Magical NumPy with JAX

Discover the power of differential computing with JAX in this comprehensive tutorial from PyCon US. Learn how to leverage your existing NumPy knowledge to write performant numerical models and train them using gradient-based optimization. Explore advanced concepts such as writing loopy numerical code without loops, thinking in data cubes, and strengthening your functional programming skills. Gain insights into generating deterministic random numbers and preview the integration of neural networks with probabilistic models. Master the NumPy-compatible JAX API to unlock the potential of differential computing without learning a new array library, all while enhancing your Python data science toolkit.