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      "body": "[<img src=\"https://images.unsplash.com/photo-1516849841032-87cbac4d88f7?ixlib=rb-0.3.5&ixid=eyJhcHBfaWQiOjEyMDd9&s=15be9f9073988c075caa018991009b74&auto=format&fit=crop&w=2250&q=80\">](\nhttps://unsplash.com/photos/uj3hvdfQujI)\nPhoto by SpaceX on Unsplash - https://unsplash.com/photos/uj3hvdfQujI\n\nThe [Machine Learning course](https://www.coursera.org/learn/machine-learning) by Andrew Ng on Coursera is brilliant. I enjoyed it a lot. Andrew breaks complex topics down and makes them understandable for everyone. Since this course is definitely not easy I want to provide some tips for other students that are currently on this curriculum.\n\n---\n\n> “So, ask yourself: If what you're working on succeeds beyond your wildest dreams, would you have significantly helped other people? If not, then keep searching for something else to work on. Otherwise you're not living up to your full potential.” \n― Andrew Ng\n\n---\n\n## Table of Contents\n\n\n  - [The complexity of the course](#the-complexity-of-the-course)\n  - [The scientific depth of the course](#the-scientific-depth-of-the-course)\n  - [The programming assignments](#the-programming-assignments)\n    - [1. Read the documentation](#1-read-the-documentation)\n    - [2. Use the debugging functionality](#2-use-the-debugging-functionality)\n    - [3. Visualize matrices ( and variables )](#3-visualize-matrices--and-variables-)\n    - [4. Read the information provided in the task](#4-read-the-information-provided-in-the-task)\n  - [The quizzes](#the-quizzes)\n  - [Formal requirements](#formal-requirements)\n    - [Id verification](#id-verification)\n    - [Payment](#payment)\n  - [Mind the honor code](#mind-the-honor-code)\n\n\n## The complexity of the course\n\nThe complexity of this course is twofold. \n1. The content goes into depth of math and statistics\n2. To make progress you have to finish quizzes and programming assignments\n\nTo fully grasp the use of all concepts in this course I think it is necessary to do more than just following along. Going though the course and finishing everything is just one step. I think the real value lies in revisiting all topics and trying to apply it to your own use cases. That's important to keep in mind.\n\n## The scientific depth of the course\n\nSince I haven't studied math or statistics I can't really give advise on that. \n\nBut if you are discouraged along the road that everything seems to be too complicated, try to focus on the very relevant parts of the learning week and it's tests. The professor says this in the videos anyways and always points out the important parts. \n\n\n## The programming assignments\n\nThis is tough. Especially if you are not familiar with programming. But even if you have no experience with any programming language, this course provides a soft introduction to it and allows to apply basic principles for powerful results.\n\nI have used Matlab for all the challenges. I simply wanted to try it and thought there must be good documentation behind this product. And I wasn't disappointed.\n\n### 1. Read the documentation\n\nAs someone who learned programming on his own, I have dealt with this problem before. One important thing is to READ THE DOCUMENTAION. This is always the most important step. \n\nFor this course this applies for using Matlab features. The reason Matlab and Octave are recommended, is because they already offer a good variety of computing features with a solid performance. \n\nLet's take multiplying as illustration:\n\nMatlab perfectly documents this [here](https://de.mathworks.com/help/matlab/ref/mtimes.html).\n\n![screenshot](../assets/mlTipps/matlabMultiplying.png) (Source [Matlab Docs](https://de.mathworks.com/help/matlab/ref/mtimes.html))\n\nWhen you are trying to multiply vectors ( and you are going to multiply many vectors ) just reading this documentation helps to prevent a lot of problems. ( As I often have encountered and seen in forums )\n\nEspecially as it says in the description: \n\n> \"That is, A*B is typically not equal to B*A.\"\n\n### 2. Use the debugging functionality \n\nAgain, read the [documentation](https://de.mathworks.com/help/matlab/matlab_prog/debugging-process-and-features.html).\n\nIt is really easy and provides a lot of insight. \n- Just set a break point\n- Run the program in the console ( otherwise arguments are not supplied to the functions )\n- Hover over variables to see their values ( this is incredibly helpful with all the matrices )\n- Adapt you code and re-run the previous steps\n\n### 3. Visualize matrices ( and variables )\n\nIf debugging is not enough and you need a better visualization try to draw them out. This is especially useful when you have multiple errors in a longer formula calculation.\n\n### 4. Read the information provided in the task \n\nThe PDF file with the assignments contains not only valuable tips on how to solve a problem, but also gives sometimes Octave/Matlab syntax to simplify code. \nBe sure to read the assignments properly and the difficulty of the task is most of the times reduced significantly. \n\n## The quizzes\n\nThe quizzes are multiple- or single choice tests. \nYou can re-take a test 2 times and then you are blocked for 8 hours before being able to re-take the quiz again. \n\nSince there is no time pressure you can easily examine the course notes and documentation to read them again. This is not only advisable but even encouraged. In my opinion this is where the learning starts. Being able to apply the learned material to different problems. \n\nSometimes, as normal with multiple choice, the questions can be very tricky and confusing. Then re-taking the quiz might be helpful to come to a correct solution. ( Be aware that the questions and answers can change )\n\n\n## Formal requirements\n\nThe following I found to be worth mentioning:\n\n### Id verification\n\nIn order to get a certificate you need to [verify yourself](https://learner.coursera.help/hc/en-us/articles/209818953-Set-up-ID-verification). This ensures quality and credibility. Just finish the process and wait for review. \n\nIf it takes too long you can send a mail to the support team, who resolve the issue very fast. ( In my case the support was fantastic! )\n\n### Payment\n\nPayment is done without problems with a credit card. I was so amazed by the quality of this course that I found it worth to buy the certificate. Not only to have credibility for the work, but also to support the people behind it.\n\n\n## Mind the honor code \n\nThe [honor code](https://learner.coursera.help/hc/en-us/articles/209818863) shall ensure academic integrity and has to be agreed upon when doing the course.\n\nIn short, it prohibits actions, that  will \"dishonestly improve your results or dishonestly improve or damage the results of others\".