1231 Open source projects that are alternatives of or similar to Keras

PipelineX: Python package to build ML pipelines for experimentation with Kedro, MLflow, and more

🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code

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

Apuntes personales sobre estadística, machine learning y lenguaje de programación R

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

A day to day plan for this challenge. Covers both theoritical and practical aspects

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

[tutorial]A functional, Data Science focused introduction to Python

Dive into Machine Learning with Python Jupyter notebook and scikit-learn! First posted in 2016, maintained as of 2021. Pull requests welcome.

Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

LAMA - automatic model creation framework

A collection of deep learning models implemented in PyTorch

🟣 Object detection of abstract shapes with neural networks

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

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

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

LATEX: TikZ package for drawing neural networks. Also available on CTAN at http://www.ctan.org/tex-archive/graphics/pgf/contrib/neuralnetwork

Open solution to the TGS Salt Identification Challenge

♾️ CML - Continuous Machine Learning | CI/CD for ML

Machine reading comprehension on clinical case reports

Airbyte is an open-source EL(T) platform that helps you replicate your data in your warehouses, lakes and databases.

Analytical Web Apps for Python, R, Julia, and Jupyter. No JavaScript Required.

BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.

A tool for retrosynthetic planning

Awesome machine learning model compression research papers, tools, and learning material.

A Django app that creates automatic web UIs for Python scripts.

Tutorial: Web scraping in Python with Beautiful Soup

Experiment tracking for machine and deep learning projects

Federated Messaging with ZeroMQ

A schema analyzer for MongoDB

OpenDS4All project, hosted by LF AI & Data

Lightweight, Python library for fast and reproducible experimentation 🔬

A fast xgboost feature selection algorithm

Library for Semi-Automated Data Science

Gender recognition by voice and speech analysis

Ranking, acceptance rate, deadline, and publication tips

Machine Learning notebooks for refreshing concepts.

Ensemble learning related books, papers, videos, and toolboxes

Dash Demo App - New York Oil and Gas

Self-knowledge through numbers

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

Accelerate data science

R client for the Elasticsearch HTTP API

A reproduction of growing neural cellular automata using PyTorch.

Ergonomic machine learning for everyone.

An open source library for artificial neural networks.

The author's officially unofficial PyTorch BigGAN implementation.

This repository provides everything you need to get started with Python for (social science) research.

General Assembly's 2015 Data Science course in Washington, DC

This repo contains logstash of various honeypots

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

This is a tutorial for PyTorch Geometric on the YooChoose dataset

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

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

Powerful financial charting library based on R's Quantmod | http://py-quantmod.readthedocs.io/en/latest/

An embedded language for probabilistic programming and meta-programming.

Support for jupyter notebook templates in jupyterlab

Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft

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

Flexible JSON traversal for rapid prototyping.