Notes on deep learning
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.
This post is only a stub for site testing; replace it with real writing when ready.