\n\nThis is important to note, since it does also not allow to copy and share results. It even says on the website:\n> You may not share your solutions to homework, quizzes, or exams with anyone else unless explicitly permitted by the instructor.\n\nOf course this is hard on the internet. Keep always in mind that this course is for you, you alone. Dishonoring the code doesn't add any value to your learning experience and harms open projects like this. \n\nHere is my certificate. If you have any questions feel free to reach out :) \nThanks Andrew Ng, Stanford and the Coursera platform for making this happen. \nAdditionally I was very surprised on how well teaching can be done. Professor Ng really is a great personality and a role model for teaching. \n\n![cert](https://github.com/DDCreationStudios/Writing/raw/master/2018/assets/mlTipps/cert.jpg)\n\n---\n\nThanks for reading my article! Feel free to leave any feedback! \n\n---\n\nDaniel is a LL.M. student in business law, working as a software engineer and organizer of tech related events in Vienna. \nHis current personal learning efforts focus on machine learning. \n\nConnect on:\n- [LinkedIn](https://www.linkedin.com/in/createdd) \n- [Github](https://github.com/DDCreationStudios)\n- [Medium](https://medium.com/@ddcreationstudi)\n- [Twitter](https://twitter.com/DDCreationStudi)\n- [Steemit](https://steemit.com/@createdd)\n- [Hashnode](https://hashnode.com/@DDCreationStudio)",
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      "body": "[<img src=\"https://images.unsplash.com/photo-1508051123996-69f8caf4891d?ixlib=rb-0.3.5&ixid=eyJhcHBfaWQiOjEyMDd9&s=288fb1a782c2813ea03f2e1e1085f853&auto=format&fit=crop&w=2251&q=80\">](\nhttps://unsplash.com/photos/_NMJqIhNpno)\nPhoto by Stephen Leonardi on Unsplash - https://unsplash.com/photos/_NMJqIhNpno\n\nThe more detailled guide from [Tensorflow's website](https://www.tensorflow.org/install/install_mac) broken down. I will use the [VirtuelEnv](https://virtualenv.pypa.io/en/stable/), Python 2.7 and [zsh](http://www.zsh.org/). Enjoy!  \n\n\n## Table of Contents\n\n- [Installing Tensorflow](#installing-tensorflow)\n  - [Shell commands](#shell-commands)\n  - [Test if the installation has worked](#test-if-the-installation-has-worked)\n- [Set up Visual Studio Code](#set-up-visual-studio-code)\n  - [Set up the virtual environment](#set-up-the-virtual-environment)\n\n\n## Installing Tensorflow\n\n### Shell commands\n\n```shell\nsudo easy_install pip\n\npip install --upgrade virtualenv \n\nvirtualenv --system-site-packages <targetDirectory>\n\ncd <targetDirectory>\n\nsource ./bin/activate \n\neasy_install -U pip\n\npip install --upgrade tensorflow \n```\n\n### Test if the installation has worked\n\n```shell\npython\n\n# inside the python shell\nimport tensorflow as tf\nhello = tf.constant('Hello, TensorFlow!')\nsess = tf.Session()\nprint(sess.run(hello))\n```\nThis should show 'Hello, TensorFlow!' in the console.\n\nIf not, check out the [official homepage](https://www.tensorflow.org/install/install_mac#common_installation_problems) for a solution.\n\n## Set up Visual Studio Code\n\nInstall the Visual Studio [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)\n\n### Set up the virtual environment \n\nFor more details, see the [official site](https://code.visualstudio.com/docs/python/environments#_virtual-environments).\n\n> Tip: To see your path use 'echo $VIRTUAL_ENV'\n\nWhen the setup settings are not working, simply activate the virtualenv in the terminal with \n\n```shell\nsource ./bin/activate \n```\n\n[See a Github repository for a test.](https://github.com/DDCreationStudios/tensorFlowTest)\n\n\n---\n\nThanks for reading my article! Feel free to leave any feedback! \n\n---\n\nDaniel is a LL.M. student in business law, working as a software engineer and organizer of tech related events in Vienna. \nHis current personal learning efforts focus on machine learning. Connect on:\n- [LinkedIn](https://www.linkedin.com/in/createdd) \n- [Github](https://github.com/DDCreationStudios)\n- [Medium](https://medium.com/@ddcreationstudi)\n- [Twitter](https://twitter.com/DDCreationStudi)\n- [Steemit](https://steemit.com/@createdd)\n- [Hashnode](https://hashnode.com/@DDCreationStudio)",
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      "title": "# Install Tensorflow with Virtuelenv and Visual Studio Code on Mac"
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{
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      "author": "createdd",
      "body": "[<img src=\"https://images.unsplash.com/photo-1503642551022-c011aafb3c88?dpr=2&auto=format&fit=crop&w=1080&h=720&q=80&cs=tinysrgb&crop=\">](\nhttps://unsplash.com/photos/2vmT5_FeMck)\nPhoto by Denys Nevozhai on Unsplash - https://unsplash.com/photos/2vmT5_FeMck\n\n\nThis is short overview of machine learning. What it is, what learning is and what it's most common concepts are. It is designed as a first step into the topic.\n\n\n## 📄 Table of contents\n\n- [What is Machine Learning (ML)](#what-is-machine-learning-ml)\n- [The learning process](#the-learning-process)\n    - [1. Asking questions](#1-asking-questions)\n    - [2. Iterate](#2-iterate)\n- [ML concepts](#ml-concepts)\n    - [Data preprocessing with supervised learning](#data-preprocessing-with-supervised-learning)\n    - [Problems](#problems)\n    - [Algorithms](#algorithms)\n    - [Training the model](#training-the-model)\n\n\n---\n>\"A wise man can learn more from a foolish question than a fool can learn from a wise answer.\" - Bruce Lee\n---\n\n## What is Machine Learning (ML)\n\nML finds patterns in data and uses them to predict the future.\n\nLearning requires:\n- identifying patterns\n- recognizing those patterns\n\nNow it's easy to find patterns. But it is not easy to find patterns that are correct. Increasing the size of data allows to predict outcome that is more and more correct.\n\n|Data|Algorithm|Model|Application|\n|-|-|-|-|\n|contains patterns|finds patterns|recognizes patterns|uses to recognition on other data|\n\n\n[![img](https://upload.wikimedia.org/wikipedia/commons/thumb/1/1b/Reinforcement_learning_diagram.svg/300px-Reinforcement_learning_diagram.svg.png)](https://en.wikipedia.org/wiki/Reinforcement_learning#/media/File:Reinforcement_learning_diagram.svg)\n\nBy <a href=\"//commons.wikimedia.org/w/index.php?title=User:Megajuice&amp;action=edit&amp;redlink=1\" class=\"new\" title=\"User:Megajuice (page does not exist)\">Megajuice</a> - <a href=\"http://creativecommons.org/publicdomain/zero/1.0/deed.en\" title=\"Creative Commons Zero, Public Domain Dedication\">CC0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=57895741\">Link</a>\n\n\n\nCommon programming languages used for ML are:\n- R\n- Python\n\n## The learning process\n\n#### 1. Asking questions\n\n- what questions to ask \n- what data helps you to answer the question\n- how do you measure success\n\n#### 2. Iterate\n\n- select and prepare your data over and over to make it useable for the algorithm\n- apply an algorithm on the data and create models over and over to increase your success rate\n- expose and test successful models to different data\n\n## ML concepts \n\n- supervised learning (the value you want to predict is already in the data)\n- unsupervised learning (the value you want to predict is not in the data)\n\n\n#### Data preprocessing with supervised learning\n\nRaw data has to be transformed in to training data by removing unnecessary items like duplicates, wrong/false information, useless information. \n\nThe training data contains features, which stand for important classifications and target values, which stand for the desired piece of information for the model.