Natural Language Processing

Tutorials

Practical Neural Networks for NLP

Structured Neural Networks for NLP: From Idea to Code

Understanding Deep Learning Models in NLP

http://nlp.yvespeirsman.be/blog/understanding-deeplearning-models-nlp/

Deep learning for natural language processing, Part 1

https://softwaremill.com/deep-learning-for-nlp/

Neural Models

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Visualizing and Understanding Neural Models in NLP

Character-Aware Neural Language Models

Skip-Thought Vectors

A Primer on Neural Network Models for Natural Language Processing

Character-aware Neural Language Models

Neural Variational Inference for Text Processing

Sequence to Sequence Learning

Generating Text with Deep Reinforcement Learning

MUSIO: A Deep Learning based Chatbot Getting Smarter

Translation

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

Neural Machine Translation by Jointly Learning to Align and Translate

Multi-Source Neural Translation

Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism

Modeling Coverage for Neural Machine Translation

A Character-level Decoder without Explicit Segmentation for Neural Machine Translation

NEMATUS: Attention-based encoder-decoder model for neural machine translation

Variational Neural Machine Translation

Neural Network Translation Models for Grammatical Error Correction

Linguistic Input Features Improve Neural Machine Translation

Sequence-Level Knowledge Distillation

Neural Machine Translation: Breaking the Performance Plateau

Tips on Building Neural Machine Translation Systems

Semi-Supervised Learning for Neural Machine Translation

EUREKA-MangoNMT: A C++ toolkit for neural machine translation for CPU

Deep Character-Level Neural Machine Translation

Neural Machine Translation Implementations

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Learning to Translate in Real-time with Neural Machine Translation

Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions

Fully Character-Level Neural Machine Translation without Explicit Segmentation

Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

Neural Machine Translation in Linear Time

Neural Machine Translation with Reconstruction

A Convolutional Encoder Model for Neural Machine Translation

Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

MXNMT: MXNet based Neural Machine Translation

Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

Massive Exploration of Neural Machine Translation Architectures

Depthwise Separable Convolutions for Neural Machine Translation

Deep Architectures for Neural Machine Translation

Marian: Fast Neural Machine Translation in C++

Summarization

Extraction of Salient Sentences from Labelled Documents

A Neural Attention Model for Abstractive Sentence Summarization

A Convolutional Attention Network for Extreme Summarization of Source Code

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

textsum: Text summarization with TensorFlow

How to Run Text Summarization with TensorFlow

Reading Comprehension

Text Comprehension with the Attention Sum Reader Network

Text Understanding with the Attention Sum Reader Network

A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

Consensus Attention-based Neural Networks for Chinese Reading Comprehension

Separating Answers from Queries for Neural Reading Comprehension

Attention-over-Attention Neural Networks for Reading Comprehension

Teaching Machines to Read and Comprehend CNN News and Children Books using Torch

Reasoning with Memory Augmented Neural Networks for Language Comprehension

Bidirectional Attention Flow: Bidirectional Attention Flow for Machine Comprehension

NewsQA: A Machine Comprehension Dataset

Gated-Attention Readers for Text Comprehension

Get To The Point: Summarization with Pointer-Generator Networks

Language Understanding

Recurrent Neural Networks with External Memory for Language Understanding

Neural Semantic Encoders

Neural Tree Indexers for Text Understanding

Better Text Understanding Through Image-To-Text Transfer

Text Classification

Convolutional Neural Networks for Sentence Classification

Recurrent Convolutional Neural Networks for Text Classification

Character-level Convolutional Networks for Text Classification

A C-LSTM Neural Network for Text Classification

Rationale-Augmented Convolutional Neural Networks for Text Classification

Text classification using DIGITS and Torch7

Recurrent Neural Network for Text Classification with Multi-Task Learning

Deep Multi-Task Learning with Shared Memory

Virtual Adversarial Training for Semi-Supervised Text Classification

Adversarial Training Methods for Semi-Supervised Text Classification

Sentence Convolution Code in Torch: Text classification using a convolutional neural network

Bag of Tricks for Efficient Text Classification

Actionable and Political Text Classification using Word Embeddings and LSTM

Implementing a CNN for Text Classification in TensorFlow

fancy-cnn: Multiparadigm Sequential Convolutional Neural Networks for text classification

Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

Tweet Classification using RNN and CNN

Hierarchical Attention Networks for Document Classification

AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification

Generative and Discriminative Text Classification with Recurrent Neural Networks

Adversarial Multi-task Learning for Text Classification

Deep Text Classification Can be Fooled

Deep neural network framework for multi-label text classification

Multi-Task Label Embedding for Text Classification

Text Clustering

Self-Taught Convolutional Neural Networks for Short Text Clustering

Alignment

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Dialog

Visual Dialog

Papers, code and data from FAIR for various memory-augmented nets with application to text understanding and dialogue.

Neural Emoji Recommendation in Dialogue Systems

Memory Networks

Neural Turing Machines

Memory Networks

End-To-End Memory Networks

Reinforcement Learning Neural Turing Machines - Revised


Learning to Transduce with Unbounded Memory

How to Code and Understand DeepMind’s Neural Stack Machine


Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)

Structured Memory for Neural Turing Machines

Dynamic Memory Networks for Visual and Textual Question Answering

Neural GPUs Learn Algorithms

Hierarchical Memory Networks

Convolutional Residual Memory Networks

NTM-Lasagne: A Library for Neural Turing Machines in Lasagne

Evolving Neural Turing Machines for Reward-based Learning

Hierarchical Memory Networks for Answer Selection on Unknown Words

Gated End-to-End Memory Networks

Can Active Memory Replace Attention?

A Taxonomy for Neural Memory Networks

Papers

Globally Normalized Transition-Based Neural Networks

A Decomposable Attention Model for Natural Language Inference

Improving Recurrent Neural Networks For Sequence Labelling

Recurrent Memory Networks for Language Modeling

Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

Learning text representation using recurrent convolutional neural network with highway layers

Ask the GRU: Multi-task Learning for Deep Text Recommendations

From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning

Visualizing Linguistic Shift

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Deep Learning applied to NLP

https://arxiv.org/abs/1703.03091

Attention Is All You Need

Recent Trends in Deep Learning Based Natural Language Processing

HotFlip: White-Box Adversarial Examples for NLP

No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

Interesting Applications

Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning

sk_p: a neural program corrector for MOOCs

Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

emoji2vec: Learning Emoji Representations from their Description

Inside-Outside and Forward-Backward Algorithms Are Just Backprop (Tutorial Paper)

