GitHub Top200 deep learning open source project announced!

Recently, Mahmoud Badry from Egypt made a list of GitHub’s Top 200 deep learning projects based on the number of stars in each deep learning project

The top 10 is as follows:

Google’s TensorFlow is no surprise at number one, followed by Keras, a minimalist, highly modular neural network library. Let’s take a closer look at the top 10 projects

1, TensorFlow

TensorFlow is Google’s second generation machine learning system. TensorFlow’s extended support for deep learning is built into TensorFlow. Any computation that can be expressed as a computational flow graph can be used with TensorFlow. (project address: https://github.com/tensorflow/tensorflow)

2, Keras

Keras is a minimalist, highly modular neural network library developed in Python (Python 2.7-3.5.) that can run on either TensorFlow or Theano platforms and is designed to enable rapid development of deep learning. (Project Address: https://github.com/keras-team/keras)

3, OpenCV

OpenCV is Intel’s open source computer vision library. It consists of a series of C functions and a small number of C++ classes. It has a cross-platform mid-level and high-level API that includes more than 300 C functions and implements many common algorithms in image processing and computer vision. (project address: https://github.com/opencv/opencv)

4, Caffe

Caffe is a deep learning framework made up of expressions, speeds and modularity. (project address: https://github.com/BVLC/caffe)

5, TensorFlow – Examples

TensorFlow is a beginner tutorial for TensorFlow beginners. (project address: https://github.com/aymericdamien/TensorFlow-Examples)

6, the Machine – Learning – For – Software Engineers

This is a multi-month program for project creators to go from mobile developer (self-taught, no CS degree) to machine learning engineer. (project address: https://github.com/ZuzooVn/machine-learning-for-software-engineers)

7, Deeplearningbook – Chinese

The Chinese translation of the book “deep learning” (project address: https://github.com/exacity/deeplearningbook-chinese)

8, Deep – Learning – cca shut – Reading – Roadmap

If you are new to the field of deep learning, the first question you may encounter is “Which paper should I start reading?” So, this project is best for you (address: https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap)

9 PyTorch.

PyTorch provides two senior: powerful GPU acceleration Tensor calculation (like numpy), building automatic upgrade system based on the tape on the depth of the neural network (address: https://github.com/pytorch/pytorch)

10 and Awesome – Deep – Learning – cca shut

This project is a selected list of the most highly regarded deep learning papers. This project lists 100 deep learning papers published between 2012 and 2016. (project address: https://github.com/terryum/awesome-deep-learning-papers)

What other deep learning frameworks make the list?

CNTK – Microsoft Computing Network Toolkit (CNTK) is a very powerful command line system for creating neural network prediction systems.

MXNet – At number 13, MXNet is a lightweight, portable, flexible distributed deep learning framework for mobile.

Deeplearning4j – DL4J for short is the first commercial-grade open source distributed deep learning library written for Java and Scala.

Caffe 2 – Open source by Facebook, Caffe 2 is a deep learning framework for expressiveness, speed, and modularity

PaddlePaddle – Baidu also has a deep learning project on the list. PaddlePaddle is a deep learning platform developed by Baidu. It is easy to use, efficient, flexible and scalable, and provides deep learning algorithm support for multiple products within Baidu.

DSSTNE – This deep learning project is open source by Amazon, but the deep learning system currently has significant limitations.

In addition, there are many tutorial projects on the list:

Awesome-deep-learning – Free online books, courses, videos and handouts, papers, tutorials, websites, data sets, frameworks and other resources on Deep Learning.

Machine-learning-tutorials – This repository contains Machine Learning and deep Learning Tutorials, articles, and other resources

Stanford-tensorflow -Tutorials – This repository contains code examples of Stanford courses

DeepLearningTutorials – This tutorial focuses on some of the most important deep learning algorithms and will show you how to run them using Theano.

The full list address: https://github.com/mbadry1/Top-Deep-Learning