Introduction to Deep Learning: Concepts, Examples, and Learning Resources

The author introduces a deep‑learning lecture series, explaining that deep learning is a subset of machine learning used to solve various tasks, and emphasizes understanding its operation rather than memorizing terminology.

Machine learning is broken down into four key parts—Digital Input, Digital Output, Criterion, and Mapping—and the article illustrates each part with concrete examples, such as classifying images of hot dogs and predicting house prices.

It then discusses how to express the cost (loss) as a function of model parameters (e.g., w1 ) and shows how gradient descent can iteratively adjust these parameters by examining the sign of the derivative to reduce the cost.

A concrete code example (using Lua and the Torch framework) demonstrates the forward and backward passes, where the forward pass computes the mapping w1 * x1 + w2 * x2 + b and the criterion measures the squared error, while the backward pass calculates gradients for parameter updates.

The article explains the chain rule for back‑propagation, detailing how derivatives flow through multiple nested functions (f, g, k) to compute the gradient of the overall cost with respect to each weight.

It also describes how stacking multiple mappings creates deep neural networks with many layers, and how the same gradient‑based optimization applies to these deeper structures.

Finally, the author lists extensive learning materials—including the textbook *Deep Learning* by Goodfellow et al., Coursera’s machine‑learning course by Andrew Ng, Stanford’s CS231n video lectures, and code resources for both Torch (Lua) and PyTorch (Python)—and provides reference links to videos, slides, and documentation.