Concept map: Tensors

Taught and used in

Deep learning and neural networks

Insights 15

• Assert prediction and target shapes right before every loss call
• Test that examples in a batch cannot influence each other's predictions
• Count parameters by hand before training and compare with your number of rows
• Check how many parameters your embedding tables hold before adding ID features
• Compute CNN feature-map sizes on paper before writing the classifier head
• Watch the flatten-to-dense layer, not the convolutions, for parameter blow-up
• Mask padding in attention, pooling and the loss, then test length invariance
• Run the first training step on the largest batch and longest sequence you will see

Projects 7

• Will this flight fill? A from-scratch numpy MLP vs gradient boosting on airline booking snapshots
• Catalogue thumbnails: a small CNN vs pixel baselines on Fashion-MNIST
• Few labels, foreign domain: frozen ImageNet features vs a small CNN on EuroSAT land cover
• Same product, different shop: TF-IDF vs sentence embeddings for product matching on Abt-Buy
• Routing airline complaints: TF-IDF + logistic regression vs a small neural text classifier
• Is it the learning rate or the seed? A repeated-seed ablation of learning rate, weight decay and dropout