418 Open source projects that are alternatives of or similar to Nettack

JeVois smart machine vision framework

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

Python Adaptive Signal Processing

RObust document image BINarization

AutoML frAmework for Neural Networks

A High-Quality PyTorch Implementation of "Globally and Locally Consistent Image Completion".

Gradient Origin Networks - a new type of generative model that is able to quickly learn a latent representation without an encoder

Julia port of the Python autograd package.

A tool for retrosynthetic planning

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

Discovering Neural Wirings (https://arxiv.org/abs/1906.00586)

An open source library for artificial neural networks.

Lacmus is a cross-platform application that helps to find people who are lost in the forest using computer vision and neural networks.

Efficient Spiking Neural Network framework, built on top of PyTorch for GPU acceleration

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

A lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

🐰 Python lib for remo - the app for annotations and images management in Computer Vision

A collection of deep learning models implemented in PyTorch

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

Implementation of simple autoencoders networks with Keras

Pytorch implementation of Block Neural Autoregressive Flow

Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.

machine learning and deep learning tutorials, articles and other resources

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

Unity Machine Learning Agents Toolkit

Computational Cognitive Neuroscience, Fourth Edition

Learn to Build a Machine Learning Application from Top Articles

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

Machine Learning for High Energy Physics.

Probabilistic time series modeling in Python

A tool for automatic phoneme transcription

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

Tools and libraries to run neural networks in Minecraft ⛏

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.

FastText for Node.js

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

Javascript/WebAssembly deep learning library for MLPs and convolutional neural networks

Deep Learning for Julia

Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

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

Deep Learning for Astronomers with Tensorflow

Machine Learning inference engine for Microcontrollers and Embedded devices

This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.

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

Machine reading comprehension on clinical case reports

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

A collection of Natural language processing pre-trained models.

Code for reproducing Flow ++ experiments

A tiny neural network 🧠

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

Acceleration package for neural networks on multi-core CPUs

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

A reproduction of growing neural cellular automata using PyTorch.

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

Various CNN models for CIFAR10 with Chainer

A unified ensemble framework for Pytorch to improve the performance and robustness of your deep learning model

KitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.

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

This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"

Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.