Cruciform: Solving Crosswords with Natural Language Processing

Smart Reply: Automated Response Suggestion for Email

Deep Learning for RegEx

Learning Python Code Suggestion with a Sparse Pointer Network

End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

https://arxiv.org/abs/1703.02458

Convolutional Sequence to Sequence Learning

DeepFix: Fixing Common C Language Errors by Deep Learning

Hierarchically-Attentive RNN for Album Summarization and Storytelling

Project

TheanoLM - An Extensible Toolkit for Neural Network Language Modeling

NLP-Caffe: natural language processing with Caffe

DL4NLP: Deep Learning for Natural Language Processing

Combining CNN and RNN for spoken language identification

Character-Aware Neural Language Models: LSTM language model with CNN over characters in TensorFlow

Neural Relation Extraction with Selective Attention over Instances

deep-simplification: Text simplification using RNNs

lamtram: A toolkit for language and translation modeling using neural networks

Lango: Language Lego

Sequence-to-Sequence Learning with Attentional Neural Networks

harvardnlp code

Seq2seq: Sequence to Sequence Learning with Keras

debug seq2seq

Recurrent & convolutional neural network modules

Datasets

Datasets for Natural Language Processing

Blogs

How to read: Character level deep learning

Heavy Metal and Natural Language Processing

Sequence To Sequence Attention Models In PyCNN

https://talbaumel.github.io/Neural+Attention+Mechanism.html

Source Code Classification Using Deep Learning

http://blog.aylien.com/source-code-classification-using-deep-learning/

My Process for Learning Natural Language Processing with Deep Learning

https://medium.com/@MichaelTeifel/my-process-for-learning-natural-language-processing-with-deep-learning-bd0a64a36086

Convolutional Methods for Text

https://medium.com/@TalPerry/convolutional-methods-for-text-d5260fd5675f

Word2Vec

Word2Vec Tutorial - The Skip-Gram Model

http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/

Word2Vec Tutorial Part 2 - Negative Sampling

http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/

Word2Vec Resources

http://mccormickml.com/2016/04/27/word2vec-resources/

Demos

AskImage.org - Deep Learning for Answering Questions about Images

Talks / Videos

Navigating Natural Language Using Reinforcement Learning

Resources

So, you need to understand language data? Open-source NLP software can help!

Curated list of resources on building bots

Notes for deep learning on NLP

https://medium.com/@frank_chung/notes-for-deep-learning-on-nlp-94ddfcb45723#.iouo0v7m7

Published: 09 Oct 2015

Generative Adversarial Networks

Generative Adversarial Networks

Generative Adversarial Nets

Adversarial Feature Learning

Generative Adversarial Networks

Adversarial Examples and Adversarial Training

How to Train a GAN? Tips and tricks to make GANs work

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Learning Interpretable Latent Representations with InfoGAN: A tutorial on implementing InfoGAN in Tensorflow

Coupled Generative Adversarial Networks

Energy-based Generative Adversarial Network

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

Connecting Generative Adversarial Networks and Actor-Critic Methods

Generative Adversarial Nets from a Density Ratio Estimation Perspective

Unrolled Generative Adversarial Networks

Generative Adversarial Networks as Variational Training of Energy Based Models

Multi-class Generative Adversarial Networks with the L2 Loss Function

Least Squares Generative Adversarial Networks

Inverting The Generator Of A Generative Adversarial Networ

ml4a-invisible-cities

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

Associative Adversarial Networks

Temporal Generative Adversarial Nets

Handwriting Profiling using Generative Adversarial Networks

  • intro: Accepted at The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17 Student Abstract and Poster Program)
  • arxiv: https://arxiv.org/abs/1611.08789

C-RNN-GAN: Continuous recurrent neural networks with adversarial training

Ensembles of Generative Adversarial Networks

Improved generator objectives for GANs

Stacked Generative Adversarial Networks

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

AdaGAN: Boosting Generative Models

Towards Principled Methods for Training Generative Adversarial Networks

Wasserstein GAN

Improved Training of Wasserstein GANs

On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks

On the Effects of Batch and Weight Normalization in Generative Adversarial Networks

Controllable Generative Adversarial Network

Generative Adversarial Networks: An Overview

  • intro: Imperial College London & Victoria University of Wellington & University of Montreal & Cortexica Vision Systems Ltd
  • intro: IEEE Signal Processing Magazine Special Issue on Deep Learning for Visual Understanding
  • arxiv: https://arxiv.org/abs/1710.07035

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

https://arxiv.org/abs/1711.03213

Spectral Normalization for Generative Adversarial Networks

https://openreview.net/forum?id=B1QRgziT-

Are GANs Created Equal? A Large-Scale Study

GAGAN: Geometry-Aware Generative Adverserial Networks

https://arxiv.org/abs/1712.00684

CycleGAN: a Master of Steganography

PacGAN: The power of two samples in generative adversarial networks

ComboGAN: Unrestrained Scalability for Image Domain Translation

Decoupled Learning for Conditional Adversarial Networks

https://arxiv.org/abs/1801.06790

No Modes left behind: Capturing the data distribution effectively using GANs

Improving GAN Training via Binarized Representation Entropy (BRE) Regularization

On GANs and GMMs

https://arxiv.org/abs/1805.12462

The Unusual Effectiveness of Averaging in GAN Training

https://arxiv.org/abs/1806.04498

Image-to-Image Translation

Pix2Pix

Image-to-Image Translation with Conditional Adversarial Networks

Remastering Classic Films in Tensorflow with Pix2Pix

Image-to-Image Translation in Tensorflow

webcam pix2pix

https://github.com/memo/webcam-pix2pix-tensorflow


Unsupervised Image-to-Image Translation with Generative Adversarial Networks

Unsupervised Image-to-Image Translation Networks

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

CycleGAN and pix2pix in PyTorch

Perceptual Adversarial Networks for Image-to-Image Transformation

https://arxiv.org/abs/1706.09138

XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings

In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks

https://arxiv.org/abs/1711.09334

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation

https://arxiv.org/abs/1711.09554

Toward Multimodal Image-to-Image Translation

Face Translation between Images and Videos using Identity-aware CycleGAN

https://arxiv.org/abs/1712.00971

Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders

https://arxiv.org/abs/1712.02050

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

On the Effectiveness of Least Squares Generative Adversarial Networks

https://arxiv.org/abs/1712.06391

GANs for Limited Labeled Data

Defending Against Adversarial Examples

Conditional Image-to-Image Translation

XOGAN: One-to-Many Unsupervised Image-to-Image Translation

https://arxiv.org/abs/1805.07277

Unsupervised Attention-guided Image to Image Translation

https://arxiv.org/abs/1806.02311

Exemplar Guided Unsupervised Image-to-Image Translation

https://arxiv.org/abs/1805.11145

Projects

Generative Adversarial Networks with Keras

Generative Adversarial Network Demo for Fresh Machine Learning #2

TextGAN: A generative adversarial network for text generation, written in TensorFlow.

cleverhans v0.1: an adversarial machine learning library

Deep Convolutional Variational Autoencoder w/ Adversarial Network

A versatile GAN(generative adversarial network) implementation. Focused on scalability and ease-of-use.

