Stars
Streamlit — A faster way to build and share data apps.
📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
Tennis Ball Speed Estimation using a Racket-mounted Motion Sensor
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
LiteRT, successor to TensorFlow Lite. is Google's On-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization
Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflo…
AI Agent + Coding Agent + 300+ assistants: agentic AI desktop with autonomous coding, intelligent automation, and unified access to frontier LLMs.
Official code, datasets and checkpoints for "Timer: Generative Pre-trained Transformers Are Large Time Series Models" (ICML 2024) and subsequent works
PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)
Google Research
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
offical implementation of TKDE paper "Deep isolation forest for anomaly detection"
CUDA Python: Performance meets Productivity
Driver stack (including user space libraries, kernel module and firmware) for the Arm® Ethos™-N NPU
Solutions for C++ Primer 5th edition exercises
Tensorflow implementation of "Unsupervised Deep Embedding for Clustering Analysis"
A Calculus book being written by Delft University of Technology employees.
A linear algebra book being written by Delft University of Technology employees.
A PyTorch implementation of the Deep SVDD anomaly detection method
Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, i…
A toolkit for time series machine learning and deep learning
Introduction to Parallel Programming class code
Google Colab Notebooks for Udacity CS344 - Intro to Parallel Programming
Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)