Understanding Deep Learning

Understanding Deep Learning is a comprehensive introduction to the theory and practice of deep learning, designed to help readers understand how modern neural networks learn from data and solve complex problems. The book covers core concepts such as linear models, optimization, backpropagation, convolutional neural networks, recurrent neural networks, transformers, generative models, and reinforcement learning. Combining intuitive explanations, mathematical foundations, and practical examples, it provides students, researchers, and machine learning practitioners with the knowledge needed to build, evaluate, and understand deep learning models across a wide range of real-world applications.