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https://github.com/CodingTrain/Machine-Learning

Examples and experiments around ML for upcoming Coding Train videos
https://github.com/CodingTrain/Machine-Learning
Last synced: 3 months ago
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Examples and experiments around ML for upcoming Coding Train videos
Host: GitHub
URL: https://github.com/CodingTrain/Machine-Learning
Owner: CodingTrain
Created: 2017-02-18T16:54:58.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2021-05-25T04:40:32.000Z (over 3 years ago)
Last Synced: 2024-05-07T23:35:43.694Z (6 months ago)
Size: 309 KB
Stars: 948
Watchers: 77
Forks: 197
Open Issues: 4
Metadata Files:
- Readme: README.md
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README

        [![Dreams in the CodingTrain](https://raw.githubusercontent.com/CodingTrain/Machine-Learning/master/codingdream.jpg)](http://thecodingtrain.com/)

# Machine-Learning

Examples and experiments around ML for upcoming Coding Train videos and ITP course.

# Resource attributes

Since resources across the internet vary in terms of their pre-requisites and general accessibility, it is useful to

give attributes to them so that it is easy to understand where a resource fits into the wider machine learning scope. Below is a few suggested attributes (please extend):

 - :rainbow: = creative

 - :bowtie: = beginner

 - :sweat_smile: = intermediate, some pre-requisites

 - :godmode: = advanced, many pre-requisites

# Table of Contents

- [Articles & Posts](#articles--posts)

- [Books](#books)

- [Courses](#courses)

- [Examples](#examples)

- [Projects](#projects)

- [Videos](#videos)

- [Resources](#resources)

- [Newsletter](#newsletter)

- [Tools](#tools)

    - [Tensorflow](#tensorflow)

    - [t-SNE](#t-sne)

## Articles & Posts

  1. [A Return to Machine Learning](https://medium.com/@kcimc/a-return-to-machine-learning-2de3728558eb#.vlqnbo9yg) :rainbow: :bowtie:

  1. [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) :rainbow: :bowtie:

  1. [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471) :bowtie:

  1. [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/) :rainbow:

  1. [Inside Libratus, the Poker AI That Out-Bluffed the Best Humans](https://www.wired.com/2017/02/libratus/?imm_mid=0ed017&cmp=em-data-na-na-newsltr_ai_20170206) :bowtie:

  1. [Machine Learning in Javascript: Introduction](http://burakkanber.com/blog/machine-learning-in-other-languages-introduction/) :bowtie:

  1. [Realtime control of sequence generation with character based Long Short Term Memory Recurrent Neural Networks](http://www.iggi.org.uk/assets/IGGI-2016-Memo-A.pdf) :sweat_smile:

  1. [Why is machine learning 'hard'?](http://ai.stanford.edu/~zayd/why-is-machine-learning-hard.html) :bowtie:

  1. [Unreasonable effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) :sweat_smile:

  1. [colah's blog](http://colah.github.io/)

  1. ‪[Machine Learning Website with many Tutorial of Machine Learning‪](https://machinelearningmastery.com/start-here/‬) ‬:rainbow:

  1. [Beginners tutorial for decision tree implementation](https://www.dezyre.com/data-science-in-r-programming-tutorial/decision-tree-tutorial) :rainbow:‪

  1. [Machine Learning Beginner tutorial Supervised and Unsupervised Learning](http://dataaspirant.com/2014/09/19/supervised-and-unsupervised-learning/‬) :rainbow:‪

  1. [Q-Learning Tutorial](http://outlace.com/rlpart3.html) :sweat_smile:

  1. [Big O notation Free Code Camp](https://medium.freecodecamp.com/time-is-complex-but-priceless-f0abd015063c?source=linkShare-4599aaae9f0b-1489449307) :bowtie:

  1. [Ray Wenderlich Big O notation](https://github.com/raywenderlich/swift-algorithm-club/blob/master/Big-O%20Notation.markdown) :bowtie:

  1. [Interview Cake Big O notation](https://www.interviewcake.com/article/java/big-o-notation-time-and-space-complexity) :bowtie:

  1. [Youtube Video Big O notation Derek Banas](https://m.youtube.com/watch?v=V6mKVRU1evU) :bowtie:

  1. [Youtube Video for Big O notation HackerRank](https://youtu.be/v4cd1O4zkGw) :bowtie:

