Foundations of Deep Neural Networks and Representation Learning

Deep learning has revolutionized how we process complex data, but understanding what actually happens inside the hidden layers of a neural network is key to building effective models. This text-only course guides you through the core mechanics of representation learning, showing you how networks extract hierarchical patterns without manual feature engineering. By the end of this course, you will transition from seeing neural networks as black boxes to understanding how multi-layered architectures construct internal data representations. You will learn how to conceptualize, trace, and explain the flow of data through a network, preparing you for modern AI workflows. What you'll learn: Understand foundational concepts of neural networks, including activation functions, weights, biases, and loss functions; Explore representation learning and how hidden layers automatically extract features from raw inputs; Learn the mechanics of forward propagation and backpropagation in multi-layer networks; Analyze modern representation techniques, including vector embeddings and latent spaces; Practice tracing data transformations through clear written explanations and pythonic code snippets. The course starts with essential terminology and the basic mathematical intuition behind single neurons, then progresses systematically to deep architectures and modern representation learning paradigms. Designed for beginners with basic programming curiosity, this course requires no prior deep learning experience. Start reading today to unlock the inner workings of deep neural networks.