Table of Contents

1. An Overview of LeNet
2. Convolutional Neural Network Basics
3. A Walkthrough of LeNet-1’s Architecture
4. A Walkthrough of LeNet-4’s Architecture
5. A Walkthrough of LeNet-5’s Architecture
6. Analysis of LeNet
7. Summary of LeNet

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1. An Overview of LeNet

LeNet was a group of Convolutional Neural Networks (CNNs) developed by Yann Le-Cun and others in the late 1990s. The networks were broadly considered as the first set of true convolutional neural networks. They were capable of classifying small single-channel (black and white) images, with promising results. LeNet consisted of three distinct networks, and they were:

1. LeNet-1 (five layers): A simple CNN.
2. LeNet-4 (six layers): An improvement over LeNet-1.
3. LeNet-5 (seven layers): An improvement over LeNet-4 and the most popular.

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