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Deep Learning - All You Need to Know

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Table of Contents

Introduction

The purpose of this project is to introduce a shortcut to developers and researcher for finding useful resources about Deep Learning.

Motivation

There are different motivations for this open source project.

What's the point of this open source project?

There are other repositories similar to this repository that are very comprehensive and useful and to be honest they made me ponder if there is a necessity for this repository!

The point of this repository is that the resources are being targeted. The organization of the resources is such that the user can easily find the things he/she is looking for. We divided the resources to a large number of categories that in the beginning one may have a headache!!! However, if someone knows what is being located, it is very easy to find the most related resources. Even if someone doesn't know what to look for, in the beginning, the general resources have been provided.

Papers

This chapter is associated with the papers published in deep learning.

Models

Convolutional Networks

• Imagenet classification with deep convolutional neural networks : [Paper][Code]

  

• Convolutional Neural Networks for Sentence Classification : [Paper][Code]

  

• Large-scale Video Classification with Convolutional Neural Networks : [Paper][Project Page]

  

• Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks : [Paper]

  

• Deep convolutional neural networks for LVCSR : [Paper]

  

• Face recognition: a convolutional neural-network approach : [Paper]

  

Recurrent Networks

• An empirical exploration of recurrent network architectures : [Paper][Code]

  

• LSTM: A search space odyssey : [Paper][Code]

  

• On the difficulty of training recurrent neural networks : [Paper][Code]

  

• Learning to forget: Continual prediction with LSTM : [Paper]

  

Autoencoders

• Extracting and composing robust features with denoising autoencoders : [Paper]

  

• Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion : [Paper][Code]

  

• Adversarial Autoencoders : [Paper][Code]

  

• Autoencoders, Unsupervised Learning, and Deep Architectures : [Paper]

  

• Reducing the Dimensionality of Data with Neural Networks : [Paper][Code]

  

Generative Models

• Exploiting generative models discriminative classifiers : [Paper]

  

• Semi-supervised Learning with Deep Generative Models : [Paper][Code]

  

• Generative Adversarial Nets : [Paper][Code]

  

• Generalized Denoising Auto-Encoders as Generative Models : [Paper]

  

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks : [Paper][Code]

  

Probabilistic Models

• Stochastic Backpropagation and Approximate Inference in Deep Generative Models : [Paper]

  

• Probabilistic models of cognition: exploring representations and inductive biases : [Paper]

  

• On deep generative models with applications to recognition : [Paper]

  

Core

Optimization

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift : [Paper]

  

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting : [Paper]

  

• Training Very Deep Networks : [Paper]

  

• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification : [Paper]

  

• Large Scale Distributed Deep Networks : [Paper]

  

Representation Learning

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks : [Paper][Code]

  

• Representation Learning: A Review and New Perspectives : [Paper]

  

• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets : [Paper][Code]

  

Understanding and Transfer Learning

• Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks : [Paper]

  

• Distilling the Knowledge in a Neural Network : [Paper]

  

• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition : [Paper][

  

• How transferable are features in deep neural networks? : [Paper][Code]

  

Reinforcement Learning

• Human-level control through deep reinforcement learning : [Paper][Code]

  

• Playing Atari with Deep Reinforcement Learning : [Paper][Code]

  

• Continuous control with deep reinforcement learning : [Paper][Code]

  

• Deep Reinforcement Learning with Double Q-Learning : [Paper][Code]

  

• Dueling Network Architectures for Deep Reinforcement Learning : [Paper][Code]

  

Applications

Image Recognition

• Deep Residual Learning for Image Recognition : [Paper][Code]

  

• Very Deep Convolutional Networks for Large-Scale Image Recognition : [Paper]

  

• Multi-column Deep Neural Networks for Image Classification : [Paper]

  

• DeepID3: Face Recognition with Very Deep Neural Networks : [Paper]

  

• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps : [Paper][Code]

  

• Deep Image: Scaling up Image Recognition : [Paper]

  

• Long-Term Recurrent Convolutional Networks for Visual Recognition and Description : [Paper][Code]

  

• 3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition : [Paper][Code]

  

Object Recognition

• ImageNet Classification with Deep Convolutional Neural Networks : [Paper]

  

• Learning Deep Features for Scene Recognition using Places Database : [Paper]

  

• Scalable Object Detection using Deep Neural Networks : [Paper]

  

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks : [Paper][Code]

  

• OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks : [Paper][Code]

  

• CNN Features Off-the-Shelf: An Astounding Baseline for Recognition : [Paper]

  

• What is the best multi-stage architecture for object recognition? : [Paper]

  

Action Recognition

• Long-Term Recurrent Convolutional Networks for Visual Recognition and Description : [Paper]

  

• Learning Spatiotemporal Features With 3D Convolutional Networks : [Paper][Code]

  

• Describing Videos by Exploiting Temporal Structure : [Paper][Code]

  

• Convolutional Two-Stream Network Fusion for Video Action Recognition : [Paper][Code]

  

• Temporal segment networks: Towards good practices for deep action recognition : [Paper][Code]

  

Caption Generation

• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention : [Paper][Code]

  

• Mind's Eye: A Recurrent Visual Representation for Image Caption Generation : [Paper]

  

• Generative Adversarial Text to Image Synthesis : [Paper][Code]

  

• Deep Visual-Semantic Al60ignments for Generating Image Descriptions : [Paper][Code]

  

• Show and Tell: A Neural Image Caption Generator : [Paper][Code]

  

Natural Language Processing

• Distributed Representations of Words and Phrases and their Compositionality : [Paper][Code]

  

• Efficient Estimation of Word Representations in Vector Space : [Paper][Code]

  

• Sequence to Sequence Learning with Neural Networks : [Paper][Code]

  

• Neural Machine Translation by Jointly Learning to Align and Translate : [Paper][Code]