Displaying 1 to 8 from 8 results

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.

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.

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.

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.

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.

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.

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