; Tensor2Tensor uses the deprecated
`tf.contrib.learn.Experiment` class.
---
## Estimator – Hooks
* Hooks are attached to a certain point of the execution.
* Can be used to initialize, save, and monitor things.
* Derived from `SessionRunHook`, which calls a method when a session is
created or `run()` is called.
* Tensor2Tensor implements `MetricsBasedHook`, which quits training after loss
has stopped decreasing.
* A dataset can be parameterized using a `tf.placeholder()` and initialized at
the beginning of a session.
---
## Problem
* `Problem` is a Tensor2Tensor concept that defines data generation and the
evaluation metrics that are computed during training.
* These cannot be changed using hyperparameters, but many other aspects can.
* Subclasses implement `generate_data()` method that generates train and dev
sets.
* Normally downloads the data from the Internet.
* `Text2TextProblem` typically creates a vocabulary and encodes text.
* `SubwordTextEncoder` encodes text using a subword vocabulary.
---
## Problem – Metrics
* The metrics computed at each validation are defined at `eval_metrics()`.
* Can be monitored using TensorBoard.
* The metric that is used for early stopping is specified using the
`--eval_early_stopping_metric` flag.
* If not set, the loss function is used.
* Otherwise has to be one of those returned by `eval_metrics()`.
---
## Problem – Applying
* Problem classes are registered using<EMAIL_ADDRESS>decorator
and selected using the `--problem` command line flag.
* The data processing is done by `t2t-datagen` tool, which creates TFRecord
files.
* Training is done by `t2t-trainer`, which operates on the binary files.
---
## Hyperparameters
* Hyperparameters are used by the problem (e.g. vocabulary size, sampling rate)
and the model (e.g. number of layers, layer size).
* Typically a set of default hyperparameters are defined for each model in a
function that returns a `tf.contrib.training.HParams` object.
* Registered using<EMAIL_ADDRESS>decorator and selected using
the `--hparams_set` flag.
* The default values can be overridden using configuration flags.
---
## Code Organization
* `data_generators`: `Problem` and subclasses
* `data_generators/text_problems.py`: `Text2TextProblem`
* `layers/common_hparams.py`: A common set of hyperparameters
* `layers/modalities.py`: `Modality` subclasses
* `models`: `T2TModel` subclasses
* `models/lstm.py`: LSTM models and their default hyperparameter sets
* `utils/beam_search.py`: Beam search decoder
* `utils/data_reader.py`: Bucketing functions
* `utils/flags.py`: Configuration flags (other than hyperparameters)
* `utils/metrics.py`: Evaluation metrics
* `utils/modality.py`: Base `Modality` class
* `utils/rouge.py`: Functions for ROUGE score computation
* `utils/t2t_model.py`: Base `T2TModel` class
* `utils/trainer_lib.py`: Functions for creating estimator, experiment, hooks


About ten years ago I discovered a box of Ultram in my mother's medicines.
I asked her what it was and she informed me that it was given to her as a pain reliever after a hip replacement. She didn't want them as they made her 'dizzy'.
Well, of course that sounded good to me so I tried a couple and had a wonderful relaxed night that was very similar to codeine. I more or less forgot about this drug.
 I couldn't get any doctor to prescribe more than a handfull of Tylenol 3. At the same time, my dog had problems with Rimadyl, so they said they would give the dog a newer drug, which turned out to be generic Tramadol.
I figured that I might as well take the Tramadol, since it was very cheap and they prescribed more than 120 of them. It definitely helped my pain which was so bad that I couldn't sleep without taking the medication. So I did, for about 2-3 months.
 I still liked the feeling of taking 100mg. at night. Last week I decided to just stop it, as I didn't want to keep taking them although they didn't interfere with my life or work or anything. I felt fine.
The first night without them, I woke up with intestinal pain, could not sleep, was sweaty in the morning. It's been about four days and I still can't fall asleep easily, have some anxiety, terrible night sweats, uneasy feeling.
 I've had codeine of various types, dilaudid, opium, pretty much any pill and most other drugs recreationally over the years with absolutely no problems until now, and I'm almost 50.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Website
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Payments
Get paid easily online. One-time or recurring payments, no problem.
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Features & Pricing
We’re on a mission to make self-employment simple and enjoyable for everyone. Your free account will be free forever, and includes a website, invoicing, and payment processing. From there, we will continue to add features to paid accounts that are useful for freelancers and small businesses.
- Website & hosting
- Invoicing
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- Add a custom domain name
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{%if page.deal.gallery.img1_path %}
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{% else %}
{% include deal-sections/product-img.html %}
{% endif %}
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