#Hyperparameter-tuning
Showing 48 of 48 repositories tagged #hyperparameter-tuning, ranked by stars
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, Slurm, 20+ clouds, on-prem).
Automated Machine Learning with scikit-learn
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
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Sequential model-based optimization with a `scipy.optimize` interface
Automated Machine Learning on Kubernetes
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
HyperView is a terminal-first TradingView strategy lab for downloading market data, backtesting Python strategies with Pine-like behavior, and optimizing SL/TP parameters.
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.
EvalML is an AutoML library written in python.
The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
Tuning hyperparams fast with Hyperband
Human-explainable AI.
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Parallel Hyperparameter Tuning in Python
Library for Semi-Automated Data Science
A web-based dashboard for Deep Learning
Library for automatic retraining and continual learning
Sentence Classifications with Neural Networks
State-of-the art Automated Machine Learning python library for Tabular Data
An automatic ML model optimization tool.
Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.
Programming assignments and lecture notes of the Deep Learning Specialization taught by Andrew Ng and offered by deeplearning.ai on Coursera.
[NeurIPS 2025] Official implementation for our paper "Scaling Diffusion Transformers Efficiently via μP".
A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!
Hyperparameter tuning for machine learning models using a distributed genetic algorithm
Metadata store for Production ML
Deep Learning Specialization course offered by DeepLearning.AI on Coursera
A library for performing hyperparameter optimization
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
Python Scripts and Jupyter Notebooks
Korean Sentence Embedding Model Performance Benchmark for RAG
📊 30 Days of Data Science is a daily challenge to guide you through Data Science essentials. From basics to advanced, this repo offers clear examples, practical exercises, and resources to help you master Data Science, one day at a time. Whether you're new or refining your skills, this challenge has something for you. Join the journey now! 🚀
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
CryptoMarket Regime Classifier is a machine learning framework that detects market regimes (Trend, Range, Squeeze, etc.) in crypto markets using multi-timeframe data and Hidden Markov Models. The project provides plug-and-play labeled datasets and trained models (HMM + LSTM) for downstream strategy development, position sizing, and risk management.
A collection of Hyper parameter tuning library.
A statistical computations and ML orientated Python package to predict stock price.
This repository includes code for the paper "Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection" accepted in AutonomousCyber, ACM CCS, 2024.
A comprehensive collection of data analysis and machine learning projects, showcasing techniques and models for various data challenges. Dive in to explore code examples, analyses, and machine learning workflows.
Moody's Bond Rating Classifier and USPHCI Economic Activity Forecast Modeling
Contains all my data science projects.
Python-based trading framework designed for high-performance backtesting, hyperparameter optimization, and live trading.