开始学习深度学习和循环神经网络Some starting points for deep learning and RNNs

最新推荐文章于 2025-09-07 17:43:44 发布

转载最新推荐文章于 2025-09-07 17:43:44 发布 · 193 阅读

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本文汇总了多位知名专家如Bengio、Hinton等人的Reddit AMA问答链接，提供了Coursera、Stanford等平台上的深度学习课程及教程视频，推荐了Nielsen的在线书籍和其他博客文章以帮助理解MLPs、CNNs和RNNs等概念。此外，还列举了Theano、TensorFlow、PyTorch等流行的深度学习软件库。

Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI have done reddit AMA's. These are nice places to start to get a Zeitgeist of the field.

Hinton and Ng lectures at Coursera, UFLDL, CS224d and CS231n at Stanford, the deep learning course at Udacity, and the summer school at IPAM have excellent tutorials, video lectures and programming exercises that should help you get started.

The online book by Nielsen, notes for CS231n, and blogs by Karpathy, Olah and Britz have clear explanations of MLPs, CNNs and RNNs. The tutorials at UFLDL and deeplearning.net give equations and code. The encyclopaedic book by Goodfellow et al. is a good place to dive into details. I have a draft book in progress.

Theano, Torch, Caffe, ConvNet, TensorFlow, MXNet, CNTK, Veles, CGT, Neon, Chainer, Blocks and Fuel, Keras, Lasagne, Mocha.jl, Deeplearning4j, DeepLearnToolbox, Currennt, Project Oxford, Autograd ( for Torch), Warp-CTC are some of the many deep learning software libraries and frameworks introduced in the last 10 years. convnet-benchmarks and deepframeworks compare the performance of many existing packages. I am working on developing an alternative, Knet.jl, written in Julia supporting CNNs and RNNs on GPUs and supporting easy development of original architectures. More software can be found at deeplearning.net.

Deeplearning.net and homepages of Bengio, Schmidhuber have further information, background and links.

from: http://www.denizyuret.com/2014/11/some-starting-points-for-deep-learning.html