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Awesome Deep Learning

A curated list of awesome Deep Learning tutorials, projects and communities.

Here you can see meta information about this topic like the time we last updated this page, the original creator of the awesome list and a link to the original GitHub repository.

Last Update: Dec. 5, 2020, 6:14 a.m.

Thank you ChristosChristofidis & contributors
View Topic on GitHub:
ChristosChristofidis/awesome-deep-learning

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Simply type in what you are looking for and the results will be filtered on the fly.

Further filter the resources on this page by type (repository/other resource), number of stars on GitHub and time of last commit in months.

Books

Courses

Deep Learning for Natural Language Processing Stanford University Lecture Videos Assignments Lecture Notes

Deep Learning Carnegie Mellon University Assignments Readings

A seven day bootcamp designed in MIT to introduce deep learning methods and applications

A free five-weekend plan to self-learners to learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs, VAEs, GANs, DQN, A3C and more

A free deep reinforcement learning course by OpenAI

Google's fast-paced, practical introduction to machine learning.

Grokking Deep Learning in Motion will not just teach you how to use a single library or framework, you’ll actually discover how to build these algorithms completely from scratch!

Videos and Lectures

In this liveVideo course, machine learning expert Oliver Zeigermann teaches you the basics of deep learning.

Deep Learning with R in Motion teaches you to apply deep learning to text and images using the powerful Keras library and its R language interface.

Papers

The most cited deep learning papers

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Deep Learning Carnegie Mellon University Assignments Readings

Better classifications with RNNs with bidirectional scanning on the time axis.

4 stacked LSTM cells of 1000 hidden size with reversed input sentences, and with beam search, on the WMT’14 English to French dataset.

Very deep residual layers with batch normalization layers - a.k.a. "how to overfit any vision dataset with too many layers and make any vision model work properly at recognition given enough data".

Tutorials

IPAM Tutorials on Theano/Torch

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the best machine learning tutorials on the web

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Simple tutorials using Google's TensorFlow Framework

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From the basics to slightly more interesting applications of Tensorflow

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TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

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Deep Learning Resources and Tutorials using Keras and Lasagne

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GPL-2.0

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

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MIT

Simple and ready-to-use tutorials for TensorFlow

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MIT

PyTorch Tutorial for Deep Learning Researchers

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MIT

Hardware & computer guide for AI: how to choose & build a computer. This guide is intended for beginers in hardware, in order to plan what you need and how to choose parts for a computer specialized in Deep Learning. 🤖

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Contains Example Notebooks for various machine learning tasks using the Trax library

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Apache-2.0

ML projects using the Flax Library

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A Comparative Analysis of Different Approaches for Fake News Classification

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MIT

This repository contains a software based on Cox Proportional Hazard Models for Regression Analysis of Primary Biliary Cirrhosis

2
0
50d
MIT

Probabilistic cross-identification of astronomy sources across observations, instruments and telescopes.

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Named Entity Recognition Model made using LSTMs in Tensorflow. Used Annotated Corpus for Named Entity Recognition by Abhinav Walia on Kaggle.

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A Cat vs Dog Classifier App built using tensorflow-lite and Flutter

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Deep Learning for Search teaches you how to leverage neural networks, NLP, and deep learning techniques to improve search performance.

Researchers

it is now led by Zaid Harchaoui, although Prof. Lecun is rumored to still stop by from time to time. It covers the theory, technique, and tricks that are used to achieve very high accuracy for machine learning tasks in computer vision and natural language processing. The assignments are in Lua and hosted on Kaggle.

Websites

Software](http://deeplearning.net/software_links/)

Datasets

Question answering dataset featured in "Teaching Machines to Read and Comprehend

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The Open Images dataset

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A MNIST-like fashion product database. Benchmark

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MIT

A dataset of millions of news articles scraped from a curated list of data sources.

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Apache-2.0

contains data sets good for machine learning

Conferences

Frameworks

Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

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MIT

[UNMAINTAINED] Simple feed-forward neural network in JavaScript

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MIT

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

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BSD-2-Clause

Implementation of some deep learning algorithms.

