Implementation of a custom TensorFlow/Keras model by subclassing tf.keras.Model, including custom layers, a user-defined loss function, and full control over the forward pass. Demonstrates dataset loading, preprocessing, and training on MNIST using the Model Subclassing API for maximum flexibility

Implementation of a simplified Inception-style CNN in TensorFlow/Keras, trained on CIFAR-10. Demonstrates parallel convolutional branches, modular block design, and efficient multi-path feature extraction for educational purposes.

Implementation of multiple deep learning models using Keras Functional API, including a CNN on MNIST, a multi-input/multi-output example, and a toy ResNet on CIFAR-10. Demonstrates advanced model architectures, tf.data pipelines, and practical training workflows with TensorFlow Datasets.

Implementation of a custom TensorFlow/Keras model by subclassing tf.keras.Model. Includes custom layers, a user-defined loss function, and full control over the forward pass while maintaining compatibility with the Keras fit/evaluate workflow.

Implementation and training of custom layers in TensorFlow/Keras using subclassing. Includes weight definition, initialization, regularization, and integration of the layer into a complete model for training and evaluation.

Transfer learning in TensorFlow/Keras using ImageNet-pretrained CNNs (e.g., VGG16, ResNet50) for CIFAR-10 classification. Demonstrates feature extraction, custom classifier heads, and fine-tuning workflows.