100 Best GitHub: Deep Learning

本文整理了深度学习领域的顶级资源、工具、教程和代码,包括Caffe、CuDNN、Darch、DeepLearning4j等热门库及平台,旨在为深度学习爱好者提供一站式学习与实践指导。
部署运行你感兴趣的模型镜像

http://meta-guide.com/software-meta-guide/100-best-github-deep-learning/

100 Best GitHub: Deep Learning

ghdeeplearning170714

Deep Learning

Resources:

References:

See also:

100 Best MATLAB VideosDeep Learning & Dialog Systems | MATLAB & Dialog Systems 2010 | MATLAB & Dialog Systems 2011 | MATLAB & Dialog Systems 2012 | MATLAB & Dialog Systems 2013Software Meta Guide


deep-learning [100x Jul 2014]

  • rasmusbergpalm/DeepLearnToolbox .. 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.
  • karpathy/convnetjs .. Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
  • nicholas-leonard/dp .. A deep learning library designed for streamlining research and development using the Torch7 distribution.
  • daemonmaker/DMPC .. Repository for experimenting with combining deep learning and model predictive control techniques.
  • woobe/deepr .. An R package to streamline the training, fine-tuning and predicting processes for deep learning based on ‘darch’ and ‘deepnet’.
  • RAMLab/deepbrains .. DeepBrains is a deep learning framework based on GO programming language and common machine learning hierarchical models.
  • Misrab/deeplearning .. A collection of Go packages for Machine Learning, with an emphasis on Deep Learning
  • drshrey/deep_learning .. Playing around with the Alchemy API, and see what’s going on with Natural Language Processing and Deep Learning
  • hycis/pynet .. pynet is meant to be a flexible and modular deep learning framework base on theano.
  • Cysu/dlearn .. A deep learning Python module based on Theano.
  • zhaojunbo/paracel-SDAE .. The repo involves a Deep Learning method, Stacked Denoising Autoencoder. The repo exploits “paracel”, which is a new parallel computing framework.
  • DrDanRyan/ML-Framework .. A modular machine learning framework for Matlab with an emphasis on deep neural network models.
  • junku901/dnn .. Deep learning library for node.js. (Includes Logistic-Regression, MLP, RBM, DBN, CRBM, CDBN)
  • xqcao/mywebapp .. Deep Learning is a new area of Machine Learning research, I share my deeper learning work overhere,
  • EderSantana/DeepEEG .. Deep learning for EEG analysis. This script was used to generate the results of the paper “Joint Optimization of Algorithmic Suites for EEG analysis” at EMBC 2014
  • samuel1208/UFLDL .. The solution of Andrew Unsupervised Feature Learning and Deep Learning
  • nibroc/SimpleSocket .. A simple non-production, hobby library for socket operations. This is intended as a learning project to deeply familiarize myself with unix sockets. It is not intended for real use.
  • daemonmaker/hedgehog .. Re-implementation of method in Playing Atari with Deep Reinforcement Learning paper.
  • chausler/deep .. some temporal RBM based models we’re playing with. Deep Learning ;-)
  • zewemli/DeepLearning .. An OpenCL implementation of Deep Belief Networks and Restricted Boltzmann Machines

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TensorFlow

TensorFlow 是由Google Brain 团队开发的开源机器学习框架,广泛应用于深度学习研究和生产环境。 它提供了一个灵活的平台,用于构建和训练各种机器学习模型

As an AI language model, I can suggest the following steps to learn deep learning: 1. Learn the basics of machine learning: Before diving into deep learning, it is essential to understand the basics of machine learning, including supervised and unsupervised learning, regression, clustering, and classification. 2. Learn programming languages: To work with deep learning, one needs to have a strong foundation in programming languages such as Python and R, as most of the deep learning libraries are written in these languages. 3. Understand the mathematics behind deep learning: Deep learning involves a lot of math, including linear algebra, calculus, and probability. Understanding these concepts will help you better understand the algorithms used in deep learning. 4. Choose a deep learning framework: Popular deep learning frameworks include Tensorflow, Keras, PyTorch, and Caffe. Choose one and learn it. 5. Practice with datasets: Work with datasets to understand how deep learning works in practice. Kaggle is a great platform to get started with real-world datasets. 6. Read research papers: Read research papers to stay up-to-date with the latest advancements in deep learning. 7. Join communities: Join online communities such as Reddit, Discord, or GitHub to connect with other deep learning enthusiasts and learn from them. 8. Build projects: Building projects is the best way to learn deep learning. Start with simple projects and gradually move on to more complex ones. Remember, deep learning is a vast field, and it takes time and effort to master it. Keep practicing, and you will get there.
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