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Mathematical Foundations

Linear algebra, calculus, and probability concepts essential for deep learning.

Deep Learning

Neural networks from perceptrons to modern architectures.

PyTorch

Practical PyTorch — tensors, autograd, training loops, and best practices.

Large Language Models

Transformers, attention mechanisms, pretraining, and fine-tuning.

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