I am exploring some frontiers in machine learning techniques for gaze / visual behavior classification. I am really interested in how Deep Learning yields sophisticated results for complex tasks. Listed below are some references I used to read :

- What’s the difference between AI, Machine Learning, and Deep Learning ?
- What is deep learning : A Pete Warden article in O’Reilly
- NVidia Deep Learning page
- MIT Press Deep Learning Book
- Deep Learning Dot Net Reading List
- Quora’s : What’s Deep Learning ?
- The Extraordinary Link Between Deep Neural Networks and The Nature of Universe

Useful links from academia :

Some readings in progress :

- Kevin P Murphy’s : Machine Learning – A Probabilistic Perspective
- Rogers and Girolami’s : A First Course in Machine Learning 1st Edition

**List of deep learning resources from across the web
**See the original post here

**Books**

- DeepLearningBook.org by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- NeuralNetworksandDeepLearning.com by Michael Nielsen

**Courses**

- Neural Networks for Machine Learning (2017) by Geoffrey Hinton, University of Toronto
- CS231n Convolutional Neural Networks for Visual Recognition by Andrej Karpathy, Stanford University
- Natural Language Processing with Deep Learning by Christopher Manning and Richard Socher, Stanford University
- Deep Natural Language Processing (2017) by Phil Blunsom, Oxford University and Google DeepMind
- Deep learning (2015) by Nando de Freitas, Oxford University
- Deep Learning for Self-Driving Cars (2017) by Lex Fridman, Massachusetts Institute of Technology
- Deep Learning by Google (on Udacity)

**Blogs**

**Newsletters**

- Weekly newsletter on Deep Learning & AI
- The Wild Week in AI
- Deep Learning Weekly
- Transmission – Self-Driving Car & Deep Learning Newsletter

**Research paper by subfield**

- Reading List « Deep Learning
- The most cited deep learning papers
- Deep Learning papers reading roadmap
- Amund Tveit’s Blog

**Learn deep learning, from novice to advanced, self-paced
**See the original post here

Tutorials have been chosen to maximize learning curve, i.e. learn the most in the shortest amount of time. Tutorials cover topics from basic deep learning all the way to research done within the last 1 year! They cover significantly more material than a typical deep learning course and take lesser time.

**Expected time to completion**: 4-6 weeks (20-30 sessions)

**Pre-requisites**: Calculus (specifically, differentiation)

We’ll cover the following topics:

- Machine Learning basics (what it is, gradient descend)
- Deep Learning basics (what it is, neural networks, computational graphs, back-propagation)
- Convolutional Neural Networks (mostly applied to computer vision)
- Recurrent Neural Networks (mostly applied to natural language processing)
- Generative Adversarial Networks and Variational Auto-Encoders (mostly applied to image generation)
- Deep Reinforcement Learning

*Pro-tip:** If you want to skip sections or tutorials, mark them as completed.*

A Visual Introduction to Machine Learning

A Machine Learning Introductory Tutorial with Examples

Deep Learning Tutorial: Perceptrons to Machine Learning Algorithms

Andrej Karpathy: Yes you should understand backprop

Calculus on Computational Graphs: Backpropagation — colah’s blog

A Beginner’s Guide To Understanding Convolutional Neural Networks – Adit Deshpande

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2 – Adit Deshpande

Conv Nets: A Modular Perspective

Understanding Convolutions — colah’s blog

Visualizing Features from a Convolutional Neural Network

Understanding CNNs Part 3: The 9 Deep Learning Papers You Need To Know About – Adit Deshpande

Neural Network Architectures in Computer Vision

Deconvolution and Checkerboard Artifacts

Breaking Linear Classifiers on ImageNet

Understanding LSTM Networks — colah’s blog

The Unreasonable Effectiveness of Recurrent Neural Networks – Andrej Karpathy

Deep Learning, NLP, and Representations

Deep Learning Research Review: Natural Language Processing – Adit Deshpande

Attention and Augmented Recurrent Neural Networks

Neural Networks, Manifolds, and Topology — colah’s blog

Deep Learning Research Review: Generative Adversarial Nets – Adit Deshpande

Generative Adversial Networks Explained

Variational Autoencoders Explained

What is DRAW (Deep Recurrent Attentive Writer)?

Deep Learning Research Review: Reinforcement Learning

Deep Reinforcement Learning: Pong from Pixels

Paper Summary: Human-Level Control Through Deep Reinforcement Learning

Four Experiments in Handwriting with a Neural Network

Neural Networks, Types, and Functional Programming — colah’s blog