AdaGAN: Boosting Generative Models

TensorFlow-GAN (TFGAN)

Blogs

Generative Adversial Networks Explained

Generative Adversarial Autoencoders in Theano

An introduction to Generative Adversarial Networks (with code in TensorFlow)

Difficulties training a Generative Adversarial Network

Are Energy-Based GANs any more energy-based than normal GANs?

http://www.inference.vc/are-energy-based-gans-actually-energy-based/

Generative Adversarial Networks Explained with a Classic Spongebob Squarepants Episode: Plus a Tensorflow tutorial for implementing your own GAN

Deep Learning Research Review Week 1: Generative Adversarial Nets

Stability of Generative Adversarial Networks

Instance Noise: A trick for stabilising GAN training

Generating Fine Art in 300 Lines of Code

Talks / Videos

Generative Adversarial Network visualization

Resources

The GAN Zoo

AdversarialNetsPapers: The classical Papers about adversial nets

GAN Timeline

Published: 09 Oct 2015

Fun With Deep Learning

Painting

Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

Neural Art

A Neural Algorithm of Artistic Style

Image Style Transfer Using Convolutional Neural Networks

Artificial Startup Style: Neural art about startup fashion

From Pixels to Paragraphs: How artistic experiments with deep learning guard us from hype

Experiments with style transfer

Style Transfer for Headshot Portraits (SIGGRAPH 2014)

Teaching recurrent Neural Networks about Monet

Content Aware Neural Style Transfer

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

Stylenet: Neural Network with Style Synthesis

Ostagram

  • intro: This program presents web-service for algorithm combining the content of one image with the style of another image using convolutional neural networks
  • github: https://github.com/SergeyMorugin/ostagram

Exploring the Neural Algorithm of Artistic Style

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

Image transformation networks with fancy loss functions

Improving the Neural Algorithm of Artistic Style

CubistMirror: an openframeworks app which repeatedly applies real-time style transfer on a webcam

Transfer Style But Not Color

neural-art-mini: Lightweight version of mxnet neural art implementation

Preserving Color in Neural Artistic Style Transfer

End to End Neural Art with Generative Models

Neural Style Explained

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

Learning Typographic Style

Instance Normalization: The Missing Ingredient for Fast Stylization

Painting style transfer for head portraits using convolutional neural networks

Style-Transfer via Texture-Synthesis

neural-style-tf: TensorFlow implementation of Neural Style

Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity

  • intro: In Proceedings of the 7th International Conference on Computational Creativity. Palo Alto: Association for the Advancement of Artificial Intelligence (AAAI) Press (2016)
  • arxiv: https://arxiv.org/abs/1610.02478

A Learned Representation For Artistic Style

How to Fake It As an Artist with Docker, AWS and Deep Learning

Multistyle Pastiche Generator

Fast Style Transfer in TensorFlow

Neural Style Transfer For Chinese Fonts

Neural Style Representations and the Large-Scale Classification of Artistic Style

Controlling Perceptual Factors in Neural Style Transfer

Awesome Typography: Statistics-Based Text Effects Transfer

Fast Patch-based Style Transfer of Arbitrary Style

Demystifying Neural Style Transfer

Son of Zorn’s Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation

Bringing Impressionism to Life with Neural Style Transfer in Come Swim

  • intro: a case study of how Neural Style Transfer can be used in a movie production context
  • keywords: Kristen Stewart !
  • arxiv: https://arxiv.org/abs/1701.04928

Pytorch tutorials for Neural Style transfert

Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses

Arbitrary Style Transfer In Real-Time With Adaptive Instance Normalization

Picking an optimizer for Style Transfer

Multi-style Generative Network for Real-time Transfer

https://arxiv.org/abs/1703.06953

Deep Photo Style Transfer

Lightweight Neural Style on Pytorch

https://github.com/lizeng614/SqueezeNet-Neural-Style-Pytorch

StyleBank: An Explicit Representation for Neural Image Style Transfer

How to Make an Image More Memorable? A Deep Style Transfer Approach

Visual Attribute Transfer through Deep Image Analogy

Characterizing and Improving Stability in Neural Style Transfer

https://arxiv.org/abs/1705.02092

Towards Metamerism via Foveated Style Transfer

https://arxiv.org/abs/1705.10041

Style Transfer for Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN

https://arxiv.org/abs/1706.03319

Meta Networks for Neural Style Transfer

Neural Color Transfer between Images

Improved Style Transfer by Respecting Inter-layer Correlations

https://arxiv.org/abs/1801.01933

Face Destylization

https://arxiv.org/abs/1802.01237

Unsupervised Typography Transfer

https://arxiv.org/abs/1802.02595

Stereoscopic Neural Style Transfer

Arbitrary Style Transfer with Deep Feature Reshuffle

https://arxiv.org/abs/1805.04103

Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration

Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer

https://arxiv.org/abs/1805.09987

A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer

https://arxiv.org/abs/1806.00868

TernausNetV2: Fully Convolutional Network for Instance Segmentation

Neural Art On Audio

MSc AI Project on generative deep networks and neural style transfer for audio

Neural Song Style

Time Domain Neural Audio Style Transfer

Neural Art On Video

neural-style-video

Instructions for making a Neural-Style movie

Artistic style transfer for videos

Artistic style transfer for videos and spherical images

https://arxiv.org/abs/1708.04538

How Deep Learning Can Paint Videos in the Style of Art’s Great Masters

DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies

Coherent Online Video Style Transfer

https://arxiv.org/abs/1703.09211

Laplacian-Steered Neural Style Transfer

Real-Time Neural Style Transfer for Videos

Multi-Content GAN for Few-Shot Font Style Transfer

https://arxiv.org/abs/1712.00516

Neural Doodle

Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

Neural Doodle

Faster neural doodle

Feed-forward neural doodle

neural image analogies: Generate image analogies using neural matching and blending

Neural doodle with Keras

https://github.com/fchollet/keras/blob/master/examples/neural_doodle.py

Deep Dreams

deepdream

cnn-vis: Use CNNs to generate images

bat-country: A lightweight, extendible, easy to use Python package for deep dreaming and image generation with Caffe and CNNs

DeepDreaming with TensorFlow

deepdraw

Understanding Deep Dreams

Generating Deep Dreams

Audio Deepdream: Optimizing Raw Audio With Convolutional Networks

Emoji

Brewing EmojiNet

Image2Emoji: Zero-shot Emoji Prediction for Visual Media

Teaching Robots to Feel: Emoji & Deep Learning 👾 💭 💕

Text input with relevant emoji sorted with deeplearning

Sketch

Sketch-a-Net that Beats Humans

How Do Humans Sketch Objects?

Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup (SIGGRAPH 2016)

Convolutional Sketch Inversion

Sketch Me That Shoe (CVPR 2016)

Mastering Sketching: Adversarial Augmentation for Structured Prediction

SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

Image Stylization

Automatic Portrait Segmentation for Image Stylization

Transfiguring Portraits

Stylize Aesthetic QR Code

Image Colorization

Deep Colorization

Learning Large-Scale Automatic Image Colorization

Learning Representations for Automatic Colorization

Colorful Image Colorization

Colorising Black & White Photos using Deep Learning

https://hackernoon.com/colorising-black-white-photos-using-deep-learning-4da22a05f531


Automatic Colorization (Tensorflow + VGG)

colornet: Neural Network to colorize grayscale images

https://github.com/pavelgonchar/colornet

Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification (SIGGRAPH 2016)

Convolutional autoencoder to colorize greyscale images

Image-Color: A deep learning approach to colorizing images

Creating an artificial artist: Color your photos using Neural Networks

Paints Chainer: line drawing colorization using chainer

Unsupervised Diverse Colorization via Generative Adversarial Networks

(DE)^2 CO: Deep Depth Colorization

https://arxiv.org/abs/1703.10881

A Neural Representation of Sketch Drawings

Real-Time User-Guided Image Colorization with Learned Deep Priors

PixColor: Pixel Recursive Colorization

cGAN-based Manga Colorization Using a Single Training Image

Interactive Deep Colorization With Simultaneous Global and Local Inputs

https://arxiv.org/abs/1801.09083

Image Colorization with Generative Adversarial Networks

https://arxiv.org/abs/1803.05400

Learning to Color from Language

Sounds

Visually Indicated Sounds

Music

GRUV: Algorithmic Music Generation using Recurrent Neural Networks

DeepHear - Composing and harmonizing music with neural networks

Using AutoHarp and a Character-Based RNN to Create MIDI Drum Loops

Musical Audio Synthesis Using Autoencoding Neural Nets

sound-rnn: Generating sound using recurrent neural networks

Using LSTM Recurrent Neural Networks for Music Generation (Project for AI Prac Fall 2015 at Cornell)

Visually Indicated Sounds (MIT. 2015)

Training a Recurrent Neural Network to Compose Music

LSTM Realbook

LSTMetallica: Generation drum tracks by learning the drum tracks of 60 Metallica songs

deepjazz: Deep learning driven jazz generation using Keras & Theano!

Magenta: Music and Art Generation with Machine Intelligence

Music Transcription with Convolutional Neural Networks

Long Short-Term Memory Recurrent Neural Network Architectures for Generating Music and Japanese Lyrics

BachBot: Use deep learning to generate and harmonize music in the style of Bach

Generate Music in TensorFlow

Generate new lyrics in the style of any artist using LSTMs and TensorFlow

sound-GAN: Generative Adversial Network for music composition

Analyzing Six Deep Learning Tools for Music Generation

WIMP2: Creating Music with AI: Highlights of Current Research

Song From PI: A Musically Plausible Network for Pop Music Generation

Grammar Argumented LSTM Neural Networks with Note-Level Encoding for Music Composition

用TensorFlow生成周杰伦歌词

Hip-Hop - Generating lyrics with RNNs

Metis Final Project: Music Composition with LSTMs

http://blog.naoya.io/metis-final-project-music-composition-with-lstms/

Neural Translation of Musical Style

Poetry

NeuralSnap: Generates poetry from images using convolutional and recurrent neural networks

Generating Chinese Classical Poems with RNN Encoder-Decoder

Chinese Poetry Generation with Planning based Neural Network

Weiqi (Go)

Teaching Deep Convolutional Neural Networks to Play Go

Move Evaluation in Go Using Deep Convolutional Neural Networks(Google DeepMind, Google Brain)

Training Deep Convolutional Neural Networks to Play Go

Computer Go Research - The Challenges Ahead (Martin Müller. IEEE CIG 2015)

GoCNN: Using CNN for Go (Weiqi/Baduk) board evaluation with tensorflow

DarkGo: Go in Darknet

BetaGo: Go bots for the people

Deep Learning and the Game of Go

DarkForest

Better Computer Go Player with Neural Network and Long-term Prediction (Facebook AI Research)

AlphaGo

Mastering the game of Go with deep neural networks and tree search

AlphaGo Teach

AlphaGo的分析

How Alphago Works

AlphaGo in Depth

Leela

  • intro: Leela is a strong Go playing program combining advances in Go programming and further original research into a small, easy to use graphical interface.
  • homepage: https://sjeng.org/leela.html

Mastering the game of Go without human knowledge

Computer Go & AlphaGo Zero

AlphaZero: Mastering Games without Human Knowledge - NIPS 2017

PhoenixGo

The future is here – AlphaZero learns chess

https://en.chessbase.com/post/the-future-is-here-alphazero-learns-chess

AlphaGo Zero Cheat Sheet

https://applied-data.science/static/main/res/alpha_go_zero_cheat_sheet.png

Chess

Giraffe: Using Deep Reinforcement Learning to Play Chess

Spawkfish: neural network based chess engine

Chess position evaluation with convolutional neural network in Julia

Deep Learning for … Chess

DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Game

Learning Game of Life with a Convolutional Neural Network

Reinforcement Learning using Tensor Flow: A deep Q learning demonstration using Google Tensorflow

Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games Using Convolutional Networks

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

BlizzCon 2016 DeepMind and StarCraft II Deep Learning Panel Transcript

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

Gym StarCraft: StarCraft environment for OpenAI Gym, based on Facebook’s TorchCraft

  • intro: Gym StarCraft is an environment bundle for OpenAI Gym. It is based on Facebook’s TorchCraft, which is a bridge between Torch and StarCraft for AI research.
  • github: https://github.com/deepcraft/gym-starcraft

Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games

https://arxiv.org/abs/1703.10069

Learning Macromanagement in StarCraft from Replays using Deep Learning

Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report

DeepLeague

DeepLeague: leveraging computer vision and deep learning on the League of Legends mini map + giving away a dataset of over 100,000 labeled images to further esports analytics research

