Part 8/12:
Attempting in-place modifications will raise errors, emphasizing the importance of immutable data structures. To modify arrays, users should generate new versions, which make code more predictable and parallelizable.
Advanced Features
Automatic Differentiation (Autograd)
Differentiation is central to ML, and JAX excels here. The speaker demonstrated how you could easily compute derivatives of functions like softmax using jax.grad. For higher-order derivatives, JAX handles up to third, fourth, or even higher derivatives with remarkable simplicity, compared to the verbose and complex code in TensorFlow or PyTorch.