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

•    HTML

A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language).

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

•    Python

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.

genann - simple neural network library in ANSI C

•    C

Genann is a minimal, well-tested library for training and using feedforward artificial neural networks (ANN) in C. Its primary focus is on being simple, fast, reliable, and hackable. It achieves this by providing only the necessary functions and little extra. Genann is self-contained in two files: genann.c and genann.h. To use Genann, simply add those two files to your project.

Top-Deep-Learning - Top 200 deep learning Github repositories sorted by the number of stars.

•    Python

Trending deep learning Github repositories can be found here. Hint: This will be updated regularly.

Applying_EANNs - A 2D Unity simulation in which cars learn to navigate themselves through different courses

•    ASP

Cars have to navigate through a course without touching the walls or any other obstacles of the course. A car has five front-facing sensors which measure the distance to obstacles in a given direction. The readings of these sensors serve as the input of the car's neural network. Each sensor points into a different direction, covering a front facing range of approximately 90 degrees. The maximum range of a sensor is 10 unity units. The output of the Neural Network then determines the car’s current engine and turning force. If you would like to tinker with the parameters of the simulation, you can do so in the Unity Editor. If you would simply like to run the simulation with default parameters, you can start the built file [Builds/Applying EANNs.exe](Builds/Applying EANNs.exe).

neupy - NeuPy is a Python library for Artificial Neural Networks and Deep Learning.

•    Python

About a year ago, it has been officially announced that Theano will stop support for their library. They don't add new features anymore and soon, they will stop adding bug fixes to the library. NeuPy cannot evolve having large number of features that depend on the dead library. For this reason, NeuPy was moved to the Tensorflow. All the Theano based code has been fully migrated to Tenorflow and it can be tested from the release/v0.7.0 branch.

dll - Deep Learning Library (DLL) for C++ (ANNs, CNNs, RBMs, DBNs...)

•    C++

DLL is a library that aims to provide a C++ implementation of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) and their convolution versions as well. It also has support for some more standard neural networks. Note: When you clone the library, you need to clone the sub modules as well, using the --recursive option.

awesome-ai-awesomeness - A curated list of awesome awesomeness about artificial intelligence

A curated list of awesome awesomeness about artificial intelligence(AI). If you want to contribute to this list (please do), send me a pull request.

Perfect-TensorFlow - TensorFlow C API Class Wrapper in Server Side Swift.

•    Swift

This project is an experimental wrapper of TensorFlow C API which enables Machine Learning in Server Side Swift.This package builds with Swift Package Manager and is part of the Perfect project but can also be used as an independent module.

fashion - The Fashion-MNIST dataset and machine learning models.

•    R

Training AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.

neatjs - A javascript implementation of Neuro Evolution of Augmenting Topologies

•    Javascript

NeuroEvolution of Augmenting Topologies (NEAT) implemented in Javascript (with tests done in Mocha for verification). Can be used as a node module or in a browser

ann.apl - Simple Neural Network Examples in 8 lines APL

•    APL

Simple Neural Network Examples in 8 lines APL

Trending-Deep-Learning - Top 100 trending deep learning repositories sorted by the number of stars gained on a specific day

•    Python

Repositories with 50000 stars or more are excluded. Top deep learning Github repositories can be found here.

LSTM-Text-Generation - Tons of fun with text and recurrent neural networks! Let your computer read a book and tell you its own story

•    Hy

During the time that I was writing my bachelor's thesis Sequence-to-Sequence Learning of Financial Time Series in Algorithmic Trading (in which I used LSTM-based RNNs for modeling the thesis problem), I became interested in natural language processing. After reading Andrej Karpathy's blog post titled The Unreasonable Effectiveness of Recurrent Neural Networks, I decided to give text generation using LSTMs for NLP a go. Although slightly trivial, the project still comprises an interesting program and demo, and gives really interesting (and sometimes very funny) results. I implemented the program over the course of a weekend in Hy (a LISP built on top of Python) using Keras and TensorFlow. You can train the model on any text sources you like. Remember to give it enough time to go over at least fifty epochs, otherwise the generated text will not be very interesting, rather seemingly random garbage.

