Source-to-Source Debuggable Derivatives in Pure Python
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Updated
Sep 29, 2022 - Python
Source-to-Source Debuggable Derivatives in Pure Python
PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
Optimal transport tools implemented with the JAX framework, to get differentiable, parallel and jit-able computations.
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
🔬 Nano size Theano LSTM module
Tensorlang, a differentiable programming language based on TensorFlow
Differentiable Fluid Dynamics Package
Tensor network based quantum software framework for the NISQ era
A deep learning framework created from scratch with Python and NumPy
Numerical integration in arbitrary dimensions on the GPU using PyTorch / TF / JAX
Differentiable Programming Tensor Networks
adam implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.
Differentiable Quantum Chemistry (only Differentiable Density Functional Theory and Hartree Fock at the moment)
Documented and Unit Tested educational Deep Learning framework with Autograd from scratch.
A JIT compiler for hybrid quantum programs in PennyLane
QuantumFlow: A Quantum Algorithms Development Toolkit
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