A Generative Adversarial Network for the generation of new synthetic art.

An in-depth guide to customizing model.fit() in TensorFlow/Keras by overriding the train_step function. Covers the manual implementation of the forward pass, loss calculation, gradient application, and metric updates. Includes a basic GAN implementation as a practical example.

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.

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.

Custom TensorFlow training loops for image classification: a foundational CNN on Eurosat using tf.GradientTape for learning, and an optimized MNIST MLP with BatchNorm, Dropout, and learning rate scheduling for higher accuracy.

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().

🧵 Classify fashion images using a compact CNN model in TensorFlow/Keras, designed for the Fashion-MNIST dataset with easy execution in Google Colab.