{"id":13810365,"url":"https://github.com/CodingTrain/Machine-Learning","last_synced_at":"2025-05-14T10:33:11.026Z","repository":{"id":47537490,"uuid":"82401644","full_name":"CodingTrain/Machine-Learning","owner":"CodingTrain","description":"Examples and experiments around ML for upcoming Coding Train videos","archived":false,"fork":false,"pushed_at":"2021-05-25T04:40:32.000Z","size":316,"stargazers_count":953,"open_issues_count":4,"forks_count":197,"subscribers_count":76,"default_branch":"master","last_synced_at":"2024-08-04T02:07:10.305Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CodingTrain.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-02-18T16:54:58.000Z","updated_at":"2024-07-31T14:31:41.000Z","dependencies_parsed_at":"2022-07-23T10:46:18.886Z","dependency_job_id":null,"html_url":"https://github.com/CodingTrain/Machine-Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CodingTrain%2FMachine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CodingTrain%2FMachine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CodingTrain%2FMachine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CodingTrain%2FMachine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CodingTrain","download_url":"https://codeload.github.com/CodingTrain/Machine-Learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254121128,"owners_count":22018107,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-04T02:00:51.299Z","updated_at":"2025-05-14T10:33:06.011Z","avatar_url":"https://github.com/CodingTrain.png","language":null,"readme":"[![Dreams in the CodingTrain](https://raw.githubusercontent.com/CodingTrain/Machine-Learning/master/codingdream.jpg)](http://thecodingtrain.com/)\n\n# Machine-Learning\nExamples and experiments around ML for upcoming Coding Train videos and ITP course.\n\n# Resource attributes\n\nSince resources across the internet vary in terms of their pre-requisites and general accessibility, it is useful to\ngive 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):\n\n - :rainbow: = creative\n - :bowtie: = beginner\n - :sweat_smile: = intermediate, some pre-requisites\n - :godmode: = advanced, many pre-requisites\n\n# Table of Contents\n\u003c!-- MarkdownTOC depth=4 --\u003e\n- [Articles \u0026 Posts](#articles--posts)\n- [Books](#books)\n- [Courses](#courses)\n- [Examples](#examples)\n- [Projects](#projects)\n- [Videos](#videos)\n- [Resources](#resources)\n- [Newsletter](#newsletter)\n- [Tools](#tools)\n    - [Tensorflow](#tensorflow)\n    - [t-SNE](#t-sne)\n\n\u003c!-- /MarkdownTOC --\u003e\n## Articles \u0026 Posts\n  1. [A Return to Machine Learning](https://medium.com/@kcimc/a-return-to-machine-learning-2de3728558eb#.vlqnbo9yg) :rainbow: :bowtie:\n  1. [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) :rainbow: :bowtie:\n  1. [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471) :bowtie:\n  1. [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/) :rainbow:\n  1. [Inside Libratus, the Poker AI That Out-Bluffed the Best Humans](https://www.wired.com/2017/02/libratus/?imm_mid=0ed017\u0026cmp=em-data-na-na-newsltr_ai_20170206) :bowtie:\n  1. [Machine Learning in Javascript: Introduction](http://burakkanber.com/blog/machine-learning-in-other-languages-introduction/) :bowtie:\n  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:\n  1. [Why is machine learning 'hard'?](http://ai.stanford.edu/~zayd/why-is-machine-learning-hard.html) :bowtie:\n  1. [Unreasonable effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) :sweat_smile:\n  1. [colah's blog](http://colah.github.io/)\n  1. ‪[Machine Learning Website with many Tutorial of Machine Learning‪](https://machinelearningmastery.com/start-here/‬) ‬:rainbow:\n  1. [Beginners tutorial for decision tree implementation](https://www.dezyre.com/data-science-in-r-programming-tutorial/decision-tree-tutorial) :rainbow:‪\n  