Machine Learning · GitHub

An elegant PyTorch deep reinforcement learning library.

Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).

《深度学习与计算机视觉》配套代码

An open-source book that helps readers quickly get started with deep learning development and research using PyTorch. All PyTorch tutorials in the book have been tested to ensure they run successfully.

A complete daily plan for studying to become a machine learning engineer.

• "You can, but it is far more difficult than when I got into the field." Drac Smith
• "I'm hiring machine learning experts for my team and your MOOC will not get you the job (there is better news below). In fact, many people with a master's in machine learning will not get the job because they (and most who have taken MOOCs) do not have a deep understanding that will help me solve my problems." Ross C. Taylor

🚀Clone a voice in 5 seconds to generate arbitrary speech in real-time

stable diffusion webui colab

Making large AI models cheaper, faster and more accessible

• Data Parallelism
• Pipeline Parallelism

Code and documentation to train Stanford's Alpaca models, and generate the data.

High-performance AI pipeline engine with a C++ core and 50+ Python-extensible nodes. Build, debug, and scale LLM workflows with 13+ model providers, 8+ vector databases, and agent orchestration, all from your IDE. Includes VS Code extension, TypeScript/Python SDKs, and Docker deployment.

• Rocket Builder - Drag, connect, and configure nodes visually, no boilerplate. Real-time observability tracks token usage, LLM calls, latency, and execution. Pipelines are portable JSON, version-controllable, shareable, and runnable anywhere.
• High-Performance C++ Runtime - Native multithreading purpose-built for the throughput demands of AI and data workloads. No bottlenecks, no compromises for production scale.

Static reproducibility checks, experiment replay, and verification for ML pipelines.

This repository provides DINOSim, a method that leverages the DINOv2 foundation model for zero-shot object detection and segmentation in bioimage analysis. DINOSim uses pretrained DINOv2 embeddings to compare patch similarities, allowing it to detect and segment unseen objects in complex datasets with minimal annotations.

Aaryan Samanta: machine learning for biology and medicine. First-author papers, mentored research, USACO Platinum, and AI Ethos, Inc.

An efficient Python toolkit for Abductive Learning (ABL), a novel paradigm that integrates machine learning and logical reasoning in a unified framework.

🧠 Sentiment Sense: Advanced NLP dashboard using DistilBERT & Zero-Shot (BART) for Aspect-Based Sentiment Analysis. Detects business topics (Food, Service, Price) & emotional context in real-time. Built for enterprise feedback processing. 🚀

• Dual-Model Pipeline: Combines a DistilBERT transformer for sentiment with a BART-Large model for zero-shot topic classification.
• Aspect-Based Analysis (ABSA): Automatically identifies specific categories (Food, Service, Price, etc.) mentioned in the text without manual labeling.