Tag

LightRAG

A graph-based RAG framework pairing a knowledge graph with vector retrieval and a dual-level (low/high) query mode. New documents merge into the graph via set operations instead of triggering a rebuild, cutting the cost of keeping the index current.

Claude Quickstarts

Runnable starter projects for the Claude API you fork and adapt: a knowledge-base customer support agent, a financial analyst that charts results in chat, plus computer-use, browser-use, and autonomous-coding-agent reference implementations.

Unsloth

Patches Hugging Face Transformers and TRL with hand-written Triton kernels to fine-tune LLMs on a single consumer GPU up to 30x faster with about 90% less memory. Does LoRA, QLoRA, and full fine-tuning across 500+ models, exporting to GGUF and Safetensors.

scikit-learn: machine learning in Python — scikit-learn 1.8.0 documentation

Provides a consistent Python API for classical machine learning, covering preprocessing, model selection, supervised and unsupervised estimators, and pipelines. Best for tabular, text, and medium-scale in-memory workflows.

NumPy

The N-dimensional array (ndarray) underpinning Python's scientific stack — pandas, scikit-learn, and SciPy build directly on it. Vectorized math, broadcasting, and a C/Fortran bridge move numeric work out of Python loops into compiled code.

OpenCV

Provides a comprehensive set of computer-vision algorithms and image/video processing utilities with multi-language bindings (C++, Python, Java), contrib modules, and community docs/forums — suitable for prototyping, production pipelines, and real-time applications.

pgmpy

Provides APIs to build, learn, and run Bayesian and dynamic Bayesian networks, perform probabilistic inference, and compute interventional/counterfactual queries. Ships example notebooks, tutorials, and PyPI/conda packages. ([github.com](https://github.com/pgmpy/pgmpy))

XGBoost

Trains gradient-boosted tree models across local and distributed environments, with bindings for Python, R, JVM, Julia, and C++. Its sparsity-aware split finding and quantile sketch made it a default baseline for tabular ML competitions.

TensorFlow

Builds and deploys machine learning models across research, production, web, mobile, and edge environments. Its ecosystem spans Keras, TFX, LiteRT, TensorFlow.js, datasets, model hubs, and visualization tools.

PythonRobotics

Readable, minimal-dependency Python implementations of core robotics algorithms — localization (EKF, particle filter), SLAM (ICP, FastSLAM), path planning (A*, RRT*, PRM), and path tracking (LQR, MPC) — written to be studied, not just run.

LightGBM

Trains gradient-boosted decision trees for classification, ranking, and large-scale tabular ML with lower memory use and faster training. GOSS and EFB help it handle high-dimensional sparse data on CPU, GPU, and distributed setups.

PyTorch

Lets researchers and engineers build neural networks as regular Python programs, with GPU-backed tensors, autograd, distributed training, and production paths through TorchScript and related tooling.