144 Open source projects that are alternatives of or similar to Hord

PyTorch implementation of Deformable Convolution

🦅Hyperparameter optimization for machine learning pipelines 🦅

Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.

⚡️ AllenNLP plugin for adding subcommands to use Optuna, making hyperparameter optimization easy

Purely functional genetic algorithms for multi-objective optimisation

Examples for https://github.com/optuna/optuna

PyTorch1.x tutorials, examples and some books I found 【不定期更新】整理的PyTorch 1.x 最新版教程、例子和书籍

Your local Flux surgeon

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

A simple collection of well working NLP models (Keras, H2O, StarSpace) tuned and benchmarked on a variety of datasets.

AutoGluon: AutoML for Text, Image, and Tabular Data

PyTorch implementation of Proximal Gradient Algorithms a la Parikh and Boyd (2014). Useful for Auto-Sizing (Murray and Chiang 2015, Murray et al. 2019).

Python library for CMA Evolution Strategy.

Using Kafka-Python to illustrate a ML production pipeline

A curated list of awesome Distributed Deep Learning resources.

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Deep Learning Library. For education. Based on pure Numpy. Support CNN, RNN, LSTM, GRU etc.

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

Set model hyperparameters using deterministic, exact algorithms.

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Real-time Web Dashboard for Optuna.

Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020

Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

Scalable, structured, dynamically-scheduled hyperparameter optimization.

Hands-On Deep Learning Algorithms with Python, By Packt

If you've always wanted to learn about deep-learning but don't know where to start, then you might have stumbled upon the right place!

Pytorch implementation of ICRA 2020 paper "360° Stereo Depth Estimation with Learnable Cost Volume"

OptKeras: wrapper around Keras and Optuna for hyperparameter optimization

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

Automated Machine Learning with scikit-learn

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.

It contains the coursework and the practice I have done while learning Deep Learning.🚀 👨‍💻💥 🚩🌈

Bayesian Optimization using GPflow

MIT DeepTraffic top 2% solution (75.01 mph) 🚗.

A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.

A library for performing hyperparameter optimization

Asynchronous Distributed Hyperparameter Optimization.

Tuning hyperparams fast with Hyperband

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

Code for our paper "Regularizing Neural Networks via Adversarial Model Perturbation", CVPR2021

An automatic ML model optimization tool.

📢 Ready to learn or review your knowledge!

Gradient based hyperparameter optimization & meta-learning package for TensorFlow

Yes, it's a camel case.

Time Series Cross-Validation -- an extension for scikit-learn

Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).

DeepArchitect: Automatically Designing and Training Deep Architectures

Python library for Bayesian hyper-parameters optimization

Population Based Training (in PyTorch with sqlite3). Status: Unsupported

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

Hyperparameter optimization in Julia.

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

A curated list of almost all machine learning algorithms and deep learning algorithms grouped by category.

Determined: Deep Learning Training Platform

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

Experimental Global Optimization Algorithm

Large scale and asynchronous Hyperparameter Optimization at your fingertip.