Introduction to LeNet-5
LeNet-5 is a convolutional neural network (CNN) architecture that introduced several key features and innovations that have become standard in modern deep learning. It demonstrated the effectiveness of CNNs for image recognition tasks and introduced key concepts such as convolution, pooling, and hierarchical feature extraction that underpin modern deep learning models.
Originally designed for handwritten digit recognition, the principles behind LeNet-5 have been extended to various applications, including:
- Handwriting recognition in postal services and banking.
- Object and face recognition in images and videos.
- Autonomous driving systems for recognizing and interpreting road signs.
LeNet-5 Architecture
In the late 1990s, Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner created a convolutional neural network (CNN) based architecture called LeNet. The LeNet-5 architecture was developed to recognize handwritten and machine-printed characters, a function that showcased the potential of deep learning in practical applications. This article provides an in-depth exploration of the LeNet-5 architecture, examining each component and its contribution in deep learning.