\n\n#### Problems\n\n\n| |regressions|classification|clustering|\n|-|:-:|:-:|:-:|\n|*Goal*|trying to find a line or curve that fit the data|trying to group data into classes|trying to identify segments of the data|\n|*Example*|[![img](https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Linear_regression.svg/438px-Linear_regression.svg.png)](https://en.wikipedia.org/wiki/Regression_analysis#/media/File:Linear_regression.svg)|[![img](https://upload.wikimedia.org/wikipedia/commons/thumb/8/8a/Perceptron_example.svg/1224px-Perceptron_example.svg.png)](https://en.wikipedia.org/wiki/Perceptron#/media/File:Perceptron_example.svg)|[![img](https://upload.wikimedia.org/wikipedia/commons/thumb/d/d1/KMeans-density-data.svg/345px-KMeans-density-data.svg.png)](https://en.wikipedia.org/wiki/Cluster_analysis#/media/File:KMeans-density-data.svg)|\n|*Image Credit*|By <a href=\"//commons.wikimedia.org/w/index.php?title=User:Sewaqu&amp;action=edit&amp;redlink=1\" class=\"new\" title=\"User:Sewaqu (page does not exist)\">Sewaqu</a> - , Public Domain, <a href=\"https://commons.wikimedia.org/w/index.php?curid=11967659\">Link</a>|By <a href=\"//commons.wikimedia.org/w/index.php?title=User:Elizabeth_goodspeed&amp;action=edit&amp;redlink=1\" class=\"new\" title=\"User:Elizabeth goodspeed (page does not exist)\">Elizabeth Goodspeed</a> - , <a href=\"http://creativecommons.org/licenses/by-sa/4.0\" title=\"Creative Commons Attribution-Share Alike 4.0\">CC BY-SA 4.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=40188333\">Link</a>|By <a href=\"//commons.wikimedia.org/wiki/User:Chire\" title=\"User:Chire\">Chire</a> -, <a href=\"http://creativecommons.org/licenses/by-sa/3.0\" title=\"Creative Commons Attribution-Share Alike 3.0\">CC BY-SA 3.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=17085333\">Link</a>|\n\n\n\n#### Algorithms\n\nCommon styles are:\n- decision trees (construct a model based on actual values of attributes in a data)\n\n[![img](https://upload.wikimedia.org/wikipedia/commons/f/f3/CART_tree_titanic_survivors.png)](https://commons.wikimedia.org/w/index.php?curid=14143467)\n\nBy <a href=\"//commons.wikimedia.org/w/index.php?title=User:Stephen_Milborrow&amp;action=edit&amp;redlink=1\" class=\"new\" title=\"User:Stephen Milborrow (page does not exist)\">Stephen Milborrow</a> - , <a href=\"http://creativecommons.org/licenses/by-sa/3.0\" title=\"Creative Commons Attribution-Share Alike 3.0\">CC BY-SA 3.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=14143467\">Link</a>\n\n\n- neural networks (construct a model based on the recombination and reevaluation of results within the training data)\n[![img](https://upload.wikimedia.org/wikipedia/commons/thumb/4/46/Colored_neural_network.svg/296px-Colored_neural_network.svg.png)](https://commons.wikimedia.org/w/index.php?curid=24913461)\n\nBy <a href=\"//commons.wikimedia.org/wiki/User_talk:Glosser.ca\" title=\"User talk:Glosser.ca\">Glosser.ca</a> - , Derivative of <a href=\"//commons.wikimedia.org/wiki/File:Artificial_neural_network.svg\" title=\"File:Artificial neural network.svg\">File:Artificial neural network.svg</a>, <a href=\"http://creativecommons.org/licenses/by-sa/3.0\" title=\"Creative Commons Attribution-Share Alike 3.0\">CC BY-SA 3.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=24913461\">Link</a>\n\n- bayesian (filters according to probabilistic classifiers)\n\n[![img](https://upload.wikimedia.org/wikipedia/commons/thumb/0/0e/SimpleBayesNet.svg/1150px-SimpleBayesNet.svg.png)](https://en.wikipedia.org/wiki/Bayesian_network#/media/File:SimpleBayesNet.svg)\n\nBy <a href=\"https://en.wikipedia.org/wiki/User:AnAj\" class=\"extiw\" title=\"en:User:AnAj\">AnAj</a> -, Public Domain, <a href=\"https://commons.wikimedia.org/w/index.php?curid=19734596\">Link</a>\n\n- K-means (construct a model based on vector quantization to the *k* closest training examples)\n\n[![img](https://upload.wikimedia.org/wikipedia/commons/thumb/1/10/Iris_Flowers_Clustering_kMeans.svg/660px-Iris_Flowers_Clustering_kMeans.svg.png)](https://en.wikipedia.org/wiki/K-means_clustering#/media/File:Iris_Flowers_Clustering_kMeans.svg)\n\nBy <a href=\"//commons.wikimedia.org/wiki/User:Chire\" title=\"User:Chire\">Chire</a> -  Public Domain, <a href=\"https://commons.wikimedia.org/w/index.php?curid=11711077\">Link</a>\n\n(Iris flower data set, clustered using k means (left) and true species in the data set (right). Note that k-means is non-determinicstic, so results vary. Cluster means are visualized using larger, semi-transparent markers. The visualization was generated using ELKI.)\n\n\n#### Training the model\n\n\n\n1. find features that are relevant to identifying the target value\n1. put a significant percentage of the features data into the algorithm\n1. generate a model\n1. test the model with the remaining percentage of the features data by comparing the target values with the values form the actual data\n1. if the model is not accurate, change the features, change the algorithm or change the data\n\n\n[![img](https://upload.wikimedia.org/wikipedia/commons/thumb/5/54/Generic_Michigan-style_Supervised_LCS_Schematic.png/1920px-Generic_Michigan-style_Supervised_LCS_Schematic.png)](https://en.wikipedia.org/wiki/Learning_classifier_system#/media/File:Generic_Michigan-style_Supervised_LCS_Schematic.png)\nBy <a href=\"//commons.wikimedia.org/w/index.php?title=User:Docurbs&amp;action=edit&amp;redlink=1\" class=\"new\" title=\"User:Docurbs (page does not exist)\">Docurbs</a> - , <a href=\"http://creativecommons.org/licenses/by-sa/4.0\" title=\"Creative Commons Attribution-Share Alike 4.0\">CC BY-SA 4.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=52379695\">Link</a>\n\n\n\n\n---\n\nThanks for reading my article! Feel free to leave any feedback! \n\n---\n\nDaniel is a LL.M. student in business law, working as a software engineer and organizer of tech related events in Vienna. \nHis current personal learning efforts focus on machine learning. Connect on:\n- [LinkedIn](https://www.linkedin.com/in/createdd) \n- [Github](https://github.com/DDCreationStudios)\n- [Medium](https://medium.com/@ddcreationstudi)\n- [Twitter](https://twitter.com/DDCreationStudi)\n- [Hashnode](https://hashnode.com/@DDCreationStudio)",
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{
