11 Jul 2018*views: 59*

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.

The "deep" in "deep learning" refers to the number of layers through which the data is transformed. Deep learning models are inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains.

Deep Learning is a vast field and cannot be understood without proper time investment into the field as one have to learn many theory maths and algorithms. The true power of Deep Learning lies in the Learning or Training process. To get started here is the top frameworks and projects regarding Deep Learning on GitHub.

Here's a list of top 180 deep learning Github repositories sorted by the number of stars and popularity.

Computation using data flow graphs for scalable machine learning. TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

Deep Learning for humans. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Open Source Computer Vision Library. OpenCV (Open Source Computer Vision Library) is released under a BSD license and hence itâ€™s free for both academic and commercial use. It has C++, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

Caffe: a fast open framework for deep learning. Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.

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

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks built on a tape-based autograd system. You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet. Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. The goal of Detectron is to provide a high-quality, high-performance codebase for object detection research. It is designed to be flexible in order to support rapid implementation and evaluation of novel research.

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

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

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

Deeplearning4j, ND4J, DataVec and more - deep learning & linear algebra for Java/Scala with GPUs + Spark - From Skymind

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

Machine Learning From Scratch. Bare bones Python implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from data mining to deep learning.

Dive into Machine Learning with Python Jupyter notebook and scikit-learn!

An awesome Data Science repository to learn and apply for real world problems.

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

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

machine learning and deep learning tutorials, articles and other resources

OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen /faster_rcnn for the official MATLAB version

Image-to-image translation in PyTorch (e.g., horse2zebra, edges2cats, and more)

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

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

ncnn is a high-performance neural network inference framework optimized for the mobile platform

A series of Docker images (and their generator) that allows you to quickly set up your deep learning research environment.

This research aims at simply deploying deeplearning on mobile and embedded devices, with low complexity and high speed. old name mobile deep learning.

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

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

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

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.

Code for "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression"

An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)

An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.

Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled.

Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library

Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial

Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow

Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

A simple interface for editing natural photos with generative neural networks.

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.

Speech recognition using the tensorflow deep learning framework, sequence-to-sequence neural networks

Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow, XGBoost and TSNE

Deep learning software for colorizing black and white images with a few clicks.

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