Offline multi-speaker meeting-speech toolkit: planted-ground-truth synthetic meeting factory, VAD, speaker embeddings, diarization (AHC/spectral/VB-HMM), overlap detection, CTC speaker-attributed ASR, cpWER/DER/JER evaluation, online streaming mode, deterministic reports. NumPy core, CPU-only torch extra.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
The fundamental package for scientific computing with Python.
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
阿布量化交易系统(股票,期权,期货,比特币,机器学习) 基于python的开源量化交易,量化投资架构
A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support
Convert Machine Learning Code Between Frameworks
Parallel computing with task scheduling
NumPy & SciPy for GPU
NumPy aware dynamic Python compiler using LLVM
Tool for real-time visualization, monitoring and collaborative analysis of AI/ML experiments and live data. Supports Python, PyTorch/Torch, NumPy, TensorFlow/Keras https://visdom.dev
Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
Efficiently computes derivatives of NumPy code.
🍊 :bar_chart: :bulb: Orange: Interactive data analysis
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
Fast data visualization and GUI tools for scientific / engineering applications
N-D labeled arrays and datasets in Python
napari: a fast, interactive, multi-dimensional image viewer for python
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
🎚️ Open Source Audio Matching and Mastering
Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more
Master the fundamentals of machine learning, deep learning, and mathematical optimization by building key concepts and models from scratch using Python.
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Fast BM25 search in Python, powered by Numpy and Numba
TensorLy: Tensor Learning in Python.
Home Assistant integration for smart-plug appliance monitoring: detects cycles, matches programs, estimates time remaining. Supports washing machines, dryers, dishwashers and more.
Fast NumPy array functions written in C
WebGL 3D plotting for Jupyter: meshes, isosurfaces, volumes, voxels and point clouds from NumPy arrays, with a path-traced cinematic renderer.
Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
Manipulate JSON-like data with NumPy-like idioms.
Computer Vision in Python
SoundFile is an audio library based on libsndfile, CFFI, and NumPy
netcdf4-python: python/numpy interface to the netCDF C library
fastest vector database made in numpy
Simple library to make working with STL files (and 3D objects in general) fast and easy.
Read and write TIFF files.
Sparse multi-dimensional arrays for the PyData ecosystem
Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!
Building Convolutional Neural Networks From Scratch using NumPy
Open Data Cube analyses continental scale Earth Observation data through time
Don't memorize ML. Understand it from first principles including Inference, Cuda, Deep Learning, Data Science and Reinforcement Learning
Scientific hypothesis-discovery workbench: grounded hypothesis-evidence graphs, synthetic literature with planted causal chains, generate-debate-evolve loops over pluggable offline adapters, Elo tournaments recovering planted skill order, paraphrase dedup, Bayesian evidence accumulation, reproducible reports. NumPy core, CPU-only torch extra.
NumPy-based text/binary PLY file reader/writer for Python
把普通文本变成以假乱真的手写体图片:手写字体 + 信纸背景 + 字距/行距/笔画随机扰动。GUI(PyQt6)与 CLI 双入口,numpy/scipy 高性能渲染引擎。
A professional Python library for 2D incompressible Navier-Stokes simulations using the Finite Difference Method, with advanced visualization.
Autonomous experiment-pipeline orchestrator: versioned research-plan DSL with dependency DAGs, deterministic checkpoint/resumable executor, tamper-evident provenance ledger, UCB1/Thompson schedulers with measured regret against synthetic oracles, ablation bookkeeping, offline report synthesis. NumPy core, CPU-only torch extra.
Automated data preparation for machine learning. Load a dataset, clean, transform, encode, scale and split it, explore it with built-in analysis, and export model-ready data with a full report. Built with Python and Streamlit.
Claude paints with code: a Claude Code skill for procedural illustration in 74 styles, from ink wash (水墨), watercolour and ukiyo-e to stained glass, Roman mosaic, Art Deco, shadow puppets, X-ray, VHS tape, pastel UI, dry-brush blocks, hatched nocturnes, a note-app UI and Flexoki paper. No image model: numpy + Pillow on a CPU, draw-on GIFs.
Neural network from scratch in pure Python + NumPy that recognises handwritten digits (MNIST), with an educational GUI: watch it learn, tweak it while it trains, look inside every layer.
End-to-end spoken language understanding toolkit: a small trainable neural audio codec with RVQ, joint intent-and-slot models over speech features, streaming partial intents, a speech-LLM instruction data pipeline, and turn-level evaluation with reproducible reports. NumPy core with a CPU-only torch extra; synthetic data only, fully offline.
Streaming speech recognition toolkit: log-mel features, CTC greedy and prefix-beam decoding, transducer-style frame-synchronous emission, forced alignment, Chinese/English CER-WER metrics, and a chunked streaming pipeline with latency accounting. NumPy core with a CPU-only torch extra; trainable demos on synthetic audio, fully offline.
Thermodynamic analysis for open quantum systems. Define a cycle as a sequence of strokes; get heat, work, entropy production, COP, and trajectory-resolved distributions. Validated against analytic limits. Built on numpy/scipy.
Trace Python+NumPy programs into SymPy formulas and check them symbolically, across all branches