Examples and experiments around ML for upcoming Coding Train videos and ITP course.
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):
- 🌈 = creative
= beginner- 😅 = intermediate, some pre-requisites
= advanced, many pre-requisites
- A Return to Machine Learning 🌈
- A Visual Introduction to Machine Learning 🌈
- Machine Learning is Fun!
- Deep Reinforcement Learning: Pong from Pixels 🌈
- [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)
- Machine Learning in Javascript: Introduction
- Realtime control of sequence generation with character based Long Short Term Memory Recurrent Neural Networks 😅
- Why is machine learning 'hard'?
- Unreasonable effectiveness of RNNs 😅
- The Neural Aesthetic @ SchoolOfMa, Summer 2016 🌈
- Machine Learning for Musicians and Artists, Kadenze[Scheduled course] 🌈
1. Creative Applications of Deep Learning with TensorFlow, Kadenze[Whole Program] 🌈 😅
- Coursera - Machine Learning
- Coursera - Neural Networks 😅
- A Deep Q Reinforcement Learning Demo
- How to use Q Learning in Video Games Easily 🌈
- K-nearest
- The Infinite Drum Machine 🌈
- Visualizing various ML algorithms 🌈
- Bidirectional LSTM for IMDB sentiment classification 😅
- Land Lines
- nnvis - Topological Visualisation of a Convolutional Neural Network 🌈
- char-rnn A character level language model (a fancy text generator) 🌈 😅
- Reinforcement Learning
- Evolutionary Algorithms
- ConvNetJS - Javascript library for training Deep Learning models (Neural Networks) 😅
- RecurrentJS - Deep Recurrent Neural Networks and LSTMs in Javascript 😅
- WORD2VEC 😅