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      "author": "createdd",
      "body": "[<img src=\"https://images.unsplash.com/photo-1504639725590-34d0984388bd?auto=format&fit=crop&w=1334&q=80&ixid=dW5zcGxhc2guY29tOzs7Ozs%3D\">](\nhttps://unsplash.com/photos/w7ZyuGYNpRQ)\nPhoto by Kevin on Unsplash - https://unsplash.com/photos/w7ZyuGYNpRQ\n\nHere is a list of some extensions I come to enjoy with VS Code. Since I work a lot in the frontend most of these extensions will be useful for web developers. \nI have been working previously with Atom, Visual Studio and Webstorm but VSCode suits me best and is just incredible. The developer did a great job ( and still do! ). Try it for yourself and enjoy!\n\n---\n## 📄 Table of contents\n\n- [HTML Extensions](#html-extensions)\n  - [Auto Close Tag](#auto-close-tag)\n  - [Auto Rename Tag](#auto-rename-tag)\n  - [HTML Snippets](#html-snippets)\n- [Markdown Extensions](#markdown-extensions)\n  - [Auto-Open Markdown Preview](#auto-open-markdown-preview)\n  - [Markdown TOC](#markdown-toc)\n  - [Markdown Theme Kit](#markdown-theme-kit)\n  - [Code Spell Checker](#code-spell-checker)\n- [JavaScript Extensions](#javascript-extensions)\n  - [Babel ES6/ES7](#babel-es6es7)\n  - [Debugger for Chrome](#debugger-for-chrome)\n  - [ESLint](#eslint)\n  - [JavaScript (ES6) code snippets](#javascript-es6-code-snippets)\n  - [Code Spell Checker](#code-spell-checker-1)\n  - [Node.js Modules Intellisense](#nodejs-modules-intellisense)\n  - [React-Native/React/Redux snippets for es6/es7](#react-nativereactredux-snippets-for-es6es7)\n  - [TSLint](#tslint)\n  - [vscode-styled-components](#vscode-styled-components)\n  - [GraphQL for VSCode](#graphql-for-vscode)\n  - [yarn](#yarn)\n- [General Extensions](#general-extensions)\n  - [Color Highlight](#color-highlight)\n  - [Custom CSS and JS Loader](#custom-css-and-js-loader)\n  - [EditorConfig for VS Code](#editorconfig-for-vs-code)\n  - [File Templates](#file-templates)\n  - [Git History (git log)](#git-history-git-log)\n  - [Guides](#guides)\n  - [Material Icon Theme](#material-icon-theme)\n  - [Rainbow Brackets](#rainbow-brackets)\n  - [Prettier](#prettier)\n  - [Project Manager](#project-manager)\n  - [Theme - Seti-Monokai](#theme---seti-monokai)\n  - [WakaTime](#wakatime)\n  - [Settings Sync](#settings-sync)\n\n\n---\n>“To enjoy life, you don't need fancy nonsense, but you do need to control your time and realize that most things just aren't as serious as you make them out to be.” \n― Timothy Ferriss\n---\n\n\n## HTML Extensions\n\n### Auto Close Tag\n\n> Automatically add HTML/XML close tag, same as Visual Studio IDE or Sublime Text does.\n\n![animated](https://github.com/formulahendry/vscode-auto-close-tag/raw/master/images/usage.gif)\n\n[See more](https://marketplace.visualstudio.com/items?itemName=formulahendry.auto-close-tag)\n\n### Auto Rename Tag\n\n> Automatically rename paired HTML/XML tag, same as Visual Studio IDE does.\n\n![animated](https://github.com/formulahendry/vscode-auto-rename-tag/raw/master/images/usage.gif)\n\n[See more](https://marketplace.visualstudio.com/items?itemName=formulahendry.auto-rename-tag)\n\n### HTML Snippets\n\n> This extension adds rich language support for the HTML Markup to VS Code, including: Full HTML5 Tags, Colorization, Snippets\n\n![animated](http://i.imgur.com/VOhBvHb.gif)\n\n[See more](https://marketplace.visualstudio.com/items?itemName=abusaidm.html-snippets)\n\n## Markdown Extensions\n\n### Auto-Open Markdown Preview\n\n> This VS Code extension automatically shows Markdown preview whenever you open new Markdown file. If you feel annoying to type \"Ctrl+K V\" or \"⌘+K V\" (preview side-by-side) many times, this extension helps you.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=hnw.vscode-auto-open-markdown-preview)\n\n### Markdown TOC\n\n> Generate TOC (table of contents) of headlines from parsed markdown file.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=AlanWalk.markdown-toc)\n\n### Markdown Theme Kit\n\n> A set of themes based on SublimeText-Markdown/MarkdownEditing.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=ms-vscode.Theme-MarkdownKit)\n\n### Code Spell Checker\n\n> A basic spell checker that works well with camelCase code.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=streetsidesoftware.code-spell-checker)\n\n\n## JavaScript Extensions\n\n### Babel ES6/ES7\n\n> Adds JS Babel es6/es7 syntax coloring\n\n[See more](https://marketplace.visualstudio.com/items?itemName=dzannotti.vscode-babel-coloring)\n\n### Debugger for Chrome\n\n> Debug your JavaScript code in the Chrome browser, or any other target that supports the Chrome Debugger protocol.\n\n![animated](https://github.com/Microsoft/vscode-chrome-debug/blob/master/images/demo.gif?raw=true)\n\n[See more](https://marketplace.visualstudio.com/items?itemName=msjsdiag.debugger-for-chrome)\n\n### ESLint\n\n> Integrates ESLint into VS Code.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=dbaeumer.vscode-eslint)\n\n### JavaScript (ES6) code snippets\n\n> This extension contains code snippets for JavaScript in ES6 syntax for Vs Code editor (supports both JavaScript and TypeScript).\n\n[See more](https://marketplace.visualstudio.com/items?itemName=xabikos.JavaScriptSnippets)\n\n### Code Spell Checker\n\n> A basic spell checker that works well with camelCase code.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=leizongmin.node-module-intellisense)\n\n### Node.js Modules Intellisense\n\n> Visual Studio Code plugin that autocompletes JavaScript / TypeScript modules in import statements.\n\n![animated](https://github.com/leizongmin/vscode-node-module-intellisense/raw/master/images/auto_complete.gif)\n\n[See more](https://marketplace.visualstudio.com/items?itemName=streetsidesoftware.code-spell-checker)\n\n### React-Native/React/Redux snippets for es6/es7\n\n[See more](https://marketplace.visualstudio.com/items?itemName=EQuimper.react-native-react-redux)\n\n### TSLint\n\n> Integrates the tslint linter for the TypeScript language into VS Code.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=eg2.tslint)\n\n### vscode-styled-components\n\n> Syntax highlighting for styled-components.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=jpoissonnier.vscode-styled-components)\n\n### GraphQL for VSCode\n\n> GraphQL syntax highlighting, linting, auto-complete, and more!\n\n[See more](https://marketplace.visualstudio.com/items?itemName=kumar-harsh.graphql-for-vscode)\n\n### yarn\n\n> yarn commands for VSCode\n\n[See more](https://marketplace.visualstudio.com/items?itemName=gamunu.vscode-yarn)\n\n## General Extensions\n\n### Color Highlight\n\n> vscode-ext-color-highlight\n\nThis extension styles css/web colors found in your document.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=naumovs.color-highlight)\n\n### Custom CSS and JS Loader\n\n> Custom CSS to your VS Code. Based on robertohuertasm’s vscode-icons.