CNN Tutorial for Beginners | Convolutional Neural Network | CNN Tutorial Python | Simplilearn

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This "CNN tutorial for beginners" by Simplilearn will take you through the concept of CNN and why we use it. This CNN tutorial python explains Conventional Neural Network with a suitable example. In addition, we will use the MNIST dataset to demonstrate the concept of Conventional Neural Networks in image classification. Below are the topics we are covering in this CNN tutorial for beginners.

00:00 CNN tutorial for beginners
01:38 What is CNN?
03:18 How does CNN recognize images?
04:06 Layers in CNN
04:25 Convolution Layer
04:53 ReLU Layer
05:37 Pooling Layer
06:21 Structure of CNN
07:12 How exactly does CNN recognize an image
08:05 Hands-on lab Demo

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What is CNN

A convolutional neural network is a feed-forward neural network that is generally used to analyze visual images by processing data with grid-like topology. It’s also known as ConvNet. A convolutional neural network is used to detect and classify objects in an image.

Layers in a Convolutional Neural Network

A convolution neural network has multiple hidden layers that help in extracting information from an image. The three important layers in CNN are:

Convolution layer:- This is the first step in the process of extracting valuable features from an image. A convolution layer has several filters that perform the convolution operation. Every image is considered a matrix of pixel values.

ReLU layer:- ReLU stands for the rectified linear unit. Once the feature maps are extracted, the next step is to move them to a ReLU layer.
ReLU performs an element-wise operation and sets all the negative pixels to 0. It introduces non-linearity to the network, and the generated output is a rectified feature map.

Pooling layer:- Pooling is a down-sampling operation that reduces the dimensionality of the feature map. The rectified feature map now goes through a pooling layer to generate a pooled feature map.

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