Deep Learning
Neural networks that learn layered representations directly from data — the engine behind modern vision, language, and speech systems.
artificial neurons
neural network basics
backpropagation
optimization
regularization & normalization
CNNs
RNNs & sequence models
transformers & attention
training dynamics
Topic overview
Study layers, activations, loss functions, backpropagation, optimization, regularization, CNNs, RNNs, and transformers — the building blocks stacked to model complex patterns in language, vision, audio, and structured data.
Core concepts
The MP neuron and perceptron; perceptrons and nonlinear activations; the chain rule as backprop; gradient-based optimizers (SGD, momentum, RMSProp, Adam); dropout, batch/layer norm, and weight decay; convolution and spatial locality; recurrence and gating (LSTM/GRU); self-attention and positional encoding; initialization and transfer learning.
Why it matters
Deep learning powers most modern AI systems — from recommendation and vision pipelines to the transformer backbones behind LLMs — but reliability depends on understanding the training mechanics, not just calling a framework API.
Interview relevance
Conceptual clarity matters more than framework usage: explain why an architecture, optimizer, or regularizer fits a problem, and reason about gradients, capacity, and failure modes from first principles.