  1. [Random Forest in Python](http://blog.yhat.com/posts/random-forests-in-python.html) :sweat_smile:

  1. [CreativeAI - On the Democratisation & Escalation of Creativity](https://medium.com/@creativeai/creativeai-9d4b2346faf3#.8oaibcklb) :rainbow: :bowtie:

  1. [Reducing the Dimensionality of Data with Neural Networks](https://www.cs.toronto.edu/~hinton/science.pdf)

  1. [Learning Deep Architectures for AI](https://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf)

  1. [Let’s code a Neural Network from scratch (Processing)](https://medium.com/typeme/lets-code-a-neural-network-from-scratch-part-1-24f0a30d7d62) :sweat_smile:

  1. [Distill - Demystifying Machine Learning Research](http://distill.pub/)

  1. [Machine Learning in Javascript](http://burakkanber.com/tag/ml-in-js/) :sweat_smile:

  1. [A.I. Experiments from google](https://aiexperiments.withgoogle.com/)    

  1. [Rohan & Lenny #3: Recurrent Neural Networks & LSTMs](https://ayearofai.com/rohan-lenny-3-recurrent-neural-networks-10300100899b) :sweat_smile:

  1. [Backpropogating an LSTM: A Numerical Example](https://medium.com/@aidangomez/let-s-do-this-f9b699de31d9) :sweat_smile:

  1. [Naive Bayes for Dummies; A Simple Explanation](http://blog.aylien.com/naive-bayes-for-dummies-a-simple-explanation/) :bowtie:

  1. [Machine Learning Crash Course @ Berkeley](https://ml.berkeley.edu/blog/tutorials/) :bowtie: :godmode:

  1. [How to approach almost any ML problem?](http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/) :sweat_smile:

  1. [Technical Notes on ML & AI by Chris Albon](https://chrisalbon.com/#machine_learning) :bowtie: :sweat_smile:

  1. [Naive Bayes and Text Classification](https://sebastianraschka.com/Articles/2014_naive_bayes_1.html) :sweat_smile:

  1. [First Contact With TensorFlow](https://torres.ai/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/) :sweat_smile:

## Books

  1. [Machine Learning for Designers](http://www.oreilly.com/design/free/machine-learning-for-designers.csp) by [Patrick Hebron](http://www.patrickhebron.com/), [Accompanying Webcast: Machine learning and the future of design](http://www.oreilly.com/pub/e/3709)

  1. [Machine Learning Book](https://machinelearningmastery.com/master-machine-learning-algorithms/) :rainbow:

  1. [A first encounter with machine learning](https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf) :bowtie:

  1. [Natural Language Processing with Python](https://www.nltk.org/book/) :sweat_smile: :bowtie:

  1. [A Brief Introduction to Neural Networks](http://www.dkriesel.com/en/science/neural_networks) :sweat_smile:

## Courses

  1. [Machine Learning Crash Course By Google](https://developers.google.com/machine-learning/crash-course/) :bowtie:

  2. [Coursera - Machine Learning with TensorFlow on GCP](https://www.coursera.org/specializations/machine-learning-tensorflow-gcp?action=enroll) :sweat_smile:

  3. [The Neural Aesthetic @ SchoolOfMa, Summer 2016](https://ml4a.github.io/classes/neural-aesthetic/) :rainbow: :bowtie:

  4. [Machine Learning for Musicians and Artists, Kadenze](https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists-i)[Scheduled course] :rainbow: :bowtie:

  5. [Creative Applications of Deep Learning with TensorFlow, Kadenze](https://www.kadenze.com/programs/creative-applications-of-deep-learning-with-tensorflow)[Whole Program] :rainbow: :sweat_smile:

  6. [Coursera - Machine Learning](https://www.coursera.org/learn/machine-learning) :bowtie:

  7. [Coursera - Neural Networks](https://www.coursera.org/learn/neural-networks-deep-learning) :sweat_smile:

  8. [Practical Deep Learning for Coders](http://www.fast.ai/2017/02/24/captions-and-notes/) :bowtie:

  9. [‪Course in Machine Learning](http://ciml.info/?utm_source=mybridge&utm_medium=ios&utm_campaign=read_more‬)

  10. [‪Stanford Course Machine Learning](http://cs229.stanford.edu/syllabus.html)