1. [Machine Learning Beginner tutorial Supervised and Unsupervised Learning](http://dataaspirant.com/2014/09/19/supervised-and-unsupervised-learning/‬) :rainbow:‪\n  1. [Q-Learning Tutorial](http://outlace.com/rlpart3.html) :sweat_smile:\n  1. [Big O notation Free Code Camp](https://medium.freecodecamp.com/time-is-complex-but-priceless-f0abd015063c?source=linkShare-4599aaae9f0b-1489449307) :bowtie:\n  1. [Ray Wenderlich Big O notation](https://github.com/raywenderlich/swift-algorithm-club/blob/master/Big-O%20Notation.markdown) :bowtie:\n  1. [Interview Cake Big O notation](https://www.interviewcake.com/article/java/big-o-notation-time-and-space-complexity) :bowtie:\n  1. [Youtube Video Big O notation Derek Banas](https://m.youtube.com/watch?v=V6mKVRU1evU) :bowtie:\n  1. [Youtube Video for Big O notation HackerRank](https://youtu.be/v4cd1O4zkGw) :bowtie:\n  1. [Random Forest in Python](http://blog.yhat.com/posts/random-forests-in-python.html) :sweat_smile:\n  1. [CreativeAI - On the Democratisation \u0026 Escalation of Creativity](https://medium.com/@creativeai/creativeai-9d4b2346faf3#.8oaibcklb) :rainbow: :bowtie:\n  1. [Reducing the Dimensionality of Data with Neural Networks](https://www.cs.toronto.edu/~hinton/science.pdf)\n  1. [Learning Deep Architectures for AI](https://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf)\n  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:\n  1. [Distill - Demystifying Machine Learning Research](http://distill.pub/)\n  1. [Machine Learning in Javascript](http://burakkanber.com/tag/ml-in-js/) :sweat_smile:\n  1. [A.I. Experiments from google](https://aiexperiments.withgoogle.com/)    \n  1. [Rohan \u0026 Lenny #3: Recurrent Neural Networks \u0026 LSTMs](https://ayearofai.com/rohan-lenny-3-recurrent-neural-networks-10300100899b) :sweat_smile:\n  1. [Backpropogating an LSTM: A Numerical Example](https://medium.com/@aidangomez/let-s-do-this-f9b699de31d9) :sweat_smile:\n  1. [Naive Bayes for Dummies; A Simple Explanation](http://blog.aylien.com/naive-bayes-for-dummies-a-simple-explanation/) :bowtie:\n  1. [Machine Learning Crash Course @ Berkeley](https://ml.berkeley.edu/blog/tutorials/) :bowtie: :godmode:\n  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:\n  1. [Technical Notes on ML \u0026 AI by Chris Albon](https://chrisalbon.com/#machine_learning) :bowtie: :sweat_smile:\n  1. [Naive Bayes and Text Classification](https://sebastianraschka.com/Articles/2014_naive_bayes_1.html) :sweat_smile:\n  1. [First Contact With TensorFlow](https://torres.ai/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/) :sweat_smile:\n\n## Books\n  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)\n  1. [Machine Learning Book](https://machinelearningmastery.com/master-machine-learning-algorithms/) :rainbow:\n  1. [A first encounter with machine learning](https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf) :bowtie:\n  1. [Natural Language Processing with Python](https://www.nltk.org/book/) :sweat_smile: :bowtie:\n  1. [A Brief Introduction to Neural Networks](http://www.dkriesel.com/en/science/neural_networks) :sweat_smile:\n\n\n## Courses\n  1. [Machine Learning Crash Course By Google](https://developers.google.com/machine-learning/crash-course/) :bowtie:\n  2. [Coursera - Machine Learning with TensorFlow on GCP](https://www.coursera.org/specializations/machine-learning-tensorflow-gcp?action=enroll) :sweat_smile:\n  3. [The Neural Aesthetic @ SchoolOfMa, Summer 2016](https://ml4a.github.io/classes/neural-aesthetic/) :rainbow: :bowtie:\n  4. [Machine Learning for Musicians and Artists, Kadenze](https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists-i)[Scheduled course] :rainbow: :bowtie:\n  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:\n  6. [Coursera - Machine Learning](https://www.coursera.org/learn/machine-learning) :bowtie:\n  