Deep Learning CNN Convolutional Neural Networks with Python - LeNet

The video discusses the evolution of convolutional neural networks (CNNs), starting with the pioneering work of John Lee Cohn at AT&T Bell Labs, who developed LeNet for handwritten digit recognition. LeNet's architecture, including its layers and non-linearities, is detailed, highlighting its significance in the 1990s. The video also covers LeNet's impact on machine learning, its adoption in ATMs, and the transition to more advanced CNN architectures like AlexNet, VGG, and ResNet.

What were the dimensions of the input images accepted by the LeNet architecture?

How many filters were used in the first convolutional layer of the LeNet architecture?

What was the final output layer of the LeNet architecture used for?