Displaying 1 to 20 from 196 results

sunfish - Sunfish: a Python Chess Engine in 111 lines of code

  •    Python

Sunfish is self contained in the sunfish.py file from the repository. I recommend running it with pypy or pypy3 for optimal performance. It is also possible to run Sunfish with a graphical interface, such as PyChess, Arena or your chess interface of choice. Sunfish' can communicate through the XBoard/CECP protocol by the command pypy -u xboard.py. Ruxy Sylwyka has a note on making it all work on Windows.

caffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.

  •    C++

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.




polyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes

  •    Python

Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

mycroft-core - Mycroft Core, the Mycroft Artificial Intelligence platform.

  •    Python

Mycroft is a hackable open source voice assistant. This script sets up dependencies and a virtualenv. If running in an environment besides Ubuntu/Debian, Arch or Fedora you may need to manually install packages as instructed by dev_setup.sh.


spaCy - 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython

  •    Python

spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 20+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration. It's commercial open-source software, released under the MIT license. 💫 Version 2.0 out now! Check out the new features here.

thinc - 🔮 spaCy's Machine Learning library for NLP in Python

  •    Assembly

Thinc is the machine learning library powering spaCy. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for spaCy v2.0. Thinc is a practical toolkit for implementing models that follow the "Embed, encode, attend, predict" architecture. It's designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences.

TransmogrifAI - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning

  •    Scala

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time. Skip to Quick Start and Documentation.

carla - Open-source simulator for autonomous driving research.

  •    C++

CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. If you want to benchmark your model in the same conditions as in our CoRL’17 paper, check out Benchmarking.

deep-neuroevolution - Deep Neuroevolution

  •    Python

Our code is based off of code from OpenAI, who we thank. The original code and related paper from OpenAI can be found here. The repo has been modified to run both ES and our algorithms, including our Deep Genetic Algorithm (DeepGA) locally and on AWS. The folder ./visual_inspector contains implementations of VINE, i.e., Visual Inspector for NeuroEvolution, an interactive data visualization tool for neuroevolution. Refer to README.md in that folder for further instructions on running and customizing your visualization. An article describing this visualization tool can be found here.

warriorjs - 🏰 An exciting game of programming and Artificial Intelligence

  •    Javascript

In WarriorJS, you are a warrior climbing a tall tower to reach The JavaScript Sword at the top level. Legend has it that the sword bearer becomes enlightened in the JavaScript language, but be warned: the journey will not be easy. On each floor, you need to write JavaScript to instruct the warrior to battle enemies, rescue captives, and reach the stairs alive... Although there is some in-game documentation, at some point you may want to visit the official docs.

guess - Libraries & tools for enabling Machine Learning driven user-experiences on the web

  •    TypeScript

Libraries and tools for enabling data-driven user-experiences on the web. Install and configure GuessPlugin - the Guess.js webpack plugin which automates as much of the setup process for you as possible.

EmojiIntelligence - Neural Network built in Apple Playground using Swift

  •    Swift

I used this challenge to learn more about neural networks and machine learning. A neural network consists of layers, and each layer has neurons. My network has three layers: an input layer, a hidden layer, and an output layer. The input to my network has 64 binary numbers. These inputs are connected to the neurons in the hidden layer. The hidden layer performs some computation and passes the result to the output layer neuron out. This also performs a computation and then outputs a 0 or a 1. The input layer doesn’t actually do anything, they are just placeholders for the input value. Only the neurons in the hidden layer and the output layer perform computations. The neurons from the input layer are connected to the neurons in the hidden layer. Likewise, both neurons from the hidden layer are connected to the output layer. These kinds of layers are called fully-connected because every neuron is connected to every neuron in the next layer. Each connection between two neurons has a weight, which is just a number. These weights form the brain of my network. For the activation function in my network, I use the sigmoid function. Sigmoid is a mathematical function. The sigmoid takes in some number x and converts it into a value between 0 and 1. That is ideal for my purposes, since I am dealing with binary numbers. This will turn a linear equation into something that is non-linear. This is important because without this, the network wouldn’t be able to learn any interesting things. I have already mentioned that the input to this network are 64 binary numbers. I resize the drawn image to 8x8 pixels which makes together 64 pixels. I go through the image and check each pixel if the pixel has a pink color I add a 1 to my array else I add a 0. At the end I will have 64 binary numbers which I can add to my input layer.

PredictionIO - Machine Learning Server

  •    Scala

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. It helps to predict user behaviors.

Mycroft - an Artificial intelligence for everyone

  •    C

Mycroft is an Artificial intelligence for everyone. It uses open software to process natural language, determine your intent and take action. It can integrate a host of professional functions – Control scenes to conserve power, grant office access with your voice. It can control all of your media and devices with the sound of your voice. Adjust your thermostat, turn on your lights, water your lawn, play your favorite movie and lot more.

Snorkel - A system for quickly generating training data with weak supervision

  •    Jupyter

Snorkel is a system for rapidly creating, modeling, and managing training data, currently focused on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain. <BR><BR> Today's state-of-the-art machine learning models require massive labeled training sets--which usually do not exist for real-world applications. Instead, Snorkel is based around the new data programming paradigm, in which the developer focuses on writing a set of labeling functions, which are just scripts that programmatically label data. The resulting labels are noisy, but Snorkel automatically models this process—learning, essentially, which labeling functions are more accurate than others—and then uses this to train an end model (for example, a deep neural network in TensorFlow).