\n\n![img](https://raw.githubusercontent.com/be5invis/vscode-custom-css/master/screenshot.png)\n\n[See more](https://marketplace.visualstudio.com/items?itemName=be5invis.vscode-custom-css)\n\n### EditorConfig for VS Code\n\n> EditorConfig helps developers define and maintain consistent coding styles between different editors and IDEs. The EditorConfig project consists of a file format for defining coding styles and a collection of text editor plugins that enable editors to read the file format and adhere to defined styles.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=EditorConfig.EditorConfig)\n\n### File Templates\n\n> Visual Studio code extenstion that allows to quickly create new files based on defined templates.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=brpaz.file-templates)\n\n### Git History (git log)\n\n> View git log along with the graph and details.\nView the history of a file (Git log) or the history of a line in a file (Git Blame). View a previous copy of the file. Compare a previous version with the version in the workspace or another. View commit log details for a selected commit. Compare commits.\n\n![animated](https://raw.githubusercontent.com/DonJayamanne/gitHistoryVSCode/master/images/gitLogv2.gif)\n\n[See more](https://marketplace.visualstudio.com/items?itemName=donjayamanne.githistory9)\n\n### Guides\n\n> A Visual Studio Code extension for more guide lines\n\n![img](https://github.com/spywhere/vscode-guides/raw/master/images/screenshot.png)\n\n[See more](https://marketplace.visualstudio.com/items?itemName=spywhere.guides)\n\n### Material Icon Theme\n\n> The Material Icon Theme provides lots of icons based on Material Design for Visual Studio Code.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=PKief.material-icon-theme)\n\n### Rainbow Brackets\n\n> Provide rainbow colors for the round brackets, the square brackets and the squiggly brackets. \n\n[See more](https://marketplace.visualstudio.com/items?itemName=2gua.rainbow-brackets)\n\n### Prettier\n\n> VS Code package to format your JavaScript / TypeScript / CSS using Prettier.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=esbenp.prettier-vscode)\n\n### Project Manager\n\n> Manage your projects right inside Visual Studio Code. Easily access and switch between them.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=alefragnani.project-manager)\n\n### Theme - Seti-Monokai\n\n> Seti Monokai color scheme\n\n[See more](https://marketplace.visualstudio.com/items?itemName=SmukkeKim.theme-setimonokai)\n\n### WakaTime\n\n> Metrics, insights, and time tracking automatically generated from your programming activity.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=WakaTime.vscode-wakatime)\n\n### Settings Sync\n\n> Synchronize Settings, Snippets, Themes, File Icons, Launch, Keybindings, Workspaces and Extensions Across Multiple Machines Using GitHub Gist.\n\n[See more](https://marketplace.visualstudio.com/items?itemName=Shan.code-settings-sync)\n\n---\n\nThanks for reading my article! Feel free to leave any feedback! \n\n---\n\nDaniel is a LL.M. student in business law, working as a software engineer and organizer of tech related events in Vienna. \nHis current personal learning efforts focus on machine learning. Connect on:\n- [LinkedIn](https://www.linkedin.com/in/createdd) \n- [Github](https://github.com/DDCreationStudios)\n- [Medium](https://medium.com/@ddcreationstudi)\n- [Twitter](https://twitter.com/DDCreationStudi)\n- [Hashnode](https://hashnode.com/@DDCreationStudio)",
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      "body": "[<img src=\"https://images.unsplash.com/photo-1488643637913-82a3820cf051?auto=format&fit=crop&w=1500&q=60&ixid=dW5zcGxhc2guY29tOzs7Ozs%3D\">](\nhttps://unsplash.com/photos/aRf1hjEHlhA)\nPhoto by Daniele Levis Pelusi on Unsplash - https://unsplash.com/photos/aRf1hjEHlhA\n\nYou come from a JavaScript background and don't want to go through all the Python related setup to get going with some machine learning? Check out libraries for JavaScript, there are a few!\n\n## 📄 Table of contents\n\n  - [Machine learning tools JavaScript by mljs](#machine-learning-tools-javascript-by-mljs)\n  - [Machine Learning by junku901](#machine-learning-by-junku901)\n  - [Convnetjs by karpathy](#convnetjs-by-karpathy)\n  - [Synaptic by cazala](#synaptic-by-cazala)\n  - [Mind by stevenmiller888](#mind-by-stevenmiller888)\n\n---\n>“Enjoyment appears at the boundary between boredom and anxiety, when the challenges are just balanced with the person's capacity to act.”\n― Mihaly Csikszentmihalyi\n---\n\n## Machine learning tools JavaScript by mljs\n\n[![githubLogo](../assets/GithubLogo/PNG/logo.png)](https://github.com/mljs/ml) [On Github](https://github.com/mljs/ml)\n\n> This library is a compilation of the tools developed in the mljs organization.\nIt is mainly maintained for use in the browser.\n\nCovered tools:\n\n- Unsupervised learning\n- Supervised learning\n- Artificial neural networks (ANN)\n- Regression\n- Optimization\n- Math\n- Statistics\n- Data preprocessing\n- Utilities like\n    - Bit array operations\n    - Hash table\n    - Pad array\n    - Binary search\n    - Number comparison functions for sorting\n\nActively maintained and a wide variety of features! Well done!\n\n---\n\n## Machine Learning by junku901\n\n[![githubLogo](../assets/GithubLogo/PNG/logo.png)](https://github.com/junku901/machine_learning) [On Github](https://github.com/junku901/machine_learning)\n\nCovered features:\n\n- Logistic Regression\n- MLP (Multi-Layer Perceptron)\n- SVM (Support Vector Machine)\n- KNN (K-nearest neighbors)\n- K-means clustering\n- 3 Optimization Algorithms (Hill-Climbing, Simulated Annealing, Genetic - Algorithm)\n- Decision Tree\n- NMF (non-negative matrix factorization)\n\nIt also offers an incredible browser demo here: http://joonku.com/project/machine_learning\n\nUnfortunately it doesn't seem to be maintained :( \n\nHowever, great job!\n\n---\n\n## Convnetjs by karpathy\n\n[![githubLogo](../assets/GithubLogo/PNG/logo.png)](https://github.com/karpathy/convnetjs) [On Github](https://github.com/karpathy/convnetjs)\n\nCovered features:\n- Common Neural Network modules (fully connected layers, non-linearities)\n- Classification (SVM/Softmax) and Regression (L2) cost functions\n- Ability to specify and train Convolutional Networks that process images\n- An experimental Reinforcement Learning module, based on Deep Q Learning\n\nIt also offers an incredible browser demo here: http://cs.stanford.edu/people/karpathy/convnetjs/index.html\n\nUnfortunately it is not maintained :( \nBut nevertheless a great package for learning and playing around.