Part 3/12:
One of the standout claims about JAX is its impressive speed, reportedly being 15 to 20 times faster than TensorFlow for certain operations. This speed advantage is crucial for organizations where computational efficiency directly impacts productivity and costs, such as ride-sharing platforms like Uber.
Compatibility with NumPy
JAX is designed to mimic NumPy closely, allowing seamless code translation. Developers can replace np with jnp (JAX's NumPy module), and most numpy functions will work with minimal tweaks. This compatibility lowers the barrier to adoption and reduces the learning curve.