Convolutional Neural Networks (CNNs)

This repository contains my implementations of CNNs for image classification tasks, created as part of my learning journey. Through these projects, I explored both binary image classification and multiclass image classification. Below, I'll share insights and key concepts I learned along the way.

What I Learned About CNNs

1. Basic Concept of CNNs

A Convolutional Neural Network (CNN) is a type of deep neural network designed to handle image data. Unlike traditional neural networks, CNNs utilize layers specifically designed to capture spatial hierarchies in images. Key components include:

• Convolutional Layers: Apply filters to extract features like edges, textures, and shapes.
• Pooling Layers: Reduce dimensionality while preserving important features (commonly through max pooling).
• Fully Connected Layers: Flatten the data and perform classification based on the extracted features.

2. Why Use CNNs?

CNNs are ideal for image classification because they:

• Automatically detect important features (like edges or patterns) from images without manual feature engineering.
• Are spatially invariant, meaning the position of an object in an image does not drastically affect classification.
• Are computationally efficient for large images compared to fully connected networks.

3. Binary Image Classification

In this task, I built a CNN to classify images into two categories (e.g., "cat" vs. "dog"). Key learning points:

• Loss Function: For binary classification, I used the binary cross-entropy loss.
• Output Layer: A single sigmoid neuron was used for output, representing probabilities for the two classes.
• Data Preprocessing: Proper image resizing, normalization, and augmentation were essential to improve generalization.

4. Multiclass Image Classification

For this task, I built a CNN to classify images into multiple categories (e.g., handwritten digits). Key takeaways:

• Loss Function: I used categorical cross-entropy for multiclass classification.
• Output Layer: A softmax output layer was implemented, which provides a probability distribution across the multiple classes.
• Data Augmentation: Techniques like rotation, zoom, and flipping helped enhance model robustness by generating more diverse training examples.

5. Model Optimization and Evaluation

Throughout both projects, I learned to:

• Optimize Models: Using optimizers like Adam to adjust weights and biases efficiently.
• Regularization Techniques: Implementing dropout to prevent overfitting.
• Metrics: Evaluate models using metrics like accuracy, precision, recall, and confusion matrix to ensure proper performance assessment.

6. Key Challenges

• Overfitting: Early on, I struggled with overfitting, especially in smaller datasets. This taught me the importance of regularization, dropout layers, and data augmentation.
• Choosing Hyperparameters: Deciding the right number of filters, kernel size, and number of layers was initially challenging. I experimented with different architectures to understand the trade-offs.