\n\n---\n\n## Synaptic by cazala\n\n[![githubLogo](../assets/GithubLogo/PNG/logo.png)](https://github.com/cazala/synaptic) [On Github](https://github.com/cazala/synaptic)\n\n>A architecture-free neural network library for node.js and the browser\n\nCovered features:\n- Neurons = synaptic.Neuron\n- Layers = synaptic.Layer\n- Networks = synaptic.Network\n- Trainers = synaptic.Trainer\n- Architects = synaptic.Architect\n\nIt also offers an incredible browser demo here: \nhttp://caza.la/synaptic/#/\n\nand a great starting guide:\nhttps://github.com/cazala/synaptic/wiki/Neural-Networks-101\n\nNot only is it currently maintained, but also evolving to a [next release](https://github.com/cazala/synaptic/issues/140).\nGreat package for getting started with neural networks! Well done!\n\n---\n\n## Mind by stevenmiller888\n\n[![githubLogo](../assets/GithubLogo/PNG/logo.png)](https://github.com/stevenmiller888/mind) [On Github](https://github.com/stevenmiller888/mind)\n\n>A flexible neural network library for Node.js and the browser.\n\nCovered features:\n- Vectorized - uses a matrix implementation to process training data\n- Configurable - allows you to customize the network topology\n- Pluggable - download/upload minds that have already learned\n\nIt also offers an incredible browser demo here: \nhttp://stevenmiller888.github.io/mindjs.net/\n\nand a great starting guide:\nhttp://stevenmiller888.github.io/mind-how-to-build-a-neural-network/\n\nAnother awesome package for neural networks! Well done!\n\n---\n\nThanks for reading my article! Feel free to leave any feedback! \n\n---",
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      "body": "I am a law student and always need to go through huge amounts of text. On my way to learn more natural language processing I came across text summarizing. Here is a list of some online summarizers. Enjoy.\n\n## 📄 Table of contents\n\n  - [SMMRY](#smmry)\n      - [What it does](#what-it-does)\n      - [How it works](#how-it-works)\n  - [Autosummarizer](#autosummarizer)\n  - [Summarize Tool (tools4noobs)](#summarize-tool-tools4noobs)\n  - [Text Compactor](#text-compactor)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page)\n  - [SummerizeThis](#summerizethis)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page-1)\n  - [Fee Summarizer](#fee-summarizer)\n\n---\n>\"The ability to simplify means to eliminate the unnecessary so that the necessary may speak.\" - Hans Hofmann\n---\n\n## SMMRY\n\n\n[SMMRY](http://smmry.com/)\n\n#### What it does\n\n- Ranking sentences by importance using the core algorithm.\n- Reorganizing the summary to focus on a topic; by selection of a keyword.\n- Removing transition phrases.\n- Removing unnecessary clauses.\n- Removing excessive examples.\n\n#### How it works\n\n- Associate words with their grammatical counterparts. (e.g. \"city\" and \"cities\")\n- Calculate the occurrence of each word in the text.\n- Assign each word with points depending on their popularity.\n- Detect which periods represent the end of a sentence. (e.g \"Mr.\" does not).\n- Split up the text into individual sentences.\n- Rank sentences by the sum of their words' points.\n- Return X of the most highly ranked sentences in chronological order.\n\n[Source](http://smmry.com/about) \n\n## Autosummarizer\n\n\n[Autosummarizer](http://autosummarizer.com/)\n\nThe project is under development according to the homepage. \n\nAs the homepage states: \n\n>\"The project is in development. Summarize your articles, splitting the most important sentences and ranking a sentence based on importance.\"\n\nI assume that the core algorithm is similar to the one used in [SMMRY](#how-it-works) (since there are some core and widely used libraries)\n\n\n## Summarize Tool (tools4noobs)\n\n\n[Online summarize tool](https://www.tools4noobs.com/summarize/)\n\nThis tool is a little bit more sophisticated then the previous systems.\nHere you can choose to add additional criteria like:\n- Setting a threshold\n- Setting a minimum for sentence or word length\n- Show most important words and highlight them\n- Show most important sentences and highlight them\n\n## Text Compactor\n\n\n[Text Compactor](http://www.textcompactor.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains. The most important sentence is deemed to be the sentence with the highest frequency count.\n\n>Obviously, human readers may disagree with this automated approach to text summarization. Automated text summarization works best on expository text such as textbooks and reference material (non-fiction). The results can be skewed when a passage has only a few sentences. Text Compactor is not recommended for use with fiction (i.e., stories about imaginary people, places, events).\n\n## SummerizeThis\n\n\n[SummerizeThis](https://www.summarizethis.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains.\n\n\n## Fee Summarizer\n\n\n[Fee Summarizer](http://freesummarizer.com/)\n\nPossibility to set to output to a certain number of sentences.\nCore algorithm is assumable similar to the previous mentioned websites.\n\n---\n\nThanks for reading my article! Feel free to leave any feedback!\n\n---",
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      "body": "[<img src=\"https://images.unsplash.com/photo-1501621667575-af81f1f0bacc?auto=format&fit=crop&w=1500&q=60&ixid=dW5zcGxhc2guY29tOzs7Ozs%3D\">](\nhttps://unsplash.com/photos/QRawWgV6gmo)\nPhoto by Daniil Kuželev on Unsplash - https://unsplash.com/photos/QRawWgV6gmo\n\n\nI am a law student and always need to go through huge amounts of text. On my way to learn more natural language processing I came across text summarizing. Here is a list of some online summarizers. Enjoy.\n\n## 📄 Table of contents\n\n  - [SMMRY](#smmry)\n      - [What it does](#what-it-does)\n      - [How it works](#how-it-works)\n  - [Autosummarizer](#autosummarizer)\n  - [Summarize Tool (tools4noobs)](#summarize-tool-tools4noobs)\n  - [Text Compactor](#text-compactor)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page)\n  - [SummerizeThis](#summerizethis)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page-1)\n  - [Fee Summarizer](#fee-summarizer)\n\n---\n>\"The ability to simplify means to eliminate the unnecessary so that the necessary may speak.\" - Hans Hofmann\n---\n\n## SMMRY\n\n\n[SMMRY](http://smmry.com/)\n\n#### What it does\n\n- Ranking sentences by importance using the core algorithm.\n- Reorganizing the summary to focus on a topic; by selection of a keyword.\n- Removing transition phrases.\n- Removing unnecessary clauses.\n- Removing excessive examples.\n\n#### How it works\n\n- Associate words with their grammatical counterparts. (e.g. \"city\" and \"cities\")\n- Calculate the occurrence of each word in the text.\n- Assign each word with points depending on their popularity.\n- Detect which periods represent the end of a sentence. (e.g \"Mr.\" does not).\n- Split up the text into individual sentences.\n- Rank sentences by the sum of their words' points.\n- Return X of the most highly ranked sentences in chronological order.