  11. [Udacity - Machine Learning Engineer](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009)[Whole Program] :sweat_smile:

  12. [DeepMind - Reinforcement Learning lectures by David Silver](https://www.youtube.com/playlist?list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT)

## Examples

  1. [A Deep Q Reinforcement Learning Demo](http://projects.rajivshah.com/rldemo/) :bowtie:

  1. [How to use Q Learning in Video Games Easily](https://github.com/llSourcell/q_learning_demo) :rainbow: :bowtie:

  1. [K-nearest](https://twitter.com/MaximilianLloyd/status/814942799351185408) :bowtie:

  1. [The Infinite Drum Machine](https://aiexperiments.withgoogle.com/drum-machine/view/) :rainbow: :bowtie:

  1. [Visualizing various ML algorithms](https://kwichmann.github.io/ml_sandbox/) :rainbow: :bowtie:

  1. [Image-to-Image - from lines to cats](http://affinelayer.com/pixsrv/) :rainbow:

  2. [Recurrent Neural Network Tutorial for Artists](http://blog.otoro.net/2017/01/01/recurrent-neural-network-artist/) :rainbow:

  1. [Browser Self-Driving Car](http://janhuenermann.com/projects/learning-to-drive),[Learning to Drive Blog Post](http://lab.janhuenermann.de/article/learning-to-drive)

  1. [The Neural Network Zoo (cheat sheet of nn architectures)](http://www.asimovinstitute.org/neural-network-zoo/)

  1. [Slice of Machine Learning](https://sliceofml.withgoogle.com/#/)

## Projects

  1. [Bidirectional LSTM for IMDB sentiment classification](https://transcranial.github.io/keras-js/#/imdb-bidirectional-lstm) :sweat_smile:

  1. [Land Lines](https://medium.com/@zachlieberman/land-lines-e1f88c745847#.1157xmhw8)

  1. [nnvis - Topological Visualisation of a Convolutional Neural Network](http://terencebroad.com/convnetvis/vis.html) :rainbow: :bowtie:

  1. [char-rnn A character level language model (a fancy text generator)](https://github.com/karpathy/char-rnn) :rainbow: :sweat_smile:

  1. [Machine Learning Projects](http://blog.yhat.com/posts/ML-to-watch.html)

## Videos

  * Reinforcement Learning

    1. [Artificial Intelligence in Google's Dinosaur (English Sub)](https://www.youtube.com/watch?v=P7XHzqZjXQs) :bowtie:

    1. [How to use Q Learning in Video Games Easily](https://www.youtube.com/watch?v=A5eihauRQvo&feature=youtu.be) :bowtie:

  * Evolutionary Algorithms

    1. [Evolving Swimming Soft-Bodied Creatures](https://www.youtube.com/watch?v=4ZqdvYrZ3ro) :rainbow: :bowtie:

    1. [Harnessing evolutionary creativity: evolving soft-bodied animats in simulated physical environments](https://www.youtube.com/watch?v=CXTZHHQ7ZiQ&feature=youtu.be) :rainbow: :bowtie:

    1. [Reproduce image with genetic algorithm](https://www.youtube.com/watch?v=iV-hah6xs2A) :bowtie:

  * Deep Learning

    1. ‪[Video Lectures of Deep Learning‪](http://videolectures.net/deeplearning2015_montreal/) ‬:sweat_smile:

    1. [Neural networks class - Université de Sherbrooke](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)

    1. ‪[A Friendly Introduction to Machine Learning‪](https://www.youtube.com/watch?v=IpGxLWOIZy4) ‬:bowtie:

    1. ‪[A friendly introduction to Deep Learning and Neural Networks](https://www.youtube.com/watch?v=BR9h47Jtqyw&t=837s) ‬:bowtie:

    1. ‪[A friendly introduction to Convolutional Neural Networks and Image Recognition](https://www.youtube.com/watch?v=2-Ol7ZB0MmU) ‬:bowtie:

    1. ‪[Deep Learning Demystified](https://www.youtube.com/watch?v=Q9Z20HCPnww&t=225s&list=PLVZqlMpoM6kbaeySxhdtgQPFEC5nV7Faa&index=4) ‬:bowtie:

    1. ‪[How Deep Neural Networks Work](https://www.youtube.com/watch?v=ILsA4nyG7I0&t=1269s&list=PLVZqlMpoM6kbaeySxhdtgQPFEC5nV7Faa&index=1) ‬:bowtie:

