Why GradientTape Is the Most Underrated Feature in TensorFlow

This guide demystifies TensorFlow’s automatic differentiation with tf.GradientTape, showing how gradients are recorded in eager mode and computed for scalars, tensors, and full models. You’ll learn what the tape watches (and how to override it), grab gradients for intermediate results, handle non-scalar targets, and reason about performance trade-offs (persistent tapes, memory). It closes with practical control-flow patterns so your gradient paths match real-world training loops.