A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!

Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)

Deep reinforcement learning framework for fast prototyping based on PyTorch

Hyperparameter tuning for FCN using Ray Tune

1st place solution to Automated Machine Learning https://www.automl.ai/competitions/2

Clean and easy to understand implementations of many Quantum Reinforcement Learning agents as well as their classical analouges. Greately inspired by the orgininal CleanRL

A sample workflow for classifying wetlands in Google Earth Engine. Uses data from multiple sources.

Code to reproduce 'Learning Distance Estimators from Pivoted Embeddings of Metric Objects'.

Learning ReLU networks to high uniform accuracy is intractable (ICLR 2023)

Hyperparameter Optimization of Tree Parity Machines to Minimize the Effectiveness of Unconventional Attacks on Neural Cryptography​.

Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Instance segmentation with U-Net/Mask R-CNN workflow using Keras & Ray Tune

Additional stoppers for ray tune

Official Repository for the paper: Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Low-code machine learning and deep learning

Training ReLU networks to high uniform accuracy is intractable

🚀 Hands-on Ray distributed computing examples: Core, Data, Train, Tune, Serve with PyTorch integration and ML workflows

My journey to explore AI related topics

This repository showcases hands-on projects leveraging distributed multi-GPU training to fine-tune large language models (LLMs).