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sunfish - Sunfish: a Python Chess Engine in 111 lines of code
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.
sunfish chess-engine chess-ai aibrain.js - 🤖 Neural networks in JavaScript
brain.js is a library of Neural Networks written in JavaScript. 💡 Note: This is a continuation of the harthur/brain repository (which is not maintained anymore). For more details, check out this issue.
neural-network brain recurrent-neural-networks easy-to-use api web nodejs browser convolutional-neural-networks node stream ai artificial-intelligence brainjs brain.js feed-forward classifier neural network neural-networks machine-learning synapse recurrent long-short-term-memory gated-recurrent-unit rnn lstm gruBigDL - BigDL: Distributed Deep Learning Library for Apache Spark
A distributed deep learning library for Apache Spark.
deep-learning spark neural-network big-data hadoop keras aicaffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.
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.
deep-learning machine-learning ai artificial-intelligence caffe2 deep-neural-networks mlpolyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes
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.
deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ai dl ml k8sgold-miner - 🥇掘金翻译计划，可能是世界最大最好的英译中技术社区，最懂读者和译者的翻译平台：
🥇掘金翻译计划，可能是世界最大最好的英译中技术社区，最懂读者和译者的翻译平台：
android translation ios frontend react tutorials ai tensorflowspaCy - 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
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.
natural-language-processing data-science big-data machine-learning cython nlp artificial-intelligence ai spacy nlp-library neural-network neural-networks deep-learningthinc - 🔮 spaCy's Machine Learning library for NLP in Python
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.
machine-learning deep-learning artificial-intelligence ai nlp natural-language-processing spacy machine-learning-libraryTransmogrifAI - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning
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.
ml automl transformations estimators dsl pipelines machine-learning salesforce einstein features feature-engineering spark sparkml ai automated-machine-learning transmogrification transmogrify structured-data transformersdvc - ⚡️ML models version control, make them shareable and reproducible
It aims to replace tools like Excel and Docs that are being commonly used as a knowledge repo and a ledger for the team, ad-hoc scripts to track and move deploy different model versions, ad-hoc data file suffixes and prefixes. DVC is compatible with Git for storing code and the dependency graph (DAG), but not data files cache. To store and share data files cache DVC supports remotes - any cloud (S3, Azure, Google Cloud, etc) or any on-premise network storage (via SSH, for example).
data-science machine-learning reproducibility data-version-control git developer-tools dvc collaboration aiMycroft - an Artificial intelligence for everyone
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.
ai nlp voice-assistant raspberry-pi natural-language-processing natural-language voice-as-an-interface artificial-intelligence personal-assistant speech-recognitioncarla - Open-source simulator for autonomous driving research.
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.
simulator autonomous-vehicles autonomous-driving research ai artificial-intelligence computer-vision deep-learning deep-reinforcement-learning imitation-learning self-driving-car ue4 unreal-engine-4 cross-platformcaffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.
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.
deep-learning machine-learning ai artificial-intelligence caffe2 deep-neural-networks mldeep-neuroevolution - Deep Neuroevolution
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.
deep-neuroevolution ai machine-learning reinforcement-learningwarriorjs - 🏰 An exciting game of programming and Artificial Intelligence
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.
warriorjs warrior epic battle game learn test js nodejs ai artificial-intelligence skills sharpenguess - Libraries & tools for enabling Machine Learning driven user-experiences on the web
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.
machine-learning performance web-performance prefetch prerender bundling webpack ml ai analytics recommendationawesome-quantum-machine-learning - Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language).
quantum quantum-computing quantum-programming-language machine-learning artificial-intelligence artificial-neural-networks tensorflow awesome-list awesome machine-learning-algorithms knn-classification fcm kmeans hmm-model qubits ant-colony-optimization ai quantum-ai qmlEmojiIntelligence - Neural Network built in Apple Playground using 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.
neural network apple playground ipad ai machine-learning artificial-intelligenceredtail - Perception and AI components for autonomous mobile robotics.
Autonomous visual navigation components for drones and ground vehicles using deep learning. Refer to wiki for more information on how to get started. This project contains deep neural networks, computer vision and control code, hardware instructions and other artifacts that allow users to build a drone or a ground vehicle which can autonomously navigate through highly unstructured environments like forest trails, sidewalks, etc. Our TrailNet DNN for visual navigation is running on NVIDIA's Jetson embedded platform. Our arXiv paper describes TrailNet and other runtime modules in detail.
ai computer-vision deep-learning robotics artificial-intelligence drones jetson
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