This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.

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

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

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

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

YOLOV8 - Object detection

No description provided.

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​.

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

Additional stoppers for ray tune

DeepAR implementation for seasonal influenza cases in German districts

Low-code machine learning and deep learning

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

Micro tutorial on how to run and scale HPO with LightGBM and Tune

Personal research project seeking to evaluate the efficacy of advanced neural architectures for the purpose of estimating energy expenditure from physical activity.

My journey to explore AI related topics

A curated collection of machine learning and deep learning notebooks — classification, regression, CV, autoencoders, NLP, and time series forecasting with TensorFlow, PyTorch, and Ray Tune.

Hyper-parameter Optimization of a XGBoost Model using Ray Tune

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

This MLOps repository contains python modules intended for distributed model training, tuning, and serving using PyTorch and Ray, a distributed computing framework.

Classifying Underlying Placental Issues in Premature Infants with Deep Learning

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

Advanced multivariate macroeconomic forecasting using AutoPatchTST and AutoML on the FRED-MD dataset, comparing feature selection strategies and hyperparameter optimization.

A project completed with personal Whoop data in order to evaluate neural models vs traditional ML methods for physical activity classification and regression on Whoop's proprietary Recovery score.

Token classification for named entities