DeepLeague (Part 2): The Technical Details

Courses

Learning Machines

http://www.patrickhebron.com/learning-machines/

Learning Bit by Bit

https://itp.nyu.edu/varwiki/Syllabus/LearningBitbyBitS10

MACHINE LEARNING FOR MUSICIANS AND ARTISTS (Course opens January 2016)

https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info

Machine learning for artists @ ITP-NYU, Spring 2016

Machine Learning for Artists @ OpenDot, November 2016

The Neural Aesthetic @ SchoolOfMa, Summer 2016

http://ml4a.github.io/classes/neural-aesthetic/

Blogs

Review of machine / deep learning in an artistic context

https://medium.com/@memoakten/machine-deep-learning-in-an-artistic-context-441f28774bcc#.gegpq99ag

Apprentice Work

https://www.technologyreview.com/s/600762/apprentice-work/

Exploring the Intersection of Art and Machine Intelligence

http://googleresearch.blogspot.jp/2016/02/exploring-intersection-of-art-and.html

Using machine learning to generate music

http://www.datasciencecentral.com/profiles/blogs/using-machine-learning-to-generate-music

art in the age of machine intelligence

https://medium.com/artists-and-machine-intelligence/what-is-ami-ccd936394a83#.hyt4ei9a9

Understanding Aesthetics with Deep Learning

https://devblogs.nvidia.com/parallelforall/understanding-aesthetics-deep-learning/

Go, Marvin Minsky, and the Chasm that AI Hasn’t Yet Crossed

blog: https://medium.com/backchannel/has-deepmind-really-passed-go-adc85e256bec#.inx8nfid0

A Return to Machine Learning

Resources

Music, Art and Machine Intelligence Workshop 2016

Published: 09 Oct 2015

Face Recognition

Papers

Published: 09 Oct 2015

Deep Learning with Machine Learning

Bayesian

Published: 09 Oct 2015

Deep Learning Tutorials

Tutorials

Deep learning

VGG Convolutional Neural Networks Practical

Hacker’s guide to Neural Networks

http://karpathy.github.io/neuralnets/

Deep Learning Tutorials

Deep Learning in a Nutshell: Core Concepts

http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/

Deep Learning in a Nutshell: History and Training

http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-history-training/

A Deep Learning Tutorial: From Perceptrons to Deep Networks

http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

Deep Neural Networks (with Python code)

Three Classes of Deep Learning Architectures and Their Applications: A Tutorial Survey

Stanford Unsupervised Feature Learning and Deep Learning Tutorial: UFLDL Tutorial

The Unreasonable Effectiveness of Deep Learning (LeCun)

Deep learning from the bottom up

Introduction to Deep Learning with Python (By Alec Radford. Theano)

New to deep learning? Here are 4 easy lessons from Google

Deep Learning 101

Neural Networks Demystified

Deep Learning SIMPLIFIED

A ‘Brief’ History of Neural Nets and Deep Learning

Deep Neural Networks — An Overview

https://medium.com/@asjad/deep-neural-networks-an-overview-480112b12a13#.i7apzmnso

A Tutorial on Deep Neural Networks for Intelligent Systems

Deep Learning for Computer Vision – Introduction to Convolution Neural Networks

BI Lab Deep Learning Tutorial

Deep Learning Tutorials

Neural Network Architectures

A Practical Introduction to Deep Learning with Caffe and Python

Notes on Convolutional Neural Networks

Feed Forward and Backward Run in Deep Convolution Neural Network

Convolutional Networks

http://deeplearning4j.org/convolutionalnets.html

Exploring convolutional neural networks with DL4J

Understanding Convolutional Neural Networks

Laws, Sausages and ConvNets

Convolutional Neural Networks (CNNs): An Illustrated Explanation

intro_deep: Introduction tutorials to deep learning with Theano and OpenDeep

Deep Learning on Java by Breandan Considine

Using Convolutional Neural Networks and TensorFlow for Image Classification (NYC TensorFlow meetup)

Neural networks with Theano and Lasagne

Introduction to Deep Learning

Introduction to Deep Learning for Image Recognition - SciPy US 2016

Deep learning tutorials (2nd ed.)


A Beginner’s Guide To Understanding Convolutional Neural Networks

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2

The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)


Deep Learning Part 1: Comparison of Symbolic Deep Learning Frameworks

Deep Learning Part 2: Transfer Learning and Fine-tuning Deep Convolutional Neural Networks

Deep Learning Part 3: Combining Deep Convolutional Neural Network with Recurrent Neural Network


Introduction to Deep Learning for Image Processing

The best explanation of Convolutional Neural Networks on the Internet!

The Evolution and Core Concepts of Deep Learning & Neural Networks

An Intuitive Explanation of Convolutional Neural Networks

How Convolutional Neural Networks Work

Preliminary Note on the Complexity of a Neural Network

Deep Learning Tutorial

Jupyter notebooks and code for Intro to DL talk at Genesys

Learn Deep Learning the Hard Way

A Complete Guide on Getting Started with Deep Learning in Python

Deep learning for complete beginners: Recognising handwritten digits

Deep learning for complete beginners: Using convolutional nets to recognise images

Deep learning for complete beginners: neural network fine-tuning techniques

How do Convolutional Neural Networks work?

http://brohrer.github.io/how_convolutional_neural_networks_work.html

Creating a Neural Network That Can Tell if a Name Is Male or Female, in JavaScript

Softmax Classifiers Explained

The Softmax function and its derivative

How an algorithm behind Deep Learning works

The Neural Network Zoo

http://www.asimovinstitute.org/neural-network-zoo/

Recognising Beer with TensorFlow

Deep learning architecture diagrams

Getting Started with Deep Learning and Python

Deep Learning Practicals

A simple workflow for deep learning

A primer on universal function approximation with deep learning (in Torch and R)

An Introduction to Implementing Neural Networks using TensorFlow

https://www.analyticsvidhya.com/blog/2016/10/an-introduction-to-implementing-neural-networks-using-tensorflow/

A Gentle Introduction to Convolutional Neural Networks

Beginning Machine Learning with Keras and TensorFlow

Shortest Way to Deep Learning

Deep learning with Matlab

Convolutional neural networks for computer vision with Matlab

Neural Net Computing Explodes

Tutorial: Optimizing Neural Networks using Keras (with Image recognition case study)

https://www.analyticsvidhya.com/blog/2016/10/tutorial-optimizing-neural-networks-using-keras-with-image-recognition-case-study/

15 Deep Learning Tutorials

Deep Learning Episode 1: Optimizing DeepMind’s A3C on Torch

http://www.allinea.com/blog/201607/deep-learning-episode-1-optimizing-deepminds-a3c-torch

