Fundamental Mathematical Concepts for Machine Learning in Science, Springer Nature 2024, Text Book in Computer Science
The textbook "Fundamental Mathematical Concepts for Machine Learning in Science" by Umberto Michelucci, published by Springer Nature in 2024, aims to provide a comprehensive foundation in the essential mathematical principles needed for applying machine learning across various scientific disciplines. It is particularly suited for individuals with a scientific background in fields such as physics, chemistry, biology, and medicine. The book covers topics like calculus, linear algebra, statistics, probability, and model validation, making these complex concepts accessible and directly applicable to machine learning tasks in scientific research.
Applied Deep Learning with TensorFlow 2
https://link.springer.com/book/10.1007/978-1-4842-8020-1
Applied Deep Learning with TensorFlow 2, authored by Umberto Michelucci in 2022 and published by Springer Nature/Apress, is a comprehensive guide that delves into the practical applications of deep learning using TensorFlow 2. This book presents an in-depth exploration of TensorFlow 2's capabilities, offering readers a hands-on approach to mastering deep learning techniques. With a focus on real-world examples and case studies, Michelucci expertly guides readers through complex concepts, making them accessible to both beginners and experienced practitioners. The book serves as an essential resource for those seeking to enhance their skills in modern AI technologies and their applications in various domains.
Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection
https://link.springer.com/book/10.1007/978-1-4842-4976-5
Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection, authored by Umberto Michelucci and published in 2019 by Springer Nature, is a pivotal text in the field of deep learning. This book offers an in-depth exploration of advanced techniques in convolutional neural networks (CNNs) and object detection, tailored for professionals and researchers. Michelucci combines theoretical foundations with practical applications, providing detailed insights into the design and implementation of CNNs in various real-world scenarios. The book is a valuable asset for those looking to deepen their understanding of complex AI methodologies and their effective deployment in solving intricate problems in image recognition and computer vision.
Applied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks
https://link.springer.com/book/10.1007/978-1-4842-4976-5
Applied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks, authored by Umberto Michelucci in 2018 and published by Springer Nature/Apress, is a significant contribution to the field of deep learning. This book provides an intuitive understanding of deep neural networks through a unique case-based approach, making complex concepts accessible to a wide range of readers. Michelucci skillfully blends theoretical knowledge with practical examples, enabling readers to grasp the fundamentals of deep learning and apply them to real-world problems. The book is an invaluable resource for students, professionals, and enthusiasts eager to explore and master the intricacies of deep neural networks in various applications.