Another Dev Notes

Resources for Applying Deep Learning in NLP

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Deep Learning Basics

Basics

Neural Networks, Manifolds, and Topology > Note

Deep Learning, NLP, and Representations

Calculus on Computational Graphs: Backpropagation

Hacker’s guide to Neural Networks

Hardware Configuration

The $1700 great Deep Learning box: Assembly, setup and benchmarks

My Deep Learning Dev Environment

Tensorflow Docker Image

Using TensorFlow via Docker

NVIDIA Docker

RNN

Basics

The Unreasonable Effectiveness of Recurrent Neural Networks

Recurrent Neural Networks Tutorial by Denny Britz

  1. Introduction To RNNs

  2. Implementing A Rnn With Python, Numpy And Theano

  3. Backpropagation Through Time And Vanishing Gradients

  4. Implementing A Gru/Lstm Rnn With Python And Theano

Attention and Augmented Recurrent Neural Networks

The Unreasonable Effectiveness of Recurrent Neural Networks

LSTM

Understanding LSTM Networks

Rnns In Tensorflow, A Practical Guide And Undocumented Features

Sequence Tagging with Tensorflow

A noob’s guide to implementing RNN-LSTM using Tensorflow

Written Memories: Understanding, Deriving and Extending the LSTM

CNN

Understanding Convolutions

Conv Nets: A Modular Perspective

Groups & Group Convolutions

Understanding Convolutional Neural Networks for NLP

Implementing A Cnn For Text Classification In Tensorflow

Notes

1

It uses visualizations to explain the mechanics of how a low-dimensional deep neural network works, in order to provide a possible way to understand how a deeper network works.