An early instance of a successful gradient-based learning technique is detailed in the paper “Gradient-Based Learning Applied to Document Recognition” by Y. LeCun et al., published in the Proceedings of the IEEE in 1998. This work exemplifies the practical application of gradient-based learning through the development of convolutional neural networks (CNNs), which were notably employed for commercial use in reading bank checks, processing several million checks per day. The paper meticulously outlines the neural architecture used, including step-by-step derivations of the algorithms and methodologies involved. This contribution not only showcased the effectiveness of CNNs in real-world applications but also provided a comprehensive framework that influenced subsequent advancements in the field of neural networks and deep learning.