PyTorch-Tutorial - Build your neural network easy and fast

•    Jupyter

---

In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

neural-network pytorch-tutorial batch-normalization cnn rnn autoencoder pytorch regression classification batch tutorial dropout dqn reinforcement-learning gan generative-adversarial-network machine-learning

Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

•    Python

---

In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

tensorflow tensorflow-tutorials gan generative-adversarial-network rnn cnn classification regression autoencoder deep-q-network dqn machine-learning tutorial dropout neural-network

theano_lstm - :microscope: Nano size Theano LSTM module

•    Python

---

Implements most of the great things that came out in 2014 concerning recurrent neural networks, and some good optimizers for these types of networks. This module also contains the SGD, AdaGrad, and AdaDelta gradient descent methods that are constructed using an objective function and a set of theano variables, and returns an updates dictionary to pass to a theano function.

machine-learning recurrent-networks theano lstm gru adadelta dropout automatic-differentiation neural-network tutorial

lstms.pth - PyTorch implementations of LSTM Variants (Dropout + Layer Norm)

•    Python

---

Implementation of LSTM variants, in PyTorch. pip install -e .

dropout lstm pytorch gal moon rnn

essence - AutoDiff DAG constructor, built on numpy and Cython

•    Python

---

A directed acyclic computational graph builder, built from scratch on numpy and C, with auto-differentiation supported. This was not just another deep learning library, its clean code base was supposed to be read. Great for any one who want to learn about Backprop design in deep learning libraries.

machine-learning dropout lstm mnist lenet neural-turing-machines question-answering computational-graphs auto-differentiation convolutional-neural-networks convolutional-networks recurrent-neural-networks lstm-model deep-learning deep-q-network reinforcement-learning cartpole

Deep-Learning-101 - The tools and syntax you need to code neural networks from day one.

•    Jupyter

---

When I started learning deep learning I spent two weeks researching. I selected tools, compared cloud services, and researched online courses. In retrospect, I wish I could have built neural networks from day one. That’s what this article is set out to do. You don’t need any prerequisites, yet a basic understanding of Python, the command line, and Jupyter notebook will help. This is the code experiments from the article.

deep-learning tflearn dropout regularization machine-learning

Dropouts - PyTorch Implementations of Dropout Variants

•    Jupyter

---

PyTorch Implementations of Dropout Variants

dropout gaussian-dropout variational-dropout variational-inference local-reparametrization-trick bayesian-neural-networks pytorch

bnn - Bayesian Neural Network in PyTorch

•    Python

---

This is a Bayesian Neural Network (BNN) implementation for PyTorch. The implementation follows Yarin Gal's papers "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning" (see BDropout) and "Concrete Dropout" (see CDropout). This package was originally based off the work here: juancamilog/prob_mbrl.

neural-network pytorch artificial-intelligence dropout bayesian-inference bayesian-neural-networks bnn concrete-dropout