Table of Contents

Conferences

• AAMAS - International Joint Conference on Autonomous Agents and Multiagent Systems
• IJCAI - International Joint Conference on Artificial Intelligence
• NIPS - Neural Information Processing Systems
• ICLR - International Conference on Learning Representations

Courses

• Neural Networks for Machine Learning
• A.I - MIT
• Convolutional Neural Networks for Visual Recognition - Stanford

Datasets

• CIFAR-10 and CIFAR-100
• Google House Numbers
• MNIST
• Tiny Images
• Fashion-MNIST

Free Books

• Neural Networks and Deep Learning by Michael Nielsen
• Neuraltalk by Andrej Karpathy

Libraries

• Tensorflow
• Caffe
• Torch7
• Theano
• TensorFlow.js - formerly known as deeplearn.js
• MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework

Papers

• Training tricks by YB
• ImageNet Classification with Deep Convolutional Neural Networks
• Using Very Deep Autoencoders for Content Based Image Retrieval
• Learning Deep Architectures for AI
• Neural Networks for Named Entity Recognition

Sample Code

• Deep Learning with Python
• Deep Learning with TensorFlow
• Fundamentals of Deep Learning
• Introduction to Deep Learning Using R

Tutorials

• How to Implement the Backpropagation Algorithm From Scratch In Python
• A Guide to Deep Learning in PyTorch
• A Quick Introduction to Neural Networks
• An Intuitive Explanation of Convolutional Neural Networks
• Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs
• image classifier using convolutional neural network

Videos and Lectures

• Deep Learning, Self-Taught Learning and Unsupervised Feature Learning
• The Unreasonable Effectiveness of Deep Learning
• Deep Learning of Representations
• How Deep Neural Networks Work
• Recent Developments in Deep Learning
• Making Sense of the World with Deep Learning