awesome-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

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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 qml

igel - a delightful machine learning tool that allows you to train, test, and use models without writing code

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The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I needed a tool sometimes, which I can use to fast create a machine learning prototype. Whether to build some proof of concept or create a fast draft model to prove a point. I find myself often stuck at writing boilerplate code and/or thinking too much of how to start this.

data-science machine-learning automation neural-network scikit-learn sklearn machine-learning-algorithms artificial-intelligence neural-networks data-analysis machine-learning-library machinelearning preprocessing automl multilayer-perceptron-network scikitlearn-machine-learning multilayer-perceptron automl-api automl-algorithms automl-experiments

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

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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.

deep-learning neural-network machine-learning tensorflow artificial-intelligence data-science pytorch

Swift-Brain - Artificial intelligence/machine learning data structures and Swift algorithms for future iOS development

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The first neural network / machine learning library written in Swift. This is a project for AI algorithms in Swift for iOS and OS X development. This project includes algorithms focused on Bayes theorem, neural networks, SVMs, Matrices, etc.. Feel free to contribute.

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

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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.

machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpeg

php-ml - PHP-ML - Machine Learning library for PHP

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Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library. PHP-ML requires PHP >= 7.1.

machine-learning classification cross-validation feature-extraction artificial-intelligence neural-network data-science

Hands-On-Deep-Learning-Algorithms-with-Python - Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

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Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.

tensorflow word-embeddings gru autoencoder gans doc2vec skip-thoughts adagrad cyclegan deep-learning-mathematics capsule-network few-shot-learning quick-thought deep-learning-scratch nadam deep-learning-math lstm-math cnn-math rnn-derivation contractive-autonencoders

ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

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A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.

artificial-intelligence machine-learning prediction image-prediction python3 offline-capable imageai artificial-neural-networks algorithm image-recognition object-detection squeezenet densenet video inceptionv3 detection gpu ai-practice-recommendations

nni - An open source AutoML toolkit for neural architecture search and hyper-parameter tuning.

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NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments (e.g. local machine, remote servers and cloud). This command will start an experiment and a WebUI. The WebUI endpoint will be shown in the output of this command (for example, http://localhost:8080). Open this URL in your browser. You can analyze your experiment through WebUI, or browse trials' tensorboard.

automl deep-learning neural-architecture-search hyperparameter-optimization optimizer

netket - Machine learning algorithms for many-body quantum systems

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NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques. It is a Python library built on JAX. Netket supports MacOS and Linux. We reccomend to install NetKet using pip For instructions on how to install the latest stable/beta release of NetKet see the Getting Started section of our website.

quantum machine-learning-algorithms neural-networks restricted-boltzmann-machine rbm convolutional-neural-networks physics-simulation hamiltonian markov-chain-monte-carlo monte-carlo-methods exact-diagonalization variational-method variational-monte-carlo quantum-state-tomography complex-neural-network

CNTK - Computational Network Toolkit (CNTK)

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The Microsoft Cognitive Toolkit is a free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. It is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.

deep-learning neural-networks artificial-intelligence

gosl - Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more

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Gosl is a Go library to develop Artificial Intelligence and High-Performance Scientific Computations. The library tries to be as general and easy as possible. Gosl considers the use of both Go concurrency routines and parallel computing using the message passing interface (MPI). Gosl has several modules (sub-packages) for a variety of tasks in scientific computing, image analysis, and data post-processing.

scientific-computing visualization linear-algebra differential-equations sparse-systems plotting mkl parallel-computations computational-geometry graph-theory tensor-algebra fast-fourier-transform eigenvalues eigenvectors hacktoberfest machine-learning artificial-intelligence optimization optimization-algorithms linear-programming

H2O - Fast Scalable Machine Learning API For Smarter Applications

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H2O is for data scientists and application developers who need fast, in-memory scalable machine learning for smarter applications. H2O is an open source parallel processing engine for machine learning. Unlike traditional analytics tools, H2O provides a combination of extraordinary math, a high performance parallel architecture, and unrivaled ease of use.

artificial-intelligence neural-networks machine-learning deep-learning numerical-computation

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

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A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-network

machine_learning_basics - Plain python implementations of basic machine learning algorithms

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This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

machine-learning logistic-regression ipynb machine-learning-algorithms linear-regression perceptron python-implementations kmeans algorithm python3 neural-network k-nearest-neighbours k-nearest-neighbor k-nn neural-networks

machineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

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I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competition

WEKA Classification Algorithms

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A collection of plug-in algorithms for the WEKA machine learning workbench including artificial neural network (ANN) algorithms, and artificial immune system (AIS) algorithms.

evostra - A fast Evolution Strategy implementation in Python

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It's compatible with both python2 and python3. The input weights of the EvolutionStrategy module is a list of arrays (one array with any shape for each layer of the neural network), so we can use any framework to build the model and just pass the weights to ES.

machine-learning reinforcement-learning ai deep-learning artificial-intelligence evolutionary-algorithms evolution-strategies ai-learning

DeepNeuralClassifier - Deep neural network using rectified linear units to classify hand written symbols from the MNIST dataset

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Example scripts for a deep, feed-forward neural network have been written from scratch. No machine learning packages are used, providing an example of how to implement the underlying algorithms of an artificial neural network. The code is written in the Julia, a programming language with a syntax similar to Matlab. The neural network is trained on the MNIST dataset of handwritten digits. On the test dataset, the neural network correctly classifies 98.42 % of the handwritten digits. The results are pretty good for a fully connected neural network that does not contain a priori knowledge about the geometric invariances of the dataset like a Convolutional Neural Network would.

encog-dotnet-core

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Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Bayesian Networks, Hidden Markov Models, Genetic Programming and Genetic Algorithms are supported. Most Encog training algorithms are multi-threaded and scale well to multicore hardware. A GUI based workbench is also provided to help model and train machine learning algorithms. Encog has been in active development since 2008.