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Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Learning to Rank in TensorFlow

Deep neural network to extract intelligent information from invoice documents.

FMA: A Dataset For Music Analysis

A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Technique was originally created by https://twitter.com/advadnoun

Automatic architecture search and hyperparameter optimization for PyTorch

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

Machine learning metrics for distributed, scalable PyTorch applications.

SDG is a specialized framework designed to generate high-quality structured tabular data.

A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine

Graph Neural Networks with Keras and Tensorflow 2.

Source-to-Source Debuggable Derivatives in Pure Python

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

An open source library and framework for deep learning on satellite and aerial imagery.

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

Repository containing notebooks of my posts on Medium

Russian GPT3 models.

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

Interpretability and explainability of data and machine learning models

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Python AutoML for Trading Systems and Sports Betting

Synthetic data curation for post-training and structured data extraction

An offline deep reinforcement learning library

Synthetic data generators for tabular and time-series data

Hyperparameter Experiments with TensorFlow and Keras

BirdNET analyzer for scientific audio data processing.