Deep Learning Episode 2: Scaling TensorFlow over multiple EC2 GPU nodes

http://www.allinea.com/blog/201608/deep-learning-episode-2-scaling-tensorflow-over-multiple-ec2-gpu-nodes

Deep Learning Episode 3: Supercomputer vs Pong

http://www.allinea.com/blog/201610/deep-learning-episode-3-supercomputer-vs-pong

Deep Learning Episode 4: Supercomputer vs Pong II

http://www.allinea.com/blog/201610/deep-learning-episode-4-supercomputer-vs-pong-ii

Nuts and Bolts of Applying Deep Learning — Summary

Intro to Deep Learning for Computer Vision

http://chaosmail.github.io/deeplearning/2016/10/22/intro-to-deep-learning-for-computer-vision/

If I Can Learn to Play Atari, I Can Learn TensorFlow

TensorFlow workshop materials

Some theorems on deep learning

Pokemon, Colors, and Deep Learning

Why Deep Learning is Radically Different from Machine Learning

Deep Learning: The Unreasonable Effectiveness of Randomness

Deep Meta-Learning : Machines now Bootstrap Themselves

Are Deep Neural Networks Creative?

Are Deep Neural Networks Creative? v2

Develop/Train A Convolutional Neural Netwok For MNIST Dataset

Rethinking Generalization in Deep Learning

https://medium.com/intuitionmachine/rethinking-generalization-in-deep-learning-ec66ed684ace#.tcnsqik5w

The hard thing about deep learning

The hard thing about deep learning

Introduction to Autoencoders

Two Days to a Demo

Deep Learning Tutorials for 10 Weeks

Deep Learning in Clojure With Cortex

A Guide to Deep Learning by YerevaNN

Learning to Learn, to Program, to Explore and to Seek Knowledge

Have Fun with Machine Learning: A Guide for Beginners

Deep Learning Cheat Sheet

How to train your Deep Neural Network

http://rishy.github.io//ml/2017/01/05/how-to-train-your-dnn/

A deep learning traffic light detector using dlib and a few images from Google street view

Recognizing Traffic Lights With Deep Learning

Tutorials for deep learning

The Holographic Principle: Why Deep Learning Works

https://medium.com/intuitionmachine/the-holographic-principle-and-deep-learning-52c2d6da8d9

Deep Neural Networks - A Brief History

Fundamental Deep Learning code in TFLearn, Keras, Theano and TensorFlow

Deep Neural Network from scratch

https://matrices.io/deep-neural-network-from-scratch/

Convolutional Neural Networks

https://github.com/Alfredvc/cnn_workshop

Exploring Optimizers

https://github.com//KeremTurgutlu/deeplearning/blob/master/Exploring%20Optimizers.ipynb

A Gentle Introduction to Exploding Gradients in Neural Networks

https://machinelearningmastery.com/exploding-gradients-in-neural-networks/

Only Numpy: (Why I do Manual Back Propagation) Implementing Multi Channel/Layer Convolution Neural Network on Numpy with Interactive Code

https://medium.com/swlh/only-numpy-why-i-do-manual-back-propagation-implementing-multi-channel-layer-convolution-neural-7d83242fcc24

92.45% on CIFAR-10 in Torch

Convolution

Understanding Convolutions

Note on the implementation of a convolutional neural networks

Convolution in Caffe: a memo

我对卷积的理解

An Analysis of Convolution for Inference

http://www.slideshare.net/nervanasys/an-analysis-of-convolution-for-inference

Understanding Convolution in Deep Learning

A guide to convolution arithmetic for deep learning

Going beyond full utilization: The inside scoop on Nervana’s Winograd kernels

Playing with convolutions in TensorFlow: From a short introduction to convolution to a complete model

How convolutional neural networks see the world: An exploration of convnet filters with Keras

One by One [ 1 x 1 ] Convolution - counter-intuitively useful

http://iamaaditya.github.io/2016/03/one-by-one-convolution/

Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize

Receptive Field

A guide to receptive field arithmetic for Convolutional Neural Networks

https://medium.com/@nikasa1889/a-guide-to-receptive-field-arithmetic-for-convolutional-neural-networks-e0f514068807

Momentum

Why Momentum Really Works

maxDNN

maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

GEMM (General Matrix Matrix Multiply)

Why GEMM is at the heart of deep learning

A full walk through of the SGEMM implementation

Backpropagation

Learning representations by back-propagating errors

Learning Internal Representations by Error Propagating

Calculus on Computational Graphs: Backpropagation

Styles of Truncated Backpropagation

Is BackPropagation Necessary?

Backpropagation In Convolutional LSTMs

https://www.doc.ic.ac.uk/~ahanda/ConvLSTMs.pdf

Backward Pass on Conv Layer

Convolutional Neural Networks backpropagation: from intuition to derivation

Backpropagation In Convolutional Neural Networks

Why do we rotate weights when computing the gradients in a convolution layer of a convolution network?

http://soumith.ch/ex/pages/2014/08/07/why-rotate-weights-convolution-gradient/

Note on the implementation of a convolutional neural networks

http://cthorey.github.io./backprop_conv/

Attention

Attention in a Convolutional Neural Net

Attention-based Networks

Attention in Neural Networks and How to Use It

http://akosiorek.github.io/ml/2017/10/14/visual-attention.html

Softmax

Hierarchical softmax and negative sampling: short notes worth telling

https://towardsdatascience.com/hierarchical-softmax-and-negative-sampling-short-notes-worth-telling-2672010dbe08

Caffe

DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe

Deep learning tutorial on Caffe technology : basic commands, Python and C++ code

http://christopher5106.github.io/deep/learning/2015/09/04/Deep-learning-tutorial-on-Caffe-Technology.html

Using Caffe with your own dataset

https://medium.com/@alexrachnog/using-caffe-with-your-own-dataset-b0ade5d71233

OpenCV 3.0.0-dev: Load Caffe framework models

http://docs.opencv.org/master/d5/de7/tutorial_dnn_googlenet.html#gsc.tab=0

Chainer

Chainer Info

https://github.com/hidetomasuoka/chainer-info

Keras

Keras tutorial

Keras Tutorial: The Ultimate Beginner’s Guide to Deep Learning in Python

https://elitedatascience.com/keras-tutorial-deep-learning-in-python

Deep Learning with Keras: Tutorial @ EuroScipy 2016

Transfer Learning and Fine Tuning for Cross Domain Image Classification with Keras

MXNet

10 Deep Learning projects based on Apache MXNet

https://medium.com/@julsimon/10-deep-learning-projects-based-on-apache-mxnet-8231109f3f64

Awesome MXNet(Beta)

https://github.com/chinakook/Awesome-MXNet

TVM

Optimize Deep Learning GPU Operators with TVM: A Depthwise Convolution Example

http://tvmlang.org/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example.html

Theano

Theano Tutorial @ LTI, Carnegie Mellon University

An Introduction to MXNet/Gluon

TensorFlow

LearningTensorFlow.com: A beginners guide to a powerful framework.

