NLP & Computer Vision

Convolution · Attention · Representations

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How convolutional networks teach machines to see, and how attention-based language models teach machines to read — two fields, one shared engine of learned representations.

By the end, you can

Explain what "learned representations" means and why it is the shared engine of vision and language.
Describe an image as a numerical tensor and state its three dimensions.
Apply the convolution output-size formula (W − K + 2P) / S + 1 to compute a feature map's dimensions.
Explain spatial locality and parameter sharing, and state what property the latter gives a filter.
Describe the three-layer feature hierarchy produced by stacking convolution layers.
Distinguish classification, detection, and segmentation by the spatial precision of their outputs.
Explain how word embeddings encode semantic relationships as directions in vector space.
Describe the vanishing-gradient problem in RNNs and explain how self-attention addresses it.
Define the roles of Query, Key, and Value in self-attention.
Explain how LLMs are trained (next-token prediction, self-supervised, pretrain then fine-tune).
Articulate the distinction between statistical pattern matching and human comprehension, and why it matters.

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