7. [Coursera - Neural Networks](https://www.coursera.org/learn/neural-networks-deep-learning) :sweat_smile:\n  8. [Practical Deep Learning for Coders](http://www.fast.ai/2017/02/24/captions-and-notes/) :bowtie:\n  9. [‪Course in Machine Learning](http://ciml.info/?utm_source=mybridge\u0026utm_medium=ios\u0026utm_campaign=read_more‬)\n  10. [‪Stanford Course Machine Learning](http://cs229.stanford.edu/syllabus.html)\n  11. [Udacity - Machine Learning Engineer](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009)[Whole Program] :sweat_smile:\n  12. [DeepMind - Reinforcement Learning lectures by David Silver](https://www.youtube.com/playlist?list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT)\n\n## Examples\n  1. [A Deep Q Reinforcement Learning Demo](http://projects.rajivshah.com/rldemo/) :bowtie:\n  1. [How to use Q Learning in Video Games Easily](https://github.com/llSourcell/q_learning_demo) :rainbow: :bowtie:\n  1. [K-nearest](https://twitter.com/MaximilianLloyd/status/814942799351185408) :bowtie:\n  1. [The Infinite Drum Machine](https://aiexperiments.withgoogle.com/drum-machine/view/) :rainbow: :bowtie:\n  1. [Visualizing various ML algorithms](https://kwichmann.github.io/ml_sandbox/) :rainbow: :bowtie:\n  1. [Image-to-Image - from lines to cats](http://affinelayer.com/pixsrv/) :rainbow:\n  2. [Recurrent Neural Network Tutorial for Artists](http://blog.otoro.net/2017/01/01/recurrent-neural-network-artist/) :rainbow:\n  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)\n  1. [The Neural Network Zoo (cheat sheet of nn architectures)](http://www.asimovinstitute.org/neural-network-zoo/)\n  1. [Slice of Machine Learning](https://sliceofml.withgoogle.com/#/)\n\n## Projects\n  1. [Bidirectional LSTM for IMDB sentiment classification](https://transcranial.github.io/keras-js/#/imdb-bidirectional-lstm) :sweat_smile:\n  1. [Land Lines](https://medium.com/@zachlieberman/land-lines-e1f88c745847#.1157xmhw8)\n  1. [nnvis - Topological Visualisation of a Convolutional Neural Network](http://terencebroad.com/convnetvis/vis.html) :rainbow: :bowtie:\n  1. [char-rnn A character level language model (a fancy text generator)](https://github.com/karpathy/char-rnn) :rainbow: :sweat_smile:\n  1. [Machine Learning Projects](http://blog.yhat.com/posts/ML-to-watch.html)\n\n## Videos\n  * Reinforcement Learning\n    1. [Artificial Intelligence in Google's Dinosaur (English Sub)](https://www.youtube.com/watch?v=P7XHzqZjXQs) :bowtie:\n    1. [How to use Q Learning in Video Games Easily](https://www.youtube.com/watch?v=A5eihauRQvo\u0026feature=youtu.be) :bowtie:\n  * Evolutionary Algorithms\n    1. [Evolving Swimming Soft-Bodied Creatures](https://www.youtube.com/watch?v=4ZqdvYrZ3ro) :rainbow: :bowtie:\n    1. [Harnessing evolutionary creativity: evolving soft-bodied animats in simulated physical environments](https://www.youtube.com/watch?v=CXTZHHQ7ZiQ\u0026feature=youtu.be) :rainbow: :bowtie:\n    1. [Reproduce image with genetic algorithm](https://www.youtube.com/watch?v=iV-hah6xs2A) :bowtie:\n  * Deep Learning\n    1. ‪[Video Lectures of Deep Learning‪](http://videolectures.net/deeplearning2015_montreal/) ‬:sweat_smile:\n    1. [Neural networks class - Université de Sherbrooke](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)\n    1. ‪[A Friendly Introduction to Machine Learning‪](https://www.youtube.com/watch?v=IpGxLWOIZy4) ‬:bowtie:\n    1. ‪[A friendly introduction to Deep Learning and Neural Networks](https://www.youtube.com/watch?v=BR9h47Jtqyw\u0026t=837s) ‬:bowtie:\n    1. ‪[A friendly introduction to Convolutional Neural Networks and Image Recognition](https://www.youtube.com/watch?v=2-Ol7ZB0MmU) ‬:bowtie:\n    1. ‪[Deep Learning Demystified](https://www.youtube.com/watch?v=Q9Z20HCPnww\u0026t=225s\u0026list=PLVZqlMpoM6kbaeySxhdtgQPFEC5nV7Faa\u0026index=4) ‬:bowtie:\n    