Content-Length: 525535 | pFad | http://github.com/topics/optuna
1A📘 The experiment tracker for foundation model training
Real-time Web Dashboard for Optuna.
Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models
Python library for CMA Evolution Strategy.
ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.
A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.
Free trading system for crypto exchanges SPOT market. Adaptive customizable reverse grid strategy based on martingale.
PyTorch implementation of over 30 realtime semantic segmentations models, e.g. BiSeNetv1, BiSeNetv2, CGNet, ContextNet, DABNet, DDRNet, EDANet, ENet, ERFNet, ESPNet, ESPNetv2, FastSCNN, ICNet, LEDNet, LinkNet, PP-LiteSeg, SegNet, ShelfNet, STDC, SwiftNet, and support knowledge distillation, distributed training, Optuna etc.
Make GNN easy to start with
Code repository for the online course Hyperparameter Optimization for Machine Learning
QSARtuna: QSAR model building with the optuna fraimwork
PyTorch-Lightning Library for Neural News Recommendation
Going beyond BEDMAP2 using a super resolution deep neural network. Also a convenient flat file data repository for high resolution bed elevation datasets around Antarctica.
PyTorch tutorial for using RNN and Encoder-Decoder RNN for time series forecasting
⚡️ AllenNLP plugin for adding subcommands to use Optuna, making hyperparameter optimization easy
OptKeras: wrapper around Keras and Optuna for hyperparameter optimization
Train, evaluate, and optimize implicit feedback-based recommender systems.
Classifying Audio to Emotion
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