Loading, inspecting, and preprocessing datasets using TensorFlow Datasets (TFDS). Demonstrates train/test split handling, image normalization, batching, shuffling, caching, and visualization to build efficient, ready-to-train deep learning pipelines.

Hands-on deep dive into TensorFlow’s low-level API — covering tensor creation, shape manipulation, broadcasting, slicing, and math operations. Builds a strong foundation for custom deep learning workflows without relying solely on high-level abstractions.

A series of Jupyter notebooks exploring TensorFlow and Keras APIs for deep learning. Covers CNNs on CIFAR10/MNIST, subclassing models, transfer learning, data pipelines, augmentation, image loading, low-level APIs, and custom layers/functions. Demonstrates practical implementations with fine-tuning, preprocessing, and visualization techniques.

Collection of TensorFlow/Keras Jupyter notebooks demonstrating low-level APIs, custom training loops, callbacks, subclassed models, custom loss functions, transfer learning, and advanced deep learning architectures.

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 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.

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().

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.

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.

Efficient image data loading and preprocessing pipeline using TensorFlow and Keras. Includes directory-based dataset loading, normalization, resizing, batching, and performance optimization with caching, shuffling, and prefetching for high-throughput model training.

Practical guide to building high-performance data pipelines in TensorFlow using the tf.data API. Covers dataset creation, preprocessing, shuffling, batching, caching, and prefetching with AUTOTUNE to maximize training throughput and hardware utilization.

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

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.