    1. ‪[How Convolutional Neural Networks work](https://www.youtube.com/watch?v=FmpDIaiMIeA&t=700s&list=PLVZqlMpoM6kbaeySxhdtgQPFEC5nV7Faa&index=2) :bowtie:

  * Artificial Intelligence

    1. [MIT 6.034 Artificial Intelligence, Fall 2010](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi) - Complete set of course lectures

## Resources

  1. [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)

  1. ‪[QA StackOverflow Machine Learning Algorithms](http://stackoverflow.com/questions/20898300/whats-the-other-major-approach-paradigms-in-machine-learning-besides-baysian-me)

  1. [‪Free dataset for projects](https://www.dataquest.io/blog/free-datasets-for-projects)

  1. [Facial Recognition Database](https://www.kairos.com/blog/166-60-facial-recognition-databases)

  1. [iOS application- Read top articles for your professional skills with @mybridge - Here you can find new articles every day for Data Science and Machine Learning among other things](https://itunes.apple.com/app/id1055459116)

  1. [Machine Learning Resources](http://blog.yhat.com/posts/ML-resources-you-should-know.html)

  1. [Isochrones using the Google Maps Distance Matrix API](http://blog.yhat.com/posts/isochrones-isocronut.html)

  1. [Index of Best AI/Machine Learning Resources](https://hackernoon.com/index-of-best-ai-machine-learning-resources-71ba0c73e34d#.f0vx1erj9)

## Newsletter

  1. [Data Science](https://www.datascienceweekly.org/)

  1. [Data Elixir](https://dataelixir.com/)

  1. [Artificial Intelligence Weekly](http://aiweekly.co/)

  1. [Data Aspirant](http://dataaspirant.com/)

## Tools

  1. [ConvNetJS - Javascript library for training Deep Learning models (Neural Networks) ](http://cs.stanford.edu/people/karpathy/convnetjs/) :sweat_smile:

  1. [RecurrentJS - Deep Recurrent Neural Networks and LSTMs in Javascript](https://github.com/shiffman/recurrentjs) :sweat_smile:

  1. [AIXIjs - JavaScript demo for running General Reinforcement Learning (RL) agents](https://github.com/aslanides/aixijs/) :sweat_smile:

  1. [WORD2VEC](http://technobium.com/find-words-similarity-using-deeplearning4j-word2vec/) :sweat_smile:

  1. [Neuro.js](https://github.com/janhuenermann/neurojs)

  1. [‪Google Chrome Extensión to download all Image of the Google Search](https://chrome.google.com/webstore/detail/fatkun-batch-download-ima/nnjjahlikiabnchcpehcpkdeckfgnohf?hl=es‬) :bowtie: :rainbow:

  1 [Scikit-Learn](http://scikit-learn.org/)

### TensorFlow

  1. [Projector](http://projector.tensorflow.org/) :sweat_smile:

  1. [Magenta](https://github.com/tensorflow/magenta) :rainbow:

  1. [TensorFlow and Flask](https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc#.96tvigb98_), Thanks to @Hebali [basic pipeline, minus TensorFlow plus a very basic placeholder function](

http://www.patrickhebron.com/learning-machines/week8.html)

  1. [Awesome Tensorflow - curated list of TensorFlow tutorials](https://github.com/jtoy/awesome-tensorflow)

### Tensorflow posts

  1. [Big deep learning news: Google Tensorflow chooses Keras](http://www.fast.ai/2017/01/03/keras/)

  1. [Simple end-to-end TensorFlow examples](http://bcomposes.com/2015/11/26/simple-end-to-end-tensorflow-examples/)

  1. [TensorFlow website Getting Started](https://www.tensorflow.org/get_started/get_started):bowtie:

### t-SNE

  1. [t-SNE](https://lvdmaaten.github.io/tsne/) :sweat_smile:

  1. [t-SNE](https://scienceai.github.io/tsne-js/) :sweat_smile:

  1. [An illustrated introduction to the t-SNE algorithm](https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm)

  1. [Visualizing Data Using t-SNE](https://www.youtube.com/watch?v=RJVL80Gg3lA&list=UUtXKDgv1AVoG88PLl8nGXmw) :rainbow: