Nvidia Driver Install

https://www.nvidia.com/Download/index.aspx?lang=en-us

After you have downloaded the file NVIDIA-Linux-x86_64-xxx.xx.run, change to the directory containing the downloaded file, and as the root user run the executable:

sh NVIDIA-Linux-x86_64-xxx.xx.run
nvidia-smi

Nvidia Driver Cuda Version Match

https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/

CUDA Download

https://developer.nvidia.com/cuda-toolkit-archive

CUDA Install

https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html

CUDNN Download

https://developer.nvidia.com/rdp/cudnn-archive

CUDNN Install

https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html

Tensorflow CUDA CUDNN Version

https://www.tensorflow.org/install/source

(LeNet-5) Gradient-based learning applied to document recognition

(AlexNet) ImageNet classification with deep convolutional neural networks

(ResNet) Deep residual networks for image recognition

(Inception Network) Going Deeper with Convolutions

Network In Network

DenseNet: Densely Connected Convolutional Networks

A guide to convolution arithmetic for deep learning

Is the deconvolution layer the same as a convolutional layer?

Visualizing and Understanding Convolutional Networks

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Understanding Neural Networks Through Deep Visualization

Learning Deep Features for Discriminative Localization

A guide to receptive field arithmetic for Convolutional Neural Networks

OverFeat: Integrated recognition, localization and detection using convolutional networks

You Only Look Once: Unified real-time object detection

YOLO9000: Better, Faster, Stronger

YOLOv3: An Incremental Improvement

Real-time Object Detection with YOLO, YOLOv2 and now YOLOv3

https://github.com/allanzelener/YAD2K/

https://github.com/thtrieu/darkflow/

https://pjreddie.com/darknet/yolo/

(R-CNN) Rich feature hierarchies for accurate object detection and semantic segmentation

Fast R-CNN

Faster R-CNN: Towards real-time object detection with region proposal networks

R-FCN: Object Detection via Region-based Fully Convolutional Networks

Mask R-CNN

Fully Convolutional Networks for Semantic Segmentation

SSD: Single Shot MultiBox Detector

FaceNet: A Unified Embedding for Face Recognition and Clustering

DeepFace: Closing the gap to human-level performance in face verification

Deep Face Reading

DeepFace Reading

FaceNet

Openface

Keras-OpenFace

DeepFace

A Neural Algorithm of Artistic Style

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

A learned representation for artistic style

Demystifying Neural Style Transfer

Exploring the structure of a real-time, arbitrary neural artistic stylization network

Neural Artistic Style: a Comprehensive Look

Harish Narayanan, Convolutional neural networks for artistic style transfer

Log0, TensorFlow Implementation of "A Neural Algorithm of Artistic Style"

[GRU] On the properties of neural machine translation: Encoder-decoder approaches

[GRU] Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Long short-term memory

Visualizing data using t-SNE

Linguistic regularities in continuous space word representations

A neural probabilistic language model

(Debiasing word embeddings) Man is to computer programmer as woman is to homemaker?

Sequence to sequence learning with neural networks

Learning phrase representations using RNN encoder-decoder for statistical machine translation

Deep captioning with multimodal recurrent neural networks

Show and tell: Neural image caption generator

Deep visual-semantic alignments for generating image descriptions

[Bleu: A method for automatic evaluation of machine translation]

Neural machine translation by jointly learning to align and translate

Show, attend and tell: Neural image caption generation with visual attention

Connectionist Temporal Classification: Labeling unsegmented sequence data with recurrent neural networks

Multiple Object Recognition with Visual Attention

DRAW: A Recurrent Neural Network For Image Generation

Mastering the game of Go without human knowledge

Human Level Control through Deep Reinforcement Learning

Francisco S. Melo: Convergence of Q-learning: a simple proof

Video credits to Two minute papers: Google DeepMind's Deep Q-learning playing Atari Breakout

Human Level Control through Deep Reinforcement Learning

Credits: DeepMind, DQN Breakout

Generative Adversarial Imitation Learning

Trust Region Policy Optimization

Proximal Policy Optimization

Emergent Complexity via multi-agent competition

OpenAI Blog: Competitive self-play

alphago-zero-learning-scratch

Mastering the game of Go without human knowledge

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Unifying Count-Based Exploration and Intrinsic Motivation

Human-level control through deep reinforcement learning

Mastering the Game of Go without Human Knowledge

Koren, Yehuda, Robert Bell, and Chris Volinsky：Matrix factorization techniques for recommender systems

Sedhain, Suvash, et al. AutoRec: Autoencoders meet collaborative filtering

Data Augmentation

Fancy PCA (Data Augmentation) with Scikit-Image

The Bayesian interpretation of weight decay

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

https://rawgit.com/danielkunin/Deeplearning-Visualizations/master/index.html

The Unreasonable Effectiveness of Recurrent Neural Networks

http://mccormickml.com/2018/06/15/applying-word2vec-to-recommenders-and-advertising/

Bjorck N, Gomes C P, Selman B, et al. Understanding batch normalization[C]//Advances in Neural Information Processing Systems. 2018: 7705-7716.

Santurkar S, Tsipras D, Ilyas A, et al. How does batch normalization help optimization?[C]//Advances in Neural Information Processing Systems. 2018: 2488-2498.

https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.py

Understanding AlexNet

Difference between Local Response Normalization and Batch Normalization