Displaying 1 to 20 from 200 results

TensorFlowSharp - TensorFlow API for .NET languages

  •    CSharp

This surfaces the C API as a strongly-typed .NET API for use from C# and F#.The API binding is pretty much done, and at this point, I am polishing the API to make it more pleasant to use from C# and F# and resolving some of the kinks and TODO-items that I left while I was doing the work.

keras - Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

  •    Python

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

TensorFlowOnSpark - TensorFlowOnSpark brings TensorFlow programs onto Apache Spark clusters

  •    Python

TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from deep learning framework TensorFlow and big-data frameworks Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers.TensorFlowOnSpark was developed by Yahoo for large-scale distributed deep learning on our Hadoop clusters in Yahoo's private cloud.

deepvariant - DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data

  •    Python

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.DeepVariant is a suite of Python/C++ programs that run on any Unix-like operating system. For convenience the documentation refers to building and running DeepVariant on Google Cloud Platform, but the tools themselves can be built and run on any standard Linux computer, including on-premise machines. Note that DeepVariant currently requires Python 2.7 and does not yet work with Python 3.




DeepSpeech - A TensorFlow implementation of Baidu's DeepSpeech architecture

  •    C

Project DeepSpeech is an open source Speech-To-Text engine. It uses a model trained by machine learning techniques, based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow project to make the implementation easier.

keras-rl - Deep Reinforcement Learning for Keras.

  •    Python

keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.


lucid - A collection of infrastructure and tools for research in neural network interpretability.

  •    Jupyter

Lucid is a collection of infrastructure and tools for research in neural network interpretability. In particular, it provides state of the art implementations of feature visualization techniques, and flexible abstractions that make it very easy to explore new research directions.

rust - Rust language bindings for TensorFlow

  •    Rust

TensorFlow Rust provides idiomatic Rust language bindings for TensorFlow. Notice: This project is still under active development and not guaranteed to have a stable API. This is especially true because the underlying TensorFlow C API has not yet been stabilized as well.

handson-ml - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow

  •    Jupyter

First, you will need to install git, if you don't have it already. If you want to go through chapter 16 on Reinforcement Learning, you will need to install OpenAI gym and its dependencies for Atari simulations.

TensorFlow-Book - Accompanying source code for Machine Learning with TensorFlow

  •    Jupyter

This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

tensorpack - A Neural Net Training Interface on TensorFlow

  •    Python

Tensorpack is a training interface based on TensorFlow. It's Yet Another TF high-level API, with speed, readability and flexibility built together.

CADL - Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

  •    Jupyter

This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for the first of three Kadenze Academy courses on Creative Applications of Deep Learning w/ Tensorflow. It also contains a python package containing all the code developed during all three courses. The first course makes heavy usage of Jupyter Notebook. This will be necessary for submitting the homeworks and interacting with the guided session notebooks I will provide for each assignment. Follow along this guide and we'll see how to obtain all of the necessary libraries that we'll be using. By the end of this, you'll have installed Jupyter Notebook, NumPy, SciPy, and Matplotlib. While many of these libraries aren't necessary for performing the Deep Learning which we'll get to in later lectures, they are incredibly useful for manipulating data on your computer, preparing data for learning, and exploring results.

luminoth - Deep Learning toolkit for Computer Vision

  •    Python

Luminoth is an open source toolkit for computer vision. Currently, we support object detection, but we are aiming for much more. It is built in Python, using TensorFlow and Sonnet. Read the full documentation here.

generative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

  •    Python

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

keras-js - Run Keras models in the browser, with GPU support using WebGL

  •    Javascript

**This project is no longer active. Please check out TensorFlow.js.** The Keras.js demos still work but is no longer updated. Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc.

deep-learning-book - Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

  •    Jupyter

Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

python-machine-learning-book-2nd-edition - The "Python Machine Learning (2nd edition)" book code repository and info resource

  •    Jupyter

Python Machine Learning, 2nd Ed. To access the code materials for a given chapter, simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub.





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