Deep Learning Papers Reading Roadmap

#ICYMI #DeepLearning Papers Reading Roadmap

  • [pdf](ResNet,Very very deep networks, CVPR best paper)

    [8] Hinton, Geoffrey, et al. “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.”

  • “Speech recognition with deep recurrent neural networks.”
  • [pdf]

    [11] Sak, Haşim, et al. “Fast and accurate recurrent neural network acoustic models for speech recognition.”

  • [pdf] (Google Speech Recognition System)

    [12] Amodei, Dario, et al. “Deep speech 2: End-to-end speech recognition in english and mandarin.”

  • [pdf] (Baidu Speech Recognition System)

    After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues.


The roadmap is constructed in accordance with the following four guidelines: from outline to detail; from old to state-of-the-art; from generic to specific areas; focus on state-of-the-art.

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Deep Learning Libraries by Language

#DeepLearning Libraries by Language

  • darch package can be used for generating neural networks with many layers (deep architectures).
  • Convnet.js is a Javascript library for training Deep Learning models (mainly Neural Networks) entirely in a browser.
  • DeepLearning is deep learning library, developed with C++ and python.
  • Pylearn2 is a library that wraps a lot of models and training algorithms such as Stochastic Gradient Descent that are commonly used in Deep Learning.
  • Intel® Deep Learning Framework provides a unified framework for Intel® platforms accelerating Deep Convolutional Neural Networks.

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@analyticbridge: “#DeepLearning Libraries by Language”


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Python

Theano is a python library for defining and evaluating mathematical expressions with numerical arrays.

Keras is a mi…


Deep Learning Libraries by Language