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

SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity

Visualization toolkit for neural networks in PyTorch! Demo -->

Deep learning codes and projects using Python

TensorFlow implementation of the Value Iteration Networks (NIPS '16) paper

End-To-End Molecular Dynamics (MD) Engine using PyTorch

Build Graph Nets in Tensorflow

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

Enhance your application with the ability to see and interact with humans using any RGB camera.

Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://nlproc.info

Monthly Series - Top 10 Machine Learning Articles

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

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

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

Message Passing Neural Networks for Molecule Property Prediction

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

This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in …

🦷 Detection of restorations and treatments on dental x-rays in Tensorflow, using Faster-RCNN

A collection of Bayesian data analysis recipes using PyMC3

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

Lab Materials for MIT 6.S191: Introduction to Deep Learning

🚗 🏎️ 🎮 online 3D multiplayer neural networks based racing game

This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai

Machine Learning inference engine for Microcontrollers and Embedded devices

A wrapper around tensor2tensor to flexibly train, interact, and generate data for neural chatbots.

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

DN2A - Digital Neural Networks Architecture

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

PyTorch Tutorial for Deep Learning Researchers

Sarcasm detection on tweets using neural network

An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.

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.

PyTorch implementations of algorithms for density estimation

An Echo State Network module for PyTorch.

Ruby library for interfacing with FANN (Fast Artificial Neural Network)

Train an RL agent to localize actively (PyTorch)

Learning to Learn in TensorFlow

Face Attribute Prediction on CelebA benchmark with PyTorch Implementation

由淺入深的深度學習資源 Collection of deep learning materials for everyone

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

Experimental keras implementation of novel neural network structures

A list of papers and other resources on deep learning with anime style images.

Implementation of Apple's Learning from Simulated and Unsupervised Images through Adversarial Training

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.

Lightweight library to build and train neural networks in Theano

An Elegant Neural Network User Interface to build drag-and-drop neural networks, train in the browser, visualize during training, and export to Python.

Repo for the Deep Reinforcement Learning Nanodegree program

Probabilistic time series modeling in Python

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

Awesome list of software that I use to do research in medical imaging.

an Open Course Platform for Stanford CS224n (2020 Winter)

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

A simple JavaScript neural network framework.

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

it's a small library for running trained Keras 2 models from a native C++ code.

Finding the genre of a song with Deep Learning