Practical implementation of image data augmentation using TensorFlow and Keras preprocessing layers. Includes real-time transformations, visual comparisons, and training integration to improve model generalization and reduce overfitting without adding new data.

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.

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.