\n\n[Source](http://smmry.com/about) \n\n## Autosummarizer\n\n\n[Autosummarizer](http://autosummarizer.com/)\n\nThe project is under development according to the homepage. \n\nAs the homepage states: \n\n>\"The project is in development. Summarize your articles, splitting the most important sentences and ranking a sentence based on importance.\"\n\nI assume that the core algorithm is similar to the one used in [SMMRY](#how-it-works) (since there are some core and widely used libraries)\n\n\n## Summarize Tool (tools4noobs)\n\n\n[Online summarize tool](https://www.tools4noobs.com/summarize/)\n\nThis tool is a little bit more sophisticated then the previous systems.\nHere you can choose to add additional criteria like:\n- Setting a threshold\n- Setting a minimum for sentence or word length\n- Show most important words and highlight them\n- Show most important sentences and highlight them\n\n## Text Compactor\n\n\n[Text Compactor](http://www.textcompactor.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains. The most important sentence is deemed to be the sentence with the highest frequency count.\n\n>Obviously, human readers may disagree with this automated approach to text summarization. Automated text summarization works best on expository text such as textbooks and reference material (non-fiction). The results can be skewed when a passage has only a few sentences. Text Compactor is not recommended for use with fiction (i.e., stories about imaginary people, places, events).\n\n## SummerizeThis\n\n\n[SummerizeThis](https://www.summarizethis.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains.\n\n\n## Fee Summarizer\n\n\n[Fee Summarizer](http://freesummarizer.com/)\n\nPossibility to set to output to a certain number of sentences.\nCore algorithm is assumable similar to the previous mentioned websites.\n\n---\n\nThanks for reading my article! Feel free to leave any feedback!\n\n---",
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      "body": "[<img src=\"https://images.unsplash.com/photo-1501621667575-af81f1f0bacc?auto=format&fit=crop&w=1500&q=60&ixid=dW5zcGxhc2guY29tOzs7Ozs%3D\">](\nhttps://unsplash.com/photos/QRawWgV6gmo)\nPhoto by Daniil Kuželev on Unsplash - https://unsplash.com/photos/QRawWgV6gmo\n\n\nI am a law student and always need to go through huge amounts of text. On my way to learn more natural language processing I came across text summarizing. Here is a list of some online summarizers. Enjoy.\n\n## 📄 Table of contents\n\n  - [SMMRY](#smmry)\n      - [What it does](#what-it-does)\n      - [How it works](#how-it-works)\n  - [Autosummarizer](#autosummarizer)\n  - [Summarize Tool (tools4noobs)](#summarize-tool-tools4noobs)\n  - [Text Compactor](#text-compactor)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page)\n  - [SummerizeThis](#summerizethis)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page-1)\n  - [Fee Summarizer](#fee-summarizer)\n\n---\n>\"The ability to simplify means to eliminate the unnecessary so that the necessary may speak.\" - Hans Hofmann\n---\n\n## SMMRY\n\n\n[SMMRY](http://smmry.com/)\n\n#### What it does\n\n- Ranking sentences by importance using the core algorithm.\n- Reorganizing the summary to focus on a topic; by selection of a keyword.\n- Removing transition phrases.\n- Removing unnecessary clauses.\n- Removing excessive examples.\n\n#### How it works\n\n- Associate words with their grammatical counterparts. (e.g. \"city\" and \"cities\")\n- Calculate the occurrence of each word in the text.\n- Assign each word with points depending on their popularity.\n- Detect which periods represent the end of a sentence. (e.g \"Mr.\" does not).\n- Split up the text into individual sentences.\n- Rank sentences by the sum of their words' points.\n- Return X of the most highly ranked sentences in chronological order.\n\n[Source](http://smmry.com/about) \n\n## Autosummarizer\n\n\n[Autosummarizer](http://autosummarizer.com/)\n\nThe project is under development according to the homepage. \n\nAs the homepage states: \n\n>\"The project is in development. Summarize your articles, splitting the most important sentences and ranking a sentence based on importance.\"\n\nI assume that the core algorithm is similar to the one used in [SMMRY](#how-it-works) (since there are some core and widely used libraries)\n\n\n## Summarize Tool (tools4noobs)\n\n\n[Online summarize tool](https://www.tools4noobs.com/summarize/)\n\nThis tool is a little bit more sophisticated then the previous systems.\nHere you can choose to add additional criteria like:\n- Setting a threshold\n- Setting a minimum for sentence or word length\n- Show most important words and highlight them\n- Show most important sentences and highlight them\n\n## Text Compactor\n\n\n[Text Compactor](http://www.textcompactor.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains. The most important sentence is deemed to be the sentence with the highest frequency count.\n\n>Obviously, human readers may disagree with this automated approach to text summarization. Automated text summarization works best on expository text such as textbooks and reference material (non-fiction). The results can be skewed when a passage has only a few sentences. Text Compactor is not recommended for use with fiction (i.e., stories about imaginary people, places, events).\n\n## SummerizeThis\n\n\n[SummerizeThis](https://www.summarizethis.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains.\n\n\n## Fee Summarizer\n\n\n[Fee Summarizer](http://freesummarizer.com/)\n\nPossibility to set to output to a certain number of sentences.\nCore algorithm is assumable similar to the previous mentioned websites.\n\n---\n\nThanks for reading my article! Feel free to leave any feedback!\n\n---",
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      "body": "[<img src=\"https://images.unsplash.com/photo-1501621667575-af81f1f0bacc?auto=format&fit=crop&w=1500&q=60&ixid=dW5zcGxhc2guY29tOzs7Ozs%3D\">](\nhttps://unsplash.com/photos/QRawWgV6gmo)\nPhoto by Daniil Kuželev on Unsplash - https://unsplash.com/photos/QRawWgV6gmo\n\n\nI am a law student and always need to go through huge amounts of text. On my way to learn more natural language processing I came across text summarizing. Here is a list of some online summarizers. Enjoy.\n\n## 📄 Table of contents\n\n  - [SMMRY](#smmry)\n      - [What it does](#what-it-does)\n      - [How it works](#how-it-works)\n  - [Autosummarizer](#autosummarizer)\n  - [Summarize Tool (tools4noobs)](#summarize-tool-tools4noobs)\n  - [Text Compactor](#text-compactor)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page)\n  - [SummerizeThis](#summerizethis)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page-1)\n  - [Fee Summarizer](#fee-summarizer)\n\n---\n>\"The ability to simplify means to eliminate the unnecessary so that the necessary may speak.\" - Hans Hofmann\n---\n\n## SMMRY\n\n\n[SMMRY](http://smmry.com/)\n\n#### What it does\n\n- Ranking sentences by importance using the core algorithm.\n- Reorganizing the summary to focus on a topic; by selection of a keyword.\n- Removing transition phrases.