Tensors and Dynamic neural networks in Python with strong GPU acceleration
- neural-network
- autograd
- gpu
- numpy
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Tensors and Dynamic neural networks in Python with strong GPU acceleration
Google Research
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
The fundamental package for scientific computing with Python.
Simulation platform for general-purpose robotics & embodied AI learning.
Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
SciPy library main repository
A computer algebra system written in pure Python
Open source code for AlphaFold 2.
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...
Open3D: A Modern Library for 3D Data Processing
NumPy & SciPy for GPU
CUDA Templates and Python DSLs for High-Performance Linear Algebra
Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
The repository host gives no description.
AI Toolkit for Healthcare Imaging
AlphaFold 3 inference pipeline.
A Python implementation of global optimization with gaussian processes.
Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.
TranslatedA knowledge graph for mathematics (primary school to high school), English (grade 3 to high school), and junior high school physics: 1,644 knowledge points, 2,403 prerequisite edges, and 6,723 questions, aligned with the curriculum standards, with an adaptive prerequisite diagnostic.
The repository host gives no description.
Efficiently computes derivatives of NumPy code.