Table of Contents

• Datasets

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

• 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

• Libraries

  • Tensorflow
  • Caffe
  • Torch7
  • Theano

• Free Books

  • Neural Networks and Deep Learning by Michael Nielsen

• Courses

  • Neural Networks for Machine Learning
  • Deep Learning Course
  • A.I - MIT
  • Convolutional Neural Networks for Visual Recognition - Stanford
  • MIT 6.S191 Introduction to Deep Learning

• Videos and Lectures

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

• Tutorials

  • Stochastic Gradient Descent (SGD) with Python
  • 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

• Papers

  • Using Very Deep Autoencoders for Content Based Image Retrieval
  • Learning Deep Architectures for AI
  • Training tricks by YB