\n- Removing unnecessary clauses.\n- Removing excessive examples.\n\n#### How it works\n\n- Associate words with their grammatical counterparts. (e.g. \"city\" and \"cities\")\n- Calculate the occurrence of each word in the text.\n- Assign each word with points depending on their popularity.\n- Detect which periods represent the end of a sentence. (e.g \"Mr.\" does not).\n- Split up the text into individual sentences.\n- Rank sentences by the sum of their words' points.\n- Return X of the most highly ranked sentences in chronological order.\n\n[Source](http://smmry.com/about) \n\n## Autosummarizer\n\n\n[Autosummarizer](http://autosummarizer.com/)\n\nThe project is under development according to the homepage. \n\nAs the homepage states: \n\n>\"The project is in development. Summarize your articles, splitting the most important sentences and ranking a sentence based on importance.\"\n\nI assume that the core algorithm is similar to the one used in [SMMRY](#how-it-works) (since there are some core and widely used libraries)\n\n\n## Summarize Tool (tools4noobs)\n\n\n[Online summarize tool](https://www.tools4noobs.com/summarize/)\n\nThis tool is a little bit more sophisticated then the previous systems.\nHere you can choose to add additional criteria like:\n- Setting a threshold\n- Setting a minimum for sentence or word length\n- Show most important words and highlight them\n- Show most important sentences and highlight them\n\n## Text Compactor\n\n\n[Text Compactor](http://www.textcompactor.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains. The most important sentence is deemed to be the sentence with the highest frequency count.\n\n>Obviously, human readers may disagree with this automated approach to text summarization. Automated text summarization works best on expository text such as textbooks and reference material (non-fiction). The results can be skewed when a passage has only a few sentences. Text Compactor is not recommended for use with fiction (i.e., stories about imaginary people, places, events).\n\n## SummerizeThis\n\n\n[SummerizeThis](https://www.summarizethis.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains.\n\n\n## Fee Summarizer\n\n\n[Fee Summarizer](http://freesummarizer.com/)\n\nPossibility to set to output to a certain number of sentences.\nCore algorithm is assumable similar to the previous mentioned websites.\n\n---\n\nThanks for reading my article! Feel free to leave any feedback!\n\n---",
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      "body": "helped a lot! Thanks!",
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      "body": "Hi! I am a robot. I just upvoted you! I found similar content that readers might be interested in:\nhttps://medium.com/@ddcreationstudi/online-text-summarizers-2017-3c80e4b2862c",
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      "body": "[<img src=\"https://images.unsplash.com/photo-1501621667575-af81f1f0bacc?auto=format&fit=crop&w=1500&q=60&ixid=dW5zcGxhc2guY29tOzs7Ozs%3D\">](\nhttps://unsplash.com/photos/QRawWgV6gmo)\nPhoto by Daniil Kuželev on Unsplash - https://unsplash.com/photos/QRawWgV6gmo\n\n\nI am a law student and always need to go through huge amounts of text. On my way to learn more natural language processing I came across text summarizing. Here is a list of some online summarizers. Enjoy.\n\n## 📄 Table of contents\n\n  - [SMMRY](#smmry)\n      - [What it does](#what-it-does)\n      - [How it works](#how-it-works)\n  - [Autosummarizer](#autosummarizer)\n  - [Summarize Tool (tools4noobs)](#summarize-tool-tools4noobs)\n  - [Text Compactor](#text-compactor)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page)\n  - [SummerizeThis](#summerizethis)\n      - [How it works (according to their page)](#how-it-works-according-to-their-page-1)\n  - [Fee Summarizer](#fee-summarizer)\n\n---\n>\"The ability to simplify means to eliminate the unnecessary so that the necessary may speak.\" - Hans Hofmann\n---\n\n## SMMRY\n\n\n[SMMRY](http://smmry.com/)\n\n#### What it does\n\n- Ranking sentences by importance using the core algorithm.\n- Reorganizing the summary to focus on a topic; by selection of a keyword.\n- Removing transition phrases.\n- Removing unnecessary clauses.\n- Removing excessive examples.\n\n#### How it works\n\n- Associate words with their grammatical counterparts. (e.g. \"city\" and \"cities\")\n- Calculate the occurrence of each word in the text.\n- Assign each word with points depending on their popularity.\n- Detect which periods represent the end of a sentence. (e.g \"Mr.\" does not).\n- Split up the text into individual sentences.\n- Rank sentences by the sum of their words' points.\n- Return X of the most highly ranked sentences in chronological order.\n\n[Source](http://smmry.com/about) \n\n## Autosummarizer\n\n\n[Autosummarizer](http://autosummarizer.com/)\n\nThe project is under development according to the homepage. \n\nAs the homepage states: \n\n>\"The project is in development. Summarize your articles, splitting the most important sentences and ranking a sentence based on importance.\"\n\nI assume that the core algorithm is similar to the one used in [SMMRY](#how-it-works) (since there are some core and widely used libraries)\n\n\n## Summarize Tool (tools4noobs)\n\n\n[Online summarize tool](https://www.tools4noobs.com/summarize/)\n\nThis tool is a little bit more sophisticated then the previous systems.\nHere you can choose to add additional criteria like:\n- Setting a threshold\n- Setting a minimum for sentence or word length\n- Show most important words and highlight them\n- Show most important sentences and highlight them\n\n## Text Compactor\n\n\n[Text Compactor](http://www.textcompactor.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains. The most important sentence is deemed to be the sentence with the highest frequency count.\n\n>Obviously, human readers may disagree with this automated approach to text summarization. Automated text summarization works best on expository text such as textbooks and reference material (non-fiction). The results can be skewed when a passage has only a few sentences. Text Compactor is not recommended for use with fiction (i.e., stories about imaginary people, places, events).\n\n## SummerizeThis\n\n\n[SummerizeThis](https://www.summarizethis.com/)\n\n#### How it works (according to their page)\n\n>After text is placed on the page, the web app calculates the frequency of each word in the passage. Then, a score is calculated for each sentence based on the frequency count associated with the words it contains.\n\n\n## Fee Summarizer\n\n\n[Fee Summarizer](http://freesummarizer.com/)\n\nPossibility to set to output to a certain number of sentences.\nCore algorithm is assumable similar to the previous mentioned websites.\n\n---\n\nThanks for reading my article! Feel free to leave any feedback!\n\n---",
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