Visualizing Convolutional Neural Networks - Feature Maps and Learning Process

Explore the inner workings of convolutional neural networks through hands-on visualization in this 38-minute tutorial that demonstrates what CNNs actually learn and how their feature maps evolve. Begin with simple fully connected networks and progressively build complexity by adding hidden layers, ReLU activation functions, convolution layers, and pooling operations. Compare six different network architectures, starting from a basic fully connected network with no hidden layers and advancing to sophisticated CNNs with multiple stacked convolution, ReLU activation, and pooling layers. Examine feature maps at each stage to understand how networks extract and transform visual information, analyze the overall experimental results, and test your understanding with a quiz. Access the accompanying Jupyter notebook code and reference materials including foundational papers on network design strategies to deepen your comprehension of convolutional neural network visualization techniques.