Machine Learning | Sunu Wibirama

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 :

Useful links from academia :

Some readings in progress :

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

List of deep learning resources from across the web
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Books

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

Courses

Blogs

Newsletters

Research paper by subfield

Learn deep learning, from novice to advanced, self-paced
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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

A Neural Network Playground

Gradient Descend intuition

Why Momentum Really Works

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

Neural Style Explained

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

How to Use t-SNE Effectively

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)?

Colorizing the DRAW Model

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