Implementing Convolutional Neural Network (CNN) in MATLAB

Convolutional Neural Networks (CNNs) are widely used in image recognition and computer vision tasks.

To implement a CNN using MATLAB's built-in deep learning library, follow these steps:

1. Load and preprocess data: Prepare your dataset, typically images for CNN. Use MATLAB functions like imageDatastore to load and preprocess the data.
2. Create CNN architecture: Define the layers of your CNN model using MATLAB's deep learning toolbox. This includes convolutional layers, pooling layers, fully connected layers, and activation functions.
3. Specify training options: Set training options such as the optimizer, learning rate, mini-batch size, and number of epochs using trainingOptions.
4. Train the CNN: Use the trainNetwork function to train the CNN model on your dataset with the specified options.
5. Evaluate the performance: Evaluate the trained model on a separate test set using metrics like accuracy, precision, and recall.
6. Make predictions: Use the trained CNN model to make predictions on new unseen data.

By following these steps, you can successfully implement a Convolutional Neural Network (CNN) using MATLAB's deep learning library for image recognition tasks.