Tensors and Dynamic neural networks in Python with strong GPU acceleration

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)

Deep learning with spiking neural networks (SNNs) in PyTorch.

Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.

Image registration laboratory for 2D and 3D image data

A high-performance distributed deep learning system targeting large-scale and automated distributed training.

A deep learning framework created from scratch with Python and NumPy

Drop-in autodiff for NumPy.

Tiny and elegant deep learning library

🌱 Guided-mode expansion of photonic crystal slabs

Documented and Unit Tested educational Deep Learning framework with Autograd from scratch.

NumPy实现类PyTorch的动态计算图和神经网络框架(MLP, CNN, RNN, Transformer)

Implementation of Karpathy's micrograd in Mojo 🔥

Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.

Qualia is a deep learning framework deeply integrated with automatic differentiation and dynamic graphing with CUDA acceleration. Qualia was built from scratch.

Deep-Learning framework from scratch

Error propagation and statistical analysis for Monte Carlo simulations in lattice QCD and statistical mechanics using autograd.