579 Open source projects that are alternatives of or similar to Keras Gp

Implementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".

A new approach for the real time 3D semantic segmentation based on feature abstract and deep learning method

Bayesian Coherent Point Drift (BCPD/BCPD++); Source Code Available

Udacity Self-Driving Car Engineer Nanodegree. Project: Build a Traffic Sign Recognition Classifier

Neural Networks that invent their own encryption 🔑

18.337 - Parallel Computing and Scientific Machine Learning

Aspect-Based Sentiment Analysis

Machine Learning inference engine for Microcontrollers and Embedded devices

Sets up a VM with Keras, TensorFlow, TFLearn and Theano installed

Code for "A-NICE-MC: Adversarial Training for MCMC"

3-day dive into deep learning at csc

End-to-end training of sparse deep neural networks with little-to-no performance loss.

Train Neuronal networks to automate your home

Xaynet represents an agnostic Federated Machine Learning framework to build privacy-preserving AI applications.

Daily Dose of Neural Network that Everyone Needs

CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

A curated list of awesome resources related to capsule networks

AETROS CLI + SDK. Command line application to manage/monitor machine learning training in AETROS Trainer

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

Anomaly detection for temporal data using LSTMs

An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.

A general, modular, and programmable architecture search framework

Descriptive Deep Learning

Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron

Efficient Multi-Scale 3D Convolutional Neural Network for Segmentation of 3D Medical Scans

Periodic Spatial Generative Adversarial Networks

Implementation of Quickdraw - an online game developed by Google

A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.

Disclaimer: This is not an official Google product.

Deepfakes Software For All

A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends

A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models!

Training neural models with structured signals.

💻 Make machines learn so that you don't have to struggle to program them; The ultimate list

Brain Modeling Toolkit

Next RecSys Library

GPstuff - Gaussian process models for Bayesian analysis

Setup and customize deep learning environment in seconds.

👷‍♂️ A simple package for extracting useful features from character objects 👷‍♀️

머신러닝 입문자 혹은 스터디를 준비하시는 분들에게 도움이 되고자 만든 repository입니다. (This repository is intented for helping whom are interested in machine learning study)

Gaussian processes in TensorFlow

Arm NN ML Software. The code here is a read-only mirror of https://review.mlplatform.org/admin/repos/ml/armnn

Japanese Riichi Mahjong AI agent. (Feel free to extend this agent or develop your own agent)

Code and other material for the book "Deep Learning and the Game of Go"

Spokestack is a library that allows a user to easily incorporate a voice interface into any Python application.

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. Now almost entirely superseded by the models-by-example repo.

TensorFlow 101: Introduction to Deep Learning for Python Within TensorFlow

R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation

🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained

Video Platform for Action Recognition and Object Detection in Pytorch

A Visual Exploration of Gaussian Processes

Handwritten Digit Recognition using Machine Learning and Deep Learning

An open-source platform for automating tasks using machine learning models

Self-organizing maps in Go

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

LaTeX/PDF + Epub version of the online book (http://neuralnetworksanddeeplearning.com) ”Neural Networks and Deep Learning“ by Michael Nielsen (@mnielsen)

A PyTorch Library for Accelerating 3D Deep Learning Research

A clear, concise, simple yet powerful and efficient API for deep learning.