A pedagogical implementation of Autograd
☆1,021May 26, 2020Updated 6 years ago
Alternatives and similar repositories for autodidact
Users that are interested in autodidact are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Efficiently computes derivatives of NumPy code.☆7,513Updated this week
- Rudimentary automatic differentiation framework☆76Apr 26, 2019Updated 7 years ago
- Research language for array processing in the Haskell/ML family☆1,688Jan 5, 2026Updated 6 months ago
- Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more☆36,028Updated this week
- Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/☆2,925Jun 29, 2026Updated 3 weeks ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Hardware accelerated, batchable and differentiable optimizers in JAX.☆1,051Jun 4, 2026Updated last month
- JAX - A curated list of resources https://github.com/google/jax☆2,138Jan 20, 2026Updated 6 months ago
- ☆948Jul 9, 2026Updated last week
- Flax is a neural network library for JAX that is designed for flexibility.☆7,270Updated this week
- Source-to-Source Debuggable Derivatives in Pure Python☆2,321Sep 29, 2022Updated 3 years ago
- Optax is a gradient processing and optimization library for JAX.☆2,302Jul 15, 2026Updated last week
- Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.☆2,720Updated this week
- JAX-based neural network library☆3,256Jul 9, 2026Updated last week
- Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/☆2,069Jun 21, 2026Updated last month
- Wordpress hosting with auto-scaling - Free Trial Offer • AdFully Managed hosting for WordPress and WooCommerce businesses that need reliable, auto-scalable performance. Cloudways SafeUpdates now available.
- Fast and Easy Infinite Neural Networks in Python☆2,386Mar 1, 2024Updated 2 years ago
- Extending JAX with custom C++ and CUDA code☆403Aug 18, 2024Updated last year
- functorch is JAX-like composable function transforms for PyTorch.☆1,434Aug 21, 2025Updated 11 months ago
- Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)☆9,554Jul 5, 2026Updated 2 weeks ago
- higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual tr…☆1,629Mar 25, 2022Updated 4 years ago
- AlgoPy is a Research Prototype for Algorithmic Differentation in Python☆89Jul 7, 2024Updated 2 years ago
- ☆774Jan 27, 2024Updated 2 years ago
- Exponential families for JAX☆77Updated this week
- Code for "Efficient optimization of loops and limits with randomized telescoping sums"☆28May 13, 2019Updated 7 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- A generic Monte Carlo method based on the Gumbel-Max trick.☆32May 31, 2016Updated 10 years ago
- Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.☆6,462Apr 4, 2025Updated last year
- ☆34Oct 5, 2020Updated 5 years ago
- Normalizing Flows in Jax☆109Aug 19, 2020Updated 5 years ago
- ☆644Jul 8, 2026Updated last week
- Tools for JAX☆50Updated this week
- Differentiable SDE solvers with GPU support and efficient sensitivity analysis.☆1,728Dec 30, 2024Updated last year
- A highly efficient implementation of Gaussian Processes in PyTorch☆3,899Jul 10, 2026Updated last week
- Code for the paper "Learning Differential Equations that are Easy to Solve"☆290Nov 25, 2021Updated 4 years ago
- GPU virtual machines on DigitalOcean Gradient AI • AdGet to production fast with high-performance AMD and NVIDIA GPUs you can spin up in seconds. The definition of operational simplicity.
- Deep universal probabilistic programming with Python and PyTorch☆9,025Jul 10, 2026Updated last week
- ☆154May 25, 2020Updated 6 years ago
- KErnel OPerationS, on CPUs and GPUs, with autodiff and without memory overflows☆1,187Updated this week
- Differentiable Optimization-Based Modeling for Machine Learning☆350Oct 28, 2019Updated 6 years ago
- Documentation:☆131May 22, 2023Updated 3 years ago
- Mathematical operations for JAX pytrees☆210Dec 5, 2024Updated last year
- Probabilistic Torch is library for deep generative models that extends PyTorch☆893May 12, 2024Updated 2 years ago