Displaying 1 to 20 from 99 results

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET


The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.


Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with.

seq2seq - A general-purpose encoder-decoder framework for Tensorflow


A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.The official code used for the Massive Exploration of Neural Machine Translation Architectures paper.

PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration


PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.




sling - SLING - A natural language frame semantics parser


SLING is a parser for annotating text with frame semantic annotations. It is trained on an annotated corpus using Tensorflow and Dragnn.The parser is a general transition-based frame semantic parser using bi-directional LSTMs for input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. It is a jointly trained model using only the text tokens as input and the transition system has been designed to output frame graphs directly without any intervening symbolic representation.

Forge - A neural network toolkit for Metal


Forge is a collection of helper code that makes it a little easier to construct deep neural networks using Apple's MPSCNN framework. Conversion functions. MPSCNN uses MPSImages and MTLTextures for everything, often using 16-bit floats. But you probably want to work with Swift [Float] arrays. Forge's conversion functions make it easy to work with Metal images and textures.

IAMDinosaur - 🦄 An Artificial Inteligence to teach Google's Dinosaur to jump cactus


A simple artificial intelligence to teach Google Chrome's offline dinosaur to jump cactus, using Neural Networks and a simple Genetic Algorithm.Install Node.js on your computer.

ELF - An End-To-End, Lightweight and Flexible Platform for Game Research


ELF is an Extensive, Lightweight and Flexible platform for game research, in particular for real-time strategy (RTS) games. On the C++-side, ELF hosts multiple games in parallel with C++ threading. On the Python side, ELF returns one batch of game state at a time, making it very friendly for modern RL. In comparison, other platforms (e.g., OpenAI Gym) wraps one single game instance with one Python interface. This makes concurrent game execution a bit complicated, which is a requirement of many modern reinforcement learning algorithms. Besides, ELF now also provides a Python version for running concurrent game environments, by Python multiprocessing with ZeroMQ inter-process communication. See ./ex_elfpy.py for a simple example.


brain - [UNMAINTAINED] Simple feed-forward neural network in JavaScript


Download the latest brain.js. Training is computationally expensive, so you should try to train the network offline (or on a Worker) and use the toFunction() or toJSON() options to plug the pre-trained network in to your website. Use train() to train the network with an array of training data. The network has to be trained with all the data in bulk in one call to train(). The more training patterns, the longer it will probably take to train, but the better the network will be at classifiying new patterns.

tfjs - A WebGL accelerated, browser based JavaScript library for training and deploying ML models.


TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models. Develop ML in the Browser Use flexible and intuitive APIs to build models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.

tfjs-core - WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.


NOTE: Building on the momentum of deeplearn.js, we have joined the TensorFlow family and we are starting a new ecosystem of libraries and tools for Machine Learning in Javascript, called TensorFlow.js. This repo moved from PAIR-code/deeplearnjs to tensorflow/tfjs-core. A part of the TensorFlow.js ecosystem, this repo hosts @tensorflow/tfjs-core, the TensorFlow.js Core API, which provides low-level, hardware-accelerated linear algebra operations and an eager API for automatic differentiation.

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


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.

have-fun-with-machine-learning - An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks


Also available in Chinese (Traditional). This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn’t require a PhD, and you don’t need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic.

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


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.

insightface - Face Recognition Project on MXNet


2018.03.14: train_softmax.py(and a new image_iter.py) is now more clear after removing experimental code. All experimental and unstable test will be put on train.py and data.py. 2018.02.16: We put the MegaFace noise list in this repo. Please refer to [https://github.com/deepinsight/insightface/blob/master/src/megaface] for detail.

gensim - Topic Modelling for Humans


Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. If this feature list left you scratching your head, you can first read more about the Vector Space Model and unsupervised document analysis on Wikipedia.

chainer - A flexible framework of neural networks for deep learning


Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter. The stable version of current Chainer is separated in here: v3.

mace - MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms


MACE Model Zoo contains several common neural networks and models which will be built daily against a list of mobile phones. The benchmark results can be found in the CI result page (choose the latest passed pipeline, click release step and you will see the benchmark results). Any kind of contribution is welcome. For bug reports, feature requests, please just open an issue without any hesitation. For code contributions, it's strongly suggested to open an issue for discussion first. For more details, please refer to the contribution guide.

Deeplearning4J - Neural Net Platform in Java and Scala


Deeplearning4J is an open source, distributed neural net library written in Java and Scala. It integrates with Hadoop and Spark and runs on several backends that enable use of CPUs and GPUs. It provides versatile n-dimensional array class for Java and Scala.