In this video, the concept of batches in deep learning is explained, along with how processing data in smaller groups improves efficiency. It explores why batches are important to balance memory usage and computational power. Furthermore, the distinctions between various types of batches are examined, with appropriate examples provided to illustrate their advantages and disadvantages. This video also details how to optimize a particular model while speeding up and stabilizing neural network training.
The workbook pdf-
