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):
- π = creative
= beginner- π = intermediate, some pre-requisites
= advanced, many pre-requisites
Table of Contents
Articles & Posts
- A Return to Machine Learning π
- A Visual Introduction to Machine Learning π
- Machine Learning is Fun!
- Deep Reinforcement Learning: Pong from Pixels π
- Inside Libratus, the Poker AI That Out-Bluffed the Best Humans
- Machine Learning in Javascript: Introduction
- Realtime control of sequence generation with character based Long Short Term Memory Recurrent Neural Networks π
- Why is machine learning 'hard'?
- Unreasonable effectiveness of RNNs π
- colah's blog
- βͺMachine Learning Website with many Tutorial of Machine Learningβͺ β¬π
- Beginners tutorial for decision tree implementation πβͺ
- Machine Learning Beginner tutorial Supervised and Unsupervised Learning πβͺ
- Q-Learning Tutorial π
- Big O notation Free Code Camp
- Ray Wenderlich Big O notation
- Interview Cake Big O notation
- Youtube Video Big O notation Derek Banas
- Youtube Video for Big O notation HackerRank
- Random Forest in Python π
- CreativeAI - On the Democratisation & Escalation of Creativity π
- Reducing the Dimensionality of Data with Neural Networks
- Learning Deep Architectures for AI
- Letβs code a Neural Network from scratchβ(Processing) π
- Distill - Demystifying Machine Learning Research
- Machine Learning in Javascript π
- A.I. Experiments from google
- Rohan & Lenny #3: Recurrent Neural Networks & LSTMs π
- Backpropogating an LSTM: A Numerical Example π
- Naive Bayes for Dummies; A Simple Explanation
- Machine Learning Crash Course @ Berkeley
- How to approach almost any ML problem? π
- Technical Notes on ML & AI by Chris Albon π
- Naive Bayes and Text Classification π
- First Contact With TensorFlow π
Books
- Machine Learning for Designers by Patrick Hebron, Accompanying Webcast: Machine learning and the future of design
- Machine Learning Book π
- A first encounter with machine learning
- Natural Language Processing with Python π
- A Brief Introduction to Neural Networks π
Courses
- Machine Learning Crash Course By Google
- Coursera - Machine Learning with TensorFlow on GCP π
- The Neural Aesthetic @ SchoolOfMa, Summer 2016 π
- Machine Learning for Musicians and Artists, Kadenze[Scheduled course] π
- Creative Applications of Deep Learning with TensorFlow, Kadenze[Whole Program] π π
- Coursera - Machine Learning
- Coursera - Neural Networks π
- Practical Deep Learning for Coders
- βͺCourse in Machine Learning
- βͺStanford Course Machine Learning
- Udacity - Machine Learning Engineer[Whole Program] π
- DeepMind - Reinforcement Learning lectures by David Silver
Examples
- A Deep Q Reinforcement Learning Demo
- How to use Q Learning in Video Games Easily π
- K-nearest
- The Infinite Drum Machine π
- Visualizing various ML algorithms π
- Image-to-Image - from lines to cats π
- Recurrent Neural Network Tutorial for Artists π
- Browser Self-Driving Car,Learning to Drive Blog Post
- The Neural Network Zoo (cheat sheet of nn architectures)
- Slice of Machine Learning
Projects
- Bidirectional LSTM for IMDB sentiment classification π
- Land Lines
- nnvis - Topological Visualisation of a Convolutional Neural Network π
- char-rnn A character level language model (a fancy text generator) π π
- Machine Learning Projects
Videos
- Reinforcement Learning
- Evolutionary Algorithms
- Deep Learning
- βͺVideo Lectures of Deep Learningβͺ β¬π
- Neural networks class - UniversitΓ© de Sherbrooke
- βͺA Friendly Introduction to Machine Learningβͺ β¬
- βͺA friendly introduction to Deep Learning and Neural Networks β¬
- βͺA friendly introduction to Convolutional Neural Networks and Image Recognition β¬
- βͺDeep Learning Demystified β¬
- βͺHow Deep Neural Networks Work β¬
- βͺHow Convolutional Neural Networks work
- Artificial Intelligence
- MIT 6.034 Artificial Intelligence, Fall 2010 - Complete set of course lectures
Resources
- Awesome Machine Learning
- βͺQA StackOverflow Machine Learning Algorithms
- βͺFree dataset for projects
- Facial Recognition Database
- 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
- Machine Learning Resources
- Isochrones using the Google Maps Distance Matrix API
- Index of Best AI/Machine Learning Resources
Newsletter
Tools
- ConvNetJS - Javascript library for training Deep Learning models (Neural Networks) π
- RecurrentJS - Deep Recurrent Neural Networks and LSTMs in Javascript π
- AIXIjs - JavaScript demo for running General Reinforcement Learning (RL) agents π
- WORD2VEC π
- Neuro.js
- βͺGoogle Chrome ExtensiΓ³n to download all Image of the Google Search π
1 Scikit-Learn
TensorFlow
- Projector π
- Magenta π
- TensorFlow and Flask, Thanks to @Hebali basic pipeline, minus TensorFlow plus a very basic placeholder function
- Awesome Tensorflow - curated list of TensorFlow tutorials
Tensorflow posts
- Big deep learning news: Google Tensorflow chooses Keras
- Simple end-to-end TensorFlow examples
- TensorFlow website Getting Started