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

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

deeplearning.ai , By Andrew Ng, All video link

CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

Easy Hyper Parameter Optimization with mlr and mlrMBO.

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

RAD: Reinforcement Learning with Augmented Data

This repository will contain the example detailed codes of Tensorflow and Keras, This repository will be useful for Deep Learning staters who find difficult to understand the example codes

Hand Written course notes of Deep Learning Specialization by Andrew NG on Coursera

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Hyperparameter optimization for PyTorch.

Study and implementation about deep learning models, architectures, applications and frameworks

A general list of resources to image text localization and recognition 场景文本位置感知与识别的论文资源与实现合集 シーンテキストの位置認識と識別のための論文リソースの要約

real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)

Simple, but essential Bayesian optimization package

A curated list of automated machine learning papers, articles, tutorials, slides and projects

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

Minimalistic Multiple Layer Neural Network from Scratch in Python.

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Automated modeling and machine learning framework FEDOT

Sequential Model-based Algorithm Configuration

Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define m…

Lets Learn AI

A C++11 implementation of the B-Tree part of "The Case for Learned Index Structures"

A hyperparameter optimization framework

A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

Hyperparameter optimization in Julia.

PyTorch implementation of Deformable Convolution

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

Purely functional genetic algorithms for multi-objective optimisation

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

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

AutoGluon: AutoML for Text, Image, and Tabular Data

Python library for CMA Evolution Strategy.

A curated list of awesome Distributed Deep Learning resources.

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

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

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

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

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

Hands-On Deep Learning Algorithms with Python, By Packt

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

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

Automated Machine Learning with scikit-learn

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

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

A library for performing hyperparameter optimization

Tuning hyperparams fast with Hyperband

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

📢 Ready to learn or review your knowledge!

Yes, it's a camel case.

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

Python library for Bayesian hyper-parameters optimization

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

a distributed Hyperband implementation on Steroids

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