Deep Learning
Neural networks, CNNs, embeddings, how nets read text, and a tiny vision application with torch/tensorflow labs on ExamMaster.
Undergraduate depth: concept notes and, where they help, in-browser labs. Reading is free.
Lessons
- Tiny Vision ApplicationIn Deep Learning because tiny image/pattern nets practice the neural training loop on spatial inputs.
- Activations and OptimizersIn Deep Learning because activations and optimizers (ReLU, Adam) shape neural training paths.
- CNNs on Tiny ImagesIn Deep Learning because convolutions exploit spatial structure — a neural, not tabular, inductive bias.
- Embeddings and SequencesIn Deep Learning because embeddings turn discrete tokens into vectors learned inside neural models.
- DL Evaluation and FailuresIn Deep Learning because overfit curves and training bugs are the failure modes specific to neural nets.
- Neural Network FundamentalsIn Deep Learning because layered neural nets (perceptron → MLP) are the core DL representation.
- How Nets Read TextIn Deep Learning because a neural net reads text as tokens, embeddings and a sequence decision — not as pixels.
- Training LoopIn Deep Learning because loss, backprop and epochs are how neural parameters are trained.
Practise Deep Learning
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