TensorFlow Examples: TensorFlow tutorials and code examples for beginners

Awesome TensorFlow: A curated list of awesome TensorFlow experiments, libraries, and projects

The Good, Bad, & Ugly of TensorFlow: A survey of six months rapid evolution (+ tips/hacks and code to fix the ugly stuff)

Tensorflow Tutorials using Jupyter Notebook

TensorFlow Tutorial

FIRST CONTACT WITH TENSORFLOW

Introduction to TensorFlow

TensorFlow-Tutorials: Simple tutorials using Google’s TensorFlow Framework

Neural Network Toolbox on TensorFlow

Awesome Tensorflow Implementations

The Ultimate List of TensorFlow Resources: Books, Tutorials & More

Install TensorFlow: Slides and code from our TensorFlow Workshop

A Tour of TensorFlow

TensorFlow Tutorials

Shapes and dynamic dimensions in TensorFlow

TensorFlow saving/restoring and mixing multiple models

https://blog.metaflow.fr/tensorflow-saving-restoring-and-mixing-multiple-models-c4c94d5d7125#.242xy4d46

Getting to Know TensorFlow

Image Classification and Segmentation with Tensorflow and TF-Slim http://warmspringwinds.github.io/tensorflow/tf-slim/2016/10/30/image-classification-and-segmentation-using-tensorflow-and-tf-slim/

Not another MNIST tutorial with TensorFlow

Dive Into TensorFlow

TensorFlow Exercises - focusing on the comparison with NumPy.

A Gentle Guide to Using Batch Normalization in Tensorflow

Using TensorFlow in Windows with a GPU

Tensorflow and deep learning - without a PhD

4 Steps To Learn TensorFlow When You Already Know scikit-learn https://medium.com/@Zelros/4-steps-to-learn-tensorflow-when-you-already-know-scikit-learn-3cd0340456b5#.q206au7u9

Gentlest Introduction to Tensorflow

learn code with tensorflow

TensorFlow Machine Learning Cookbook

TensorFlow Image Recognition on a Raspberry Pi

http://svds.com/tensorflow-image-recognition-raspberry-pi/

TensorFlow For Machine Intelligence

Installing TensorFlow on Raspberry Pi 3 (and probably 2 as well)

CodinGame: Deep Learning - TensorFlow

A Practical Guide for Debugging Tensorflow Codes

Debugging Tips on TensorFlow

Tensorflow Projects: Deep learning using tensorflow

Machine Learning with TensorFlow

Convolutional Networks: from TensorFlow to iOS BNNS

Android TensorFlow Machine Learning Example

TensorFlow and Deep Learning Tutorials

https://github.com/wagamamaz/tensorflow-tutorial

Finetuning AlexNet with TensorFlow

Deep Learning examples using Tensorflow

https://github.com/aditya101993/Deep-Learning

How To Write Your Own Tensorflow in C++

https://oneraynyday.github.io/ml/2017/10/20/Tensorflow-C++/

Tensorflow on Android

A Guide to Running Tensorflow Models on Android

TensorFlow Android stand-alone demo

Torch

Torch Developer Guide

PyTorch

Practical PyTorch tutorials

The Incredible PyTorch

PyTorch quick start: Classifying an image

tutorial for researchers to learn deep learning with pytorch.

https://github.com/yunjey/pytorch-tutorial

Building a System for Fun!

Facial Recognition On A Jetson TX1 In Tensorflow

Build an AI Cat Chaser with Jetson TX1 and Caffe

Deep Learning in Aerial Systems Using Jetson

Cherry Autonomous Racecar (CAR): NCAT ECE Senior Design Project

Traffic Signs Classification

Traffic signs classification with Deep Learning.

Traffic Sign Recognition with TensorFlow

Traffic signs classification with a convolutional network

http://navoshta.com/traffic-signs-classification/

Convolutional Neural Network for Traffic Sign Classification — CarND

Talks

A Tour of Deep Learning With C++

Published: 09 Oct 2015

Deep Learning Tricks

Papers

Practical recommendations for gradient-based training of deep architectures

Blogs

Efficient BackProp

Deep Learning for Vision: Tricks of the Trade

Optimizing RNN performance

  • intro: Silicon Valley AI Lab
  • keywords: Optimize GEMM, parallel GPU, GRU and LSTM…
  • blog: http://svail.github.io/

Must Know Tips/Tricks in Deep Neural Networks

Training Tricks from Deeplearning4j

http://deeplearning4j.org/trainingtricks.html

Suggestions for DL from Llya Sutskeve

Efficient Training Strategies for Deep Neural Network Language Models

Neural Networks Best Practice

Dark Knowledge from Hinton

Stochastic Gradient Descent Tricks(Leon Bottou)

http://leon.bottou.org/publications/pdf/tricks-2012.pdf

Advice for applying Machine Learning

https://jmetzen.github.io/2015-01-29/ml_advice.html

How to Debug Learning Algorithm for Regression Model

http://vitalflux.com/machine-learning-debug-learning-algorithm-regression-model/

Large-scale L-BFGS using MapReduce

Selecting good features

– Part I: univariate selection: http://blog.datadive.net/selecting-good-features-part-i-univariate-selection/ – Part II: linear models and regularization: http://blog.datadive.net/selecting-good-features-part-ii-linear-models-and-regularization/ – Part III: random forests: http://blog.datadive.net/selecting-good-features-part-iii-random-forests/ – Part IV: stability selection, RFE and everything side by side: http://blog.datadive.net/selecting-good-features-part-iv-stability-selection-rfe-and-everything-side-by-side/

机器学习代码心得之有监督学习的模块

http://www.weibo.com/p/1001603795687165852957

Stochastic Gradient Boosting: Choosing the Best Number of Iterations

Large-Scale High-Precision Topic Modeling on Twitter

H2O World - Top 10 Deep Learning Tips & Tricks - Arno Candel

http://www.slideshare.net/0xdata/h2o-world-top-10-deep-learning-tips-tricks-arno-candel

How To Improve Deep Learning Performance: 20 Tips, Tricks and Techniques That You Can Use To Fight Overfitting and Get Better Generalization

http://machinelearningmastery.com/improve-deep-learning-performance/

Neural Network Training Speed Trick

https://medium.com/machine-learning-at-petiteprogrammer/neural-network-training-speed-trick-92d6b22a7754#.4v6qukpn7

The Black Magic of Deep Learning - Tips and Tricks for the practitioner

http://nmarkou.blogspot.ru/2017/02/the-black-magic-of-deep-learning-tips.html

Published: 09 Oct 2015

Deep Learning Software and Hardware

Papers

Accelerating Deep Convolutional Neural Networks Using Specialized Hardware

Installation / Deploying

Setting up a Deep Learning Machine from Scratch (Software): Instructions for setting up the software on your deep learning machine

  • intro: A detailed guide to setting up your machine for deep learning research. Includes instructions to install drivers, tools and various deep learning frameworks. This was tested on a 64 bit machine with Nvidia Titan X, running Ubuntu 14.04
  • github: https://github.com/saiprashanths/dl-setup

How to install CUDA Toolkit and cuDNN for deep learning

Deploying Deep Learning: Guide to deploying deep-learning inference networks and realtime object detection with TensorRT and Jetson TX1.

