This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.

Generative AI, from GANs to CLIP, with Python and PyTorch is a comprehensive and advanced-level course designed to guide learners through the fascinating world of generative artificial intelligence. The course takes a deep dive into how modern AI systems create images, understand text, and generate new content by exploring core architectures such as GANs (Generative Adversarial Networks), VAEs, and CLIP-based multimodal models. Learners will gain hands-on experience coding AI systems using Python and PyTorch, building models that can generate realistic images, manipulate visual content, and connect textual prompts with visual outputs. The course also explains key theoretical concepts in an intuitive way, helping students understand how neural networks learn representations, optimize latent spaces, and produce creative results. Through step-by-step coding exercises and real-world examples, students develop both theoretical knowledge and practical skills in deep learning and generative AI. This course is ideal for developers, data scientists, machine learning enthusiasts, and anyone looking to build a strong foundation in modern AI systems and creative machine learning applications.

The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice.

This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2021), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from April 10 to 11, 2021. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.

Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been benefitting from recent research efforts, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning. Deep Learning: Methods and Applications is a timely and important book for researchers and students with an interest in deep learning methodology and its applications in signal and information processing. "This book provides an overview of a sweeping range of up-to-date deep learning methodologies and their application to a variety of signal and information processing tasks, including not only automatic speech recognition (ASR), but also computer vision, language modeling, text processing, multimodal learning, and information retrieval. This is the first and the most valuable book for "deep and wide learning" of deep learning, not to be missed by anyone who wants to know the breathtaking impact of deep learning on many facets of information processing, especially ASR, all of vital importance to our modern technological society." - Sadaoki Furui, President of Toyota Technological Institute at Chicago, and Professor at the Tokyo Institute of Technology.

Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required―every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice.

This book is a short introduction to deep learning for readers with a STEM background. It aims at providing the necessary background to understand landmark AI models for image generation and language understanding.

This textbook aims at providing detailed explanations on how to design and construct image analysis workflows to successfully conduct bioimage analysis.