A hands-on guide to automatic differentiation in TensorFlow using tf.GradientTape. Covers computing gradients for variables vs. constants, using tape.watch(), visualizing derivatives, and handling multiple parameters.

An end-to-end implementation of a custom training and validation loop for a CNN on the Fashion MNIST dataset. This project demonstrates low-level model training using tf.GradientTape and tf.keras.metrics, without relying on model.fit().

Implementation of Linear Regression using TensorFlow's low-level API with a custom tf.GradientTape training loop. Covers manual gradient computation, weight updates, and visualization of predictions vs actual values for educational understanding of core training mechanics.