Classical algorithms — linear regression, SVM, boosting — work well on tabular data. But a 224×224 image is 150 thousand features. A word in text is a vector with thousands of dimensions. Deep neural networks were built for such problems: hierarchical functions that learn the right features. This chapter is built around PyTorch — from the perceptron to modern optimizers. Each lesson is one fundamental building block.