Topic: CNN (Convolutional Neural Networks) – Part 1: Introduction and Basic Concepts

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1. What is a CNN?

• A Convolutional Neural Network (CNN) is a type of deep learning model primarily used for analyzing visual data.

• CNNs automatically learn spatial hierarchies of features through convolutional layers.

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2. Key Components of CNN

• Convolutional Layer: Applies filters (kernels) to input images to extract features like edges, textures, and shapes.

• Activation Function: Usually ReLU (Rectified Linear Unit) is applied after convolution for non-linearity.

• Pooling Layer: Reduces the spatial size of feature maps, typically using Max Pooling.

• Fully Connected Layer: After feature extraction, maps features to output classes.

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3. How Convolution Works

• A kernel (small matrix) slides over the input image, computing element-wise multiplications and summing them up to form a feature map.

• Kernels detect features like edges, lines, and patterns.

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4. Basic CNN Architecture Example

| Layer Type | Description |
| --------------- | ---------------------------------- |
| Input | Image of size (e.g., 28x28x1) |
| Conv Layer | 32 filters of size 3x3 |
| Activation | ReLU |
| Pooling Layer | MaxPooling 2x2 |
| Fully Connected | Flatten + Dense for classification |

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5. Simple CNN with PyTorch Example

import torch.nn as nn
import torch.nn.functional as F

class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)  # 1 input channel, 32 filters
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(32 * 13 * 13, 10)  # Assuming input 28x28

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = x.view(-1, 32 * 13 * 13)  # Flatten
        x = self.fc1(x)
        return x

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6. Why CNN over Fully Connected Networks?

• CNNs reduce the number of parameters by weight sharing in kernels.

• They preserve spatial relationships unlike fully connected layers.

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Summary

• CNNs are powerful for image and video tasks due to convolution and pooling.

• Understanding convolution, pooling, and architecture basics is key to building models.

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Exercise

• Implement a CNN with two convolutional layers and train it on MNIST digits.

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#CNN #DeepLearning #NeuralNetworks #Convolution #MachineLearning

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