Install Log

Lessons Learned from Deploying Deep Learning at Scale

Docker

All-in-one Docker image for Deep Learning

NVIDIA Docker: GPU Server Application Deployment Made Easy

Deep learning base image for Docker (Tensorflow, Caffe, MXNet, Torch, Openface, etc.)

https://github.com/dominiek/deep-base

Deepo: a Docker image with a full reproducible deep learning research environment

Cloud

SuperVessel Cloud for POWER/OpenPOWER LoginRegisterTutorials

http://www.ptopenlab.com/

Building Deep Neural Networks in the Cloud with Azure GPU VMs, MXNet and Microsoft R Server

https://blogs.technet.microsoft.com/machinelearning/2016/09/15/building-deep-neural-networks-in-the-cloud-with-azure-gpu-vms-mxnet-and-microsoft-r-server/

Microsoft open sources its next-gen cloud hardware design

Google Taps AMD For Accelerating Machine Learning In The Cloud

http://www.forbes.com/sites/aarontilley/2016/11/15/google-taps-amd-for-accelerating-machine-learning-in-the-cloud/#3549d8554181

Amazon EC2

Deep Learning AMI on AWS Marketplace

https://aws.amazon.com/marketplace/pp/B01M0AXXQB

We Have To Go Deeper: AWS p2.xlarge GPU optimized deep learning cluster-grenade

A GPU enabled AMI for Deep Learning

Keras with GPU on Amazon EC2 – a step-by-step instruction

https://medium.com/@mateuszsieniawski/keras-with-gpu-on-amazon-ec2-a-step-by-step-instruction-4f90364e49ac#.k27d0mqir

Microsoft R Server

Training Deep Neural Networks on ImageNet Using Microsoft R Server and Azure GPU VMs

Hardware System

I: Building a Deep Learning (Dream) Machine

II: Running a Deep Learning (Dream) Machine

A Full Hardware Guide to Deep Learning

Build your own Deep Learning Box

32-TFLOP Deep Learning GPU Box: A super-fast linux-based machine with multiple GPUs for training deep neural nets

https://hackaday.io/project/12070-32-tflop-deep-learning-gpu-box

Hands-on with the NVIDIA DIGITS DevBox for Deep Learning

Considerations when setting up deep learning hardware

Building a Workstation for Deep Learning

Deep Learning Machine: First build experience

Building a machine learning/deep learning workstation for under $5000

Hardware Guide: Neural Networks on GPUs (Updated 2016-1-30)

Building Your Own Deep Learning Box

https://medium.com/@bfortuner/building-your-own-deep-learning-box-47b918aea1eb#.4r5zchk4f

Setting up a Deep learning machine in a lazy yet quick way https://medium.com/@sravsatuluri/setting-up-a-deep-learning-machine-in-a-lazy-yet-quick-way-be2642318850#.jrxrkfxa2

Deep Confusion: Misadventures In Building A Deep Learning Machine

http://www.topbots.com/deep-confusion-misadventures-in-building-a-machine-learning-server/

DIY-Deep-Learning-Workstation

GPU

Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning

从深度学习选择什么样的gpu来谈谈gpu的硬件架构

GPU折腾手记——2015 (by 李沐)

HPC, Deep Learning and GPUs(2016 Stanford HPC Conference)

Modern GPU 2.0: Design patterns for GPU computing

CuMF: CUDA-Acclerated ALS on mulitple GPUs.

Basic Performance Analysis of NVIDIA GPU Accelerator Cards for Deep Learning Applications

CuPy : NumPy-like API accelerated with CUDA

NumPy GPU acceleration

Efficient Convolutional Neural Network Inference on Mobile GPUs (Embedded Vision Summit)

Deep Learning with Multiple GPUs on Rescale: Torch

GPU-accelerated Theano & Keras on Windows 10 native

NVIDIA Announces Quadro GP100 - Big Pascal Comes to Workstations

http://www.anandtech.com/show/11102/nvidia-announces-quadro-gp100

FPGA

Recurrent Neural Networks Hardware Implementation on FPGA

Is implementing deep learning on FPGAs a natural next step after the success with GPUs?

Efficient Implementation of Neural Network Systems Built on FPGAs, Programmed with OpenCL

Deep Learning on FPGAs: Past, Present, and Future

FPGAs Challenge GPUs as a Platform for Deep Learning

Convolution Neural Network CNN Implementation on Altera FPGA using OpenCL

Accelerating Deep Learning Using Altera FPGAs (Embedded Vision Summit)

Machine Learning on FPGAs: Neural Networks

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

Microsoft Goes All in for FPGAs to Build Out AI Cloud

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

Intel Unveils FPGA to Accelerate Neural Networks

http://datacenterfrontier.com/intel-unveils-fpga-to-accelerate-ai-neural-networks/

Deep Learning with FPGA

A General Neural Network Hardware Architecture on FPGA

Approximate FPGA-based LSTMs under Computation Time Constraints

ARM / Processor

‘Neural network’ spotted deep inside Samsung’s Galaxy S7 silicon brain: Secrets of Exynos M1 cores spilled

Intel will add deep-learning instructions to its processors

SRAM

ShiDianNao: Shifting Vision Processing Closer to the Sensor http://lap.epfl.ch/files/content/sites/lap/files/shared/publications/DuJun15_ShiDianNaoShiftingVisionProcessingCloserToTheSensor_ISCA15.pdf

Blogs

Emerging “Universal” FPGA, GPU Platform for Deep Learning

An Early Look at Startup Graphcore’s Deep Learning Chip

https://www.nextplatform.com/2017/03/09/early-look-startup-graphcores-deep-learning-chip/

Hardware for Deep Learning

https://medium.com/towards-data-science/hardware-for-deep-learning-8d9b03df41a

Videos

Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks

Published: 09 Oct 2015