Notes on deep learning

Wed, 03 Jun 2026 00:00:00 +0000

Notes on deep learning

A short dummy post about deep learning — placeholder content to exercise the Org → Hugo → deploy pipeline.

What is deep learning?

Deep learning is a branch of machine learning that uses neural networks with many layers to learn representations from data. Instead of hand-designed features, the model learns hierarchical features: early layers might detect edges, later layers objects or concepts.

Core ingredients

Data — labeled examples (images, text, audio) at sufficient scale
Architecture — CNNs for vision, transformers for language, and hybrids for multimodal tasks
Loss function — measures how wrong predictions are (cross-entropy, MSE, etc.)
Optimizer — SGD, Adam, and variants that update weights to reduce loss
Compute — GPUs or TPUs make training large models practical

A minimal mental model

Forward pass: input flows through the network to produce a prediction
Loss: compare prediction to the target
Backward pass: backpropagation computes gradients
Update: optimizer adjusts weights; repeat for many epochs

Why it matters

Deep learning powers modern speech recognition, machine translation, recommendation systems, and generative models. The same training loop — predict, measure error, update — scales from small experiments to billion-parameter models, with engineering around data, stability, and deployment becoming as important as the math.

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Notes on deep learning