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
  • CVPR - IEEE Conference on Computer Vision and Pattern Recognition

• 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
  • Neural networks class

• Datasets

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

• Free Books

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

• Libraries

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

• Papers

  • Using Very Deep Autoencoders for Content Based Image Retrieval
  • Learning Deep Architectures for AI
  • 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
  • Stochastic Gradient Descent (SGD) with 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
  • A Beginner’s Guide to Recurrent Networks and LSTMs
  • Stochastic Gradient Descent (SGD) with Python

• 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
  • Recent Developments in Deep Learning
  • Making Sense of the World with Deep Learning
  • MIT 6.S191 Introduction to Deep Learning