CIFAR-10 image classification using TensorFlow/Keras with a custom F-Beta metric. Includes CNN architecture, data preprocessing, EarlyStopping, and performance tracking to balance precision and recall beyond standard accuracy.

A collection of projects demonstrating deep TensorFlow/Keras expertise. Includes custom Dense & Conv2D layers and tf.GradientTape training loops for image classification (MNIST, CIFAR-10, Eurosat). Emphasizes model optimization, regularization, and core framework mechanics.

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.

A collection of TensorFlow/Keras notebooks demonstrating custom model subclassing and custom layer creation. Includes regression on California Housing and filtered MNIST classification, showcasing preprocessing, training, evaluation, and deep learning customization skills.