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BSD-3-Clause

Deep learning in Python

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MIT

java deep learning algorithms and deep neural networks with gpu acceleration

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GPU-Accelerated Deep Learning Library in Python

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GPL-2.0

Deep Learning framework for Julia

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The Deep Learning training framework on Spark

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Apache-2.0

Koç University deep learning framework.

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Deep Learning GPU Training System

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Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

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Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

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MatConvNet: CNNs for MATLAB

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Minerva: a fast and flexible tool for deep learning on multi-GPU. It provides ndarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy.

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Fast, flexible and fun neural networks.

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An Open Source Machine Learning Framework for Everyone

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Microsoft Distributed Machine Learning Toolkit

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MIT

Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

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Apache-2.0

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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Distributed machine learning platform

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Marvin: A Minimalist GPU-only N-Dimensional ConvNets Framework

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MIT

Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models

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Models and examples built with TensorFlow

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Torch on steroids

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PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

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Lightweight library to build and train neural networks in Theano

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Combines the ease of use of scikit-learn with the power of Theano/Lasagne

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MIT

TensorFlow-based neural network library

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Tensors and Dynamic neural networks in Python with strong GPU acceleration

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Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

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Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!

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MIT

Caffe2 is a lightweight, modular, and scalable deep learning framework.

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Apache-2.0

WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.

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Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

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Apache-2.0

Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

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MIT

A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

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Apache-2.0

Accelerated deep learning R&D

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A toolkit for reproducible reinforcement learning research.

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MIT

Build fully-functioning computer vision models with PyTorch

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MIT

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

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GPL-3.0

A lightweight library for neural networks that runs anywhere

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MIT

Tensorforce: a TensorFlow library for applied reinforcement learning

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Apache-2.0

Hopsworks - Data-Intensive AI platform with a Feature Store

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Feature Store for Machine Learning

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A python library for self-supervised learning.

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MIT

Trax — Deep Learning with Clear Code and Speed

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Flax is a neural network ecosystem for JAX that is designed for flexibility.

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An Easy To Use PyTorch Computer Vision Library

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Deep Learning Framework

Scientific computing framework with wide support for machine learning algorithms, used by Facebook, Google, and more.

Minimal, modular deep learning library for TensorFlow and Theano

Tools

Visualizer for neural network, deep learning, and machine learning models

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MIT

TensorFlow's Visualization Toolkit

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Debugging, monitoring and visualization for Python Machine Learning and Data Science

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MIT

🛠 All-in-one web-based IDE specialized for machine learning and data science.

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Apache-2.0

A little logger for machine learning research

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MIT

Determined: Deep Learning Training Platform

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Browser extension (Chrome and Firefox) that automatically finds and shows code implementations for machine learning papers anywhere: Google, Twitter, Arxiv, Scholar, etc.

Miscellaneous

Docker files

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A GPU implementation of Convolutional Neural Nets in C++

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BSD-2-Clause

A graphics backend for the browser (with a Torch7 client).

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MIT

http://torch.ch

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Deep Pink is a chess AI that learns to play chess using deep learning.

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Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind

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GPL-3.0

Generating Vectors for DBpedia Entities via Word2Vec and Wikipedia Dumps. Questions? https://gitter.im/idio-opensource/Lobby

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The original code from the DeepMind article + my tweaks

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A recurrent neural network designed to generate classical music.

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BSD-2-Clause

Memory Networks implementations

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Face recognition with deep neural networks.

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Apache-2.0

Digit recognition with neural network

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Caffe models in TensorFlow

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An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search" (Nature 529, 484-489, 28 Jan 2016), details of which can be found on their website https://deepmind.com/publications.html.

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7m
MIT

A complete daily plan for studying to become a machine learning engineer.

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3d
CC-BY-SA-4.0

Face detection using deep learning.

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List of articles related to deep learning applied to music

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MIT

A collection of important graph embedding, classification and representation learning papers with implementations.

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CC0-1.0

A curated list of network embedding techniques.

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Best Practices on Recommendation Systems

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8d
MIT

Semi-Supervised Learning with Ladder Networks in Keras. Get 98% test accuracy on MNIST with just 100 labeled examples !

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Curated list of libraries for a faster machine learning workflow

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4m
MIT

Roadmap to becoming an Artificial Intelligence Expert in 2020

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MIT