1. ‪[How Deep Neural Networks Work](https://www.youtube.com/watch?v=ILsA4nyG7I0\u0026t=1269s\u0026list=PLVZqlMpoM6kbaeySxhdtgQPFEC5nV7Faa\u0026index=1) ‬:bowtie:\n    1. ‪[How Convolutional Neural Networks work](https://www.youtube.com/watch?v=FmpDIaiMIeA\u0026t=700s\u0026list=PLVZqlMpoM6kbaeySxhdtgQPFEC5nV7Faa\u0026index=2) :bowtie:\n  * Artificial Intelligence\n    1. [MIT 6.034 Artificial Intelligence, Fall 2010](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi) - Complete set of course lectures\n\n## Resources\n  1. [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)\n  1. ‪[QA StackOverflow Machine Learning Algorithms](http://stackoverflow.com/questions/20898300/whats-the-other-major-approach-paradigms-in-machine-learning-besides-baysian-me)\n  1. [‪Free dataset for projects](https://www.dataquest.io/blog/free-datasets-for-projects)\n  1. [Facial Recognition Database](https://www.kairos.com/blog/166-60-facial-recognition-databases)\n  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)\n  1. [Machine Learning Resources](http://blog.yhat.com/posts/ML-resources-you-should-know.html)\n  1. [Isochrones using the Google Maps Distance Matrix API](http://blog.yhat.com/posts/isochrones-isocronut.html)\n  1. [Index of Best AI/Machine Learning Resources](https://hackernoon.com/index-of-best-ai-machine-learning-resources-71ba0c73e34d#.f0vx1erj9)\n\n## Newsletter\n  1. [Data Science](https://www.datascienceweekly.org/)\n  1. [Data Elixir](https://dataelixir.com/)\n  1. [Artificial Intelligence Weekly](http://aiweekly.co/)\n  1. [Data Aspirant](http://dataaspirant.com/)\n\n## Tools\n  1. [ConvNetJS - Javascript library for training Deep Learning models (Neural Networks) ](http://cs.stanford.edu/people/karpathy/convnetjs/) :sweat_smile:\n  1. [RecurrentJS - Deep Recurrent Neural Networks and LSTMs in Javascript](https://github.com/shiffman/recurrentjs) :sweat_smile:\n  1. [AIXIjs - JavaScript demo for running General Reinforcement Learning (RL) agents](https://github.com/aslanides/aixijs/) :sweat_smile:\n  1. [WORD2VEC](http://technobium.com/find-words-similarity-using-deeplearning4j-word2vec/) :sweat_smile:\n  1. [Neuro.js](https://github.com/janhuenermann/neurojs)\n  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:\n  1 [Scikit-Learn](http://scikit-learn.org/)\n\n### TensorFlow\n  1. [Projector](http://projector.tensorflow.org/) :sweat_smile:\n  1. [Magenta](https://github.com/tensorflow/magenta) :rainbow:\n  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](\nhttp://www.patrickhebron.com/learning-machines/week8.html)\n  1. [Awesome Tensorflow - curated list of TensorFlow tutorials](https://github.com/jtoy/awesome-tensorflow)\n\n### Tensorflow posts\n  1. [Big deep learning news: Google Tensorflow chooses Keras](http://www.fast.ai/2017/01/03/keras/)\n  1. [Simple end-to-end TensorFlow examples](http://bcomposes.com/2015/11/26/simple-end-to-end-tensorflow-examples/)\n  1. [TensorFlow website Getting Started](https://www.tensorflow.org/get_started/get_started):bowtie:\n\n### t-SNE\n  1. [t-SNE](https://lvdmaaten.github.io/tsne/) :sweat_smile:\n  1. [t-SNE](https://scienceai.github.io/tsne-js/) :sweat_smile:\n  1. [An illustrated introduction to the t-SNE algorithm](https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm)\n  1. [Visualizing Data Using t-SNE](https://www.youtube.com/watch?v=RJVL80Gg3lA\u0026list=UUtXKDgv1AVoG88PLl8nGXmw) :rainbow:\n","funding_links":[],"categories":["Others","Machine learning • Computer Vision • AI","Machine learning • Computer Vision • Ai"],"sub_categories":["Other"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCodingTrain%2FMachine-Learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FCodingTrain%2FMachine-Learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCodingTrain%2FMachine-Learning/lists"}