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The convolutional Neural Networks tutorial by Simplilearn will take you through the concept of CNN With TensorFlow and why we use it. This CNN tutorial with Python explains Conventional Neural Networks 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 Convolutional Neural Networks tutorial for beginners
01:38 What is Convolutional Neural Networks ?
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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Layers in a Convolutional Neural Network

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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️ Professional Certificate Program In AI And Machine Learning

In collaboration with Purdue, our AI and ML Course will help you unlock your AI and ML potential. You'll study Machine Learning, Deep Learning, Computer Vision, NLP, Speech Recognition, and Reinforcement Learning with Simplilearn's AI ML course online.

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- Professional Certificate Program certificate and Alumni Association membership
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Skills Covered

- Statistics
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- Unsupervised Learning
- NLP
- Neural Networks
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-GANs
- Keras
- Tensorflow
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- NumPy

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