Convolutional Neural Networks: Fundamentals and Applications for Analyzing Visual Imagery

In the field of deep learning, a convolutional neural network, also known as a CNN, is a type of artificial neural network that is typically used to conduct analysis on visual data. At least one of the layers in a CNN substitutes the mathematical operation of convolution, sometimes known as convolving, for the more traditional matrix multiplication. They are utilized in both the image recognition and processing processes, as their primary purpose is the processing of pixel data. Applications can be found in areas such as image and video recognition, recommender systems, and more.image classification,image segmentation,image analysis for medical purposes,natural language processing,interfaces between the human brain and computers, andfinance time series.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Convolutional Neural Network

Chapter 2: Artificial Neural Network

Chapter 3: Types of Artificial Neural Networks

Chapter 4: Deep Learning

Chapter 5: Activation Function

Chapter 6: Layer (Deep Learning)

Chapter 7: LeNet

Chapter 8: Tensor (Machine Learning)

Chapter 9: Receptive Field

Chapter 10: History of Artificial Neural Networks

(II) Answering the public top questions about convolutional neural networks.

(III) Real world examples for the usage of convolutional neural networks in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of convolutional neural networks.

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