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Mathematical Foundations
Linear algebra, calculus, probability, and the math every deep learning concept builds on.
Machine Learning Basics
Supervised/unsupervised learning, evaluation metrics, and the fundamentals behind every model.
Neural Network Fundamentals
Perceptrons, activation functions, forward propagation, and backpropagation from first principles.
Deep Neural Networks
Vanishing/exploding gradients, batch norm, dropout, optimizers, and training deep networks reliably.
Specialized Architectures
CNNs, RNNs, LSTMs, Transformers, LLMs, generative models, and deployment.