Sequence-to-Sequence-Learning-of-Financial-Time-Series-in-Algorithmic-Trading - My bachelor's thesis—analyzing the application of LSTM-based RNNs on financial markets

•    TeX

This is my bachelor's thesis that I wrote over the course of two months during my final year of studies, earning my Bachelor of Science in Computer Science degree. The thesis was co-authored by my good friend Tobias Ånhed. Click here for revised edition on DiVA.

pyERA - Python implementation of the Epigenetic Robotic Architecture (ERA)

•    Python

Because the different modules are standalone you can use pyERA for building SOM using only the som.py class. Feel free to fork the project and add your own stuff. Any feedback is appreciated. The Epigenetic Robotic Architecture (ERA) is a hybrid behavior-based robotics and neural architecture purposely built to implement embodied principles in cognitive development. This architecture has been already tested in a variety of cognitive and developmental tasks directly modeling child psychology data. The ERA architecture uses a behaviour-based subsumption mechanism to handle the integration of competing sensorimotor input. The learning system is based on an ensemble of pre-trained SOMs connected via Hebbian weights. The basic unit of the ERA architecture is formed by the structured association of multiple self-organizing maps. Each SOM receives a subset of the input available to that unit and is typically partially prestabilized using random input distributed across the appropriate ranges for those inputs. In the simplest case, the ERA architecture comprises of multiple SOMs, each receiving input from a different sensory modality, and each with a single winning unit. Each of these winning units is then associated to the winning unit of a special “hub” SOM using a bidirectional connection weighted with positive Hebbian learning.

python-neuron - Neuron class provides LNU, QNU, RBF, MLP, MLP-ELM neurons

•    Python

Neuron class provides LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neurons learned with Gradient descent or LeLevenberg–Marquardt algorithm. This class is suitable for prediction on time series. Neuron class needs pandas and numpy to work propertly.

goNEAT - The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to grow and teach Artificial Neural Networks without back propagation

•    Go

This repository provides implementation of NeuroEvolution of Augmenting Topologies (NEAT) method written in Go language. The Neuroevolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuroevolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).

goNEAT_NS - This project provides GOLang implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima

•    Go

This repository provides implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization implemented in GoLang. The Neuro-Evolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuro-Evolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).

Model-Playgrounds - A project developed and maintained as part of the aim at bringing current capabilities in machine learning and artificial intelligence into practical use for non-programmers and average computer users

•    Python

        The Machine Learning Model Playgrounds is a project that is part of the dream of a team of Moses Olafenwa and John Olafenwa to bring current capabilities in machine learning and artificial intelligence into practical use for non-programmers and average computer users. This project is the first step in what we hope will become mainstream application in modern technology in which Computers, Smartphones, Edge Devices and Systems will have in-built state-of-the-art Machine Learning and Artificial Intelligence capabilities without having to connect to cloud based services.         The Machine Learning Model Playgrounds is a series of Windows programs built using pure python libraries and code. Each of the programs is a user-friendly demo of Image Classification powered by a specific image classification model of popular Machine Learning Algorithms trained on the ImageNet (1000 object classes ) dataset. Each program provides a user interface where users can select a picture from their Windows system folder while the program process the selected picture and give top-10 possible results of the objects detected with percentage probability per each result.           This repository contains the source code, models and builds of each of the programs in the Model Playgrounds series. It is provided to allow other developers outside our team to adapt, modify or extend the code to produce more programs that may be specific to a social, business, economic or scientific need.         The dependencies used for this project are listed below:     - Python 3.5.2     - Tensorflow 1.4.0     - Keras 2.0.8     - Numpy 1.13.1     - Scipy 0.19.1     - wxPython 4.0.0 Below you will find the details and pictures of each of the programs in the series.           The ResNet Playground is powered by the ResNet50 model trained on the ImageNet dataset. You can find its source codes in the resnet-playground folder of this repository or follow this link. You can also download the Windows Installer for the program in the Release section of this project or follow this link.           This program is a Windows 64-bit software that can be installed on Windows 7 and later versions of the Operating System. It has an installer size of 227mb and install size of 690mb. The program was compiled using PyInstaller 3.3 for Python 3.5 .