418 Open source projects that are alternatives of or similar to Keras Sharp

Machine Learning notebooks for refreshing concepts.

💥 Fast matrix-multiplication as a self-contained Python library – no system dependencies!

RadIO is a library for data science research of computed tomography imaging

Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

✨Fast Coreference Resolution in spaCy with Neural Networks

A single / multi layer / recurrent neural network written in Golang.

Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.

Scalable Implementation of Deep CFR and Single Deep CFR

Deep learning codes and projects using Python

[NO MAINTENANCE INTENDED] A PyTorch implementation of CapsNet architecture in the NIPS 2017 paper "Dynamic Routing Between Capsules".

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

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

DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

Machine Learning inference engine for Microcontrollers and Embedded devices

Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series

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.

Train an RL agent to localize actively (PyTorch)

🛠 All-in-one web-based IDE specialized for machine learning and data science.

A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing models.

Probabilistic time series modeling in Python

My personal notes, presentations, and notebooks on everything Deep Learning.

Deep Learning for Julia

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

My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge.

Easy whole-brain modeling for computational neuroscientists 🧠💻👩🏿‍🔬

An easy-to-use interface for measuring uncertainty and robustness.

A Keras CTC implementation of Baidu's DeepSpeech for model experimentation

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (official repository)

A neural network for end-to-end music source separation

A collection of common algorithms and data structures implemented in java, c++, and python.

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

Neural Networks that invent their own encryption 🔑

A P300 online spelling mechanism for Emotiv headsets. It's completely written in Node.js, and the GUI is based on Electron and Vue.

🍃 Using a Neural Network to improve web accessibility in JavaScript.

PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）

Just another DL library

Code for reproducing Flow ++ experiments

Bayesian Deep Learning: A Survey

PyTorch 101 series covering everything from the basic building blocks all the way to building custom architectures.

A repository of state-of-the-art deep learning methods in computer vision

Various CNN models for CIFAR10 with Chainer

A utility library around PyTorch

Computational Cognitive Neuroscience, Fourth Edition

All about attention in neural networks. Soft attention, attention maps, local and global attention and multi-head attention.

Machine Learning for High Energy Physics.

A library for prototyping realtime hand detection (bounding box), directly in the browser.

A tool for automatic phoneme transcription

List of articles related to deep learning applied to music

Tools and libraries to run neural networks in Minecraft ⛏

A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

Spying using Smartwatch and Deep Learning

The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others.

Dual-Stage Attention-Based Recurrent Neural Net for Time Series Prediction

a toolkit for pose estimation using deep learning

Gradient based Hyperparameter Tuning library in PyTorch

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

🟣 Object detection of abstract shapes with neural networks