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Master specialized neural network architectures for structured data. Learn convolutional networks that exploit spatial patterns in images, recurrent networks that process sequences with memory, and attention mechanisms that power modern transformers.
Different data types have different structures that specialized architectures can exploit. Images have spatial locality—nearby pixels are related. Sequences like text and time series have temporal dependencies—earlier elements influence later ones.
Convolutional Neural Networks (CNNs) revolutionized computer vision by using learnable filters that detect local patterns regardless of their position in the image. The same edge detector works whether the edge appears in the top-left or bottom-right. This translation equivariance, combined with parameter sharing, makes CNNs incredibly efficient for visual data.
Recurrent Neural Networks (RNNs) process sequences by maintaining a hidden state that acts as memory, carrying information from earlier time steps. However, vanilla RNNs struggle with long-range dependencies due to vanishing gradients. LSTMs and GRUs solve this with gating mechanisms that control information flow.
The Transformer architecture replaced recurrence with self-attention, allowing direct connections between any positions in a sequence. This parallel processing is faster and handles long-range dependencies better. Transformers now dominate both NLP (BERT, GPT) and increasingly vision (ViT).
This chapter covers:
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Learnable filters that exploit spatial locality — parameter sharing, translation equivariance, and feature hierarchies.
Spatial downsampling for translation invariance — max pooling, average pooling, and global average pooling.
Recurrent architectures with memory — hidden states, gating mechanisms, and long-range sequence dependencies.
Self-attention and multi-head attention — parallel sequence processing that replaced recurrence in modern NLP and vision.
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