Description: Block training is a training method in the field of Deep Learning that focuses on processing data in segments or blocks, rather than doing so globally. This approach allows for handling large volumes of data more efficiently, as it divides the dataset into smaller parts that can be processed sequentially or in parallel. By working with blocks, memory usage is optimized and training time is reduced, resulting in faster and more effective learning. Additionally, this method facilitates the implementation of regularization techniques and improves model generalization by allowing the algorithm to focus on specific patterns within each block. In summary, block training is a key strategy in the development of Deep Learning models, aimed at maximizing the efficiency and effectiveness of the learning process.
