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ML algorithms implemented and derived from first-principles in Jupyter Notebooks and NumPy

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

A Data Streaming Library for Efficient Neural Network Training

Audio processing by using pytorch 1D convolution network

A high performance Vector Database leveraging parallel graph computing

Cookiecutter template for FastAPI projects using: Machine Learning, uv, Github Actions and Pytests

🕵️‍♂️ Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach.

A C++17 library to access the entire OpenAI API.

A curated list of all machine learning algorithms and deep learning algorithms grouped by category.

Effortless plugin and play Optimizer to cut model training costs by 50%. New optimizer that is 2x faster than Adam on LLMs.

A deep learning framework built on an autograd engine with high level abstractions and low level control.

A deep learning framework built on an autograd engine with high level abstractions and low level control.

InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy

Python library for backtesting technical/mechanical strategies in the stock and currency markets

Native C and C++ implementation of RAISR (Rapid and Accurate Image Super-Resolution). Intel Video Super Resolution Library

A small deep-learning library to distinguish human and bot from their mouse movements.

AI crypto trading bot with deep neural network (84.9% accuracy, 25 coins). BiLSTM + Attention trained on GPU. Bybit, Binance, OKX, Gate.io. Free cloud or self-hosted.

Prototype of an intelligent safety system for detecting driver drowsiness