*This is an AI generated summary. There may be inaccuracies.*

Summarize another video · Purchase summarize.tech Premium

This video explains the basics of how convolutional neural networks (CNNs) work. CNNs are designed to work with grid data, such as images. The first layer in a CNN is a convolutional layer, which performs a convolution. Next, a nonlinear unit is used, and the output of the convolution is passed to a pooling layer. This is called a convolutional layer, and each layer in a CNN is a convolutional layer.

**00:00:00**Convolutional neural networks are a new neural network architecture that is specifically designed for grid data, such as images. This new architecture, called a convolutional neural network, replaces the matrix multiplications that are typically used in neural nets. Each layer in a convolutional neural network can be seen as a matrix multiplication by two operations: convolutions and pooling operations. Convolutional neural networks are motivated by the fact that images are made up of little elements, or pixels, that each have a particular color or intensity.**00:05:00**CNNs are a type of neural network that are used to recognize objects in images. In this video, we learn about the basics of CNNs, including how to create filters that recognize specific patterns in an image. We also discuss how the network's output can be used to identify regions of an image that contain objects.**00:10:00**In this video, the basics of CNNs are explained. The first layer in a CNN is a convolutional layer, which performs a convolution. Next, a nonlinear unit is used, and the output of the convolution is passed to a pooling layer. This is called a convolutional layer, and each layer in a CNN is a convolutional layer.

Copyright © 2024 Summarize, LLC. All rights reserved. · Terms of Service · Privacy Policy · As an Amazon Associate, summarize.tech earns from qualifying purchases.