1. Load and Preprocess the Dataset

• Import the CIFAR-10 dataset from Keras.
• Normalize the pixel values to the range [0,1] for better convergence.
• Convert the class labels into one-hot encoded format for categorical classification.

2. Define the CNN Model

Implement a VGG-like CNN architecture with the following key layers:

Convolutional Layers:

• Kernel size: 3×3;
• Activation function: ReLU;
• Padding: 'same'

Pooling Layers:

• Pooling type: Max Pooling
• Pooling size: 2×2

Dropout Layers (Prevent overfitting by randomly disabling neurons):

• Dropout rate: 25%

Flatten Layer - convert 2D feature maps into a 1D vector for classification.

Fully Connected Layers - dense layers for final classification, with a relu or softmax output layer.

Compile the model using:

• Adam optimizer (for efficient learning).
• Categorical cross-entropy loss function (for multi-class classification).
• Accuracy metric to measure performance (classes are ballanced).

3. Train the Model

• Use 20 epochs and batch size of 64 for training.
• Include validation data to track model performance on unseen images.
• Save the training history to visualize accuracy and loss trends.

4. Evaluate and Visualize Results

• Test the model on CIFAR-10 test data and print the accuracy.
• Plot training loss vs. validation loss to check for overfitting.
• Plot training accuracy vs. validation accuracy to ensure learning progression.

COLAB CNN PROJECT