"Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks" by Umberto Michelucci is a comprehensive guide to understanding the fundamental principles of deep learning and the practical applications of deep neural networks. The book is geared towards individuals with a solid understanding of linear algebra, calculus, and probability theory.
The book provides a case-based approach, where the reader can learn by working through practical examples and case studies. The book covers various topics such as feedforward neural networks, convolutional neural networks, recurrent neural networks, autoencoders, and generative models. Each chapter is accompanied by Python code examples that illustrate the concepts being discussed.
One of the strengths of the book is that it provides a good balance between theory and practice. The author does a good job of explaining the underlying mathematical concepts and provides clear and concise explanations of how they are applied in practice. The book also covers important topics such as hyperparameter tuning, regularization, and optimization techniques.
Overall, "Applied Deep Learning" is an excellent resource for individuals looking to develop a solid understanding of deep learning and its practical applications. The book is well-written, easy to follow, and provides plenty of practical examples that can be easily adapted to a wide range of applications.
