Build Your First CNN

Welcome! This guide will walk you through building a Convolutional Neural Network (CNN) from scratch. We'll cover the core concepts and provide a hands-on experience.

Step 1: Understanding CNN Basics

CNNs are a type of deep learning model that excel at processing images. They learn hierarchical features, starting with simple edges and textures and progressively building up to more complex shapes and objects. Key components include Convolutional layers, Pooling layers, and Fully Connected layers.

CNN Architecture

Step 2: Implementing a Simple Convolutional Layer

Let's create a basic convolutional layer. This layer will apply filters to the input image to detect specific features.

You'll need to define the filter size, number of filters, and stride. Experiment with different values to see how they affect the output.

Convolutional Layer

Step 3: Adding a Pooling Layer

Pooling layers reduce the spatial dimensions of the feature maps, which helps to reduce the number of parameters and make the model more robust to variations in the input.

Max pooling is a common choice. Experiment with different pool sizes and strides.

Pooling Layer

Step 4: Connecting to a Fully Connected Layer

Finally, connect the output of the pooling layers to a fully connected layer. This layer will learn to combine the extracted features to classify the image.

Adjust the number of neurons in the fully connected layer to fine-tune the model's performance.

Fully Connected Layer

This is just a starting point. There's a lot more to learn about CNNs. Feel free to explore different architectures, activation functions, and optimization techniques.

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