JAX-DIPS is a differentiable interfacial PDE solver.
? Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Deep Learning for humans
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
? Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
? A ranked list of awesome machine learning Python libraries. Updated weekly.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Trax — Deep Learning with Clear Code and Speed
Python code for "Probabilistic Machine learning" book by Kevin Murphy
