972 Open source projects that are alternatives of or similar to Wyrm

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

Build Graph Nets in Tensorflow

💫 Industrial-strength Natural Language Processing (NLP) in Python

Amacımız Türkçe NLP literatüründeki birçok farklı sorunu bir arada çözebilen, eşsiz yaklaşımlar öne süren ve literatürdeki çalışmaların eksiklerini gideren open source bir yazım destekleyicisi/denetleyicisi oluşturmak. Kullanıcıların yazdıkları metinlerdeki yazım yanlışlarını derin öğrenme yaklaşımıyla çözüp aynı zamanda metinlerde anlamsal analizi de gerçekleştirerek bu bağlamda ortaya çıkan yanlışları da fark edip düzeltebilmek.

Deep Learning tools for brain medical images

Deep learning codes and projects using Python

Grokking Deep Reinforcement Learning

🌓 Bringing pjreddie's DarkNet out of the shadows #yolo

Avalanche: a End-to-End Library for Continual Learning.

Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.

TensorFlow-based neural network library

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

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

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

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

Datasets, tools and more from Darwinex Labs - Prop Investing Arm & Quant Team @ Darwinex

A curated list of awesome awesomeness about artificial intelligence

Learning to Learn in TensorFlow

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

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

repo with challenge material for riceteacatpanda (2020)

Computer code collated for use with Artificial Intelligence Engines book by JV Stone

Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference

Getting started with AllenNLP and PyTorch by training a tweet classifier

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

Code and data for the paper 'Classifying Graphs as Images with Convolutional Neural Networks' (new title: 'Graph Classification with 2D Convolutional Neural Networks')

A tool for automatic phoneme transcription

List of AI Residency & Research programs, Ph.D Fellowships, Research Internships

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

Support content for my blog

Vector AI — A platform for building vector based applications. Encode, query and analyse data using vectors.

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

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

TensorHub is a library built on top of TensorFlow 2.0 to provide simple, modular and repeatable abstractions to accelerate deep learning research.

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

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.

Komputation is a neural network framework for the Java Virtual Machine written in Kotlin and CUDA C.

A PyTorch Library for Accelerating 3D Deep Learning Research

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!

Deep Learning Computer Vision Algorithms for Real-World Use

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 …

Finding the genre of a song with Deep Learning

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

Disclaimer: This is not an official Google product.

Daily Dose of Neural Network that Everyone Needs

SincNet is a neural architecture for efficiently processing raw audio samples.

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

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

MemeGen is a web application where the user gives an image as input and our tool generates a meme at one click for the user.

Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

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.

Dwarf Fortress + Artificial Intelligence

End-to-end speech recognition using RNN Transducers in Tensorflow 2.0

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.

Collection of Artificial Intelligence projects.

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

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

The Cutest Deep Learning Framework which is also a wonderful Declarative Language

Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

Practice and tutorial-style notebooks covering wide variety of machine learning techniques