Real World: Convolutional Neural Networks

Linear Algebra for Machine Learning · Axiom Academy

Convolutional Neural Networks deconstructed as matrix operations Convolution: Structured matrix multiplication (discussed in Module 13) Pooling: Downsampling via max/average Normalization: Batch norm, layer norm Representing Images as Matrices Parameter Sharing and Efficiency Linear Combination Across Channels Backpropagation: Same chain rule as before, applied through convolution operations Convolution gradient: Backward pass also uses convolution-like operations Batch normalization: Normalizes layer inputs, allows higher learning rates, acts as regularization Skip connections: Allow gradients to flow directly, help train very deep networks Data augmentation: Randomly crop, rotate, flip images to increase training data diversity Unit 8: Linear Algebra in Neural Networks | Module 16: CNNs

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