If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server’s response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you’re likely to encounter. Parse complicated HTML pages Develop crawlers with the Scrapy framework Learn methods to store data you scrape Read and extract data from documents Clean and normalize badly formatted data Read and write natural languages Crawl through forms and logins Scrape JavaScript and crawl through APIs Use and write image-to-text software Avoid scraping traps and bot blockers Use scrapers to test your website

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.

This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplines—such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating machine learning effectively into their research. Numerous texts delve into the technical execution of machine learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. For instance, a firm grasp of calculus is imperative to comprehend the training processes of algorithms and neural networks, while linear algebra is essential for the application and efficient training of various algorithms, including neural networks. Absent a solid mathematical base, machine learning applications may be, at best, cursory, or at worst, fundamentally flawed. This book lays the foundation for a comprehensive understanding of machine learning algorithms and approaches.

Learn how to build an end-to-end data to AI solution on Google Cloud using Vertex AI KEY FEATURES ? Harness the power of AutoML capabilities to build machine learning models. ? Learn how to train custom machine learning models on the Google Cloud Platform. ? Accelerate your career in data analytics by leveraging the capabilities of GCP. DESCRIPTION Google Cloud Vertex AI is a platform for machine learning (ML) offered by Google Cloud, with the objective of making the creation, deployment, and administration of ML models on a large scale easier. If you are seeking a unified and collaborative environment for your ML projects, this book is a valuable resource for you. This comprehensive guide is designed to help data enthusiasts effectively utilize Google Cloud Platform's Vertex AI for a wide range of machine learning operations. It covers the basics of the Google Cloud Platform, encompassing cloud storage, big query, and IAM. Subsequently, it delves into the specifics of Vertex AI, including AutoML, custom model training, model deployment on endpoints, development of Vertex AI pipelines, and the Explainable AI feature store. By the time you finish reading this book, you will be able to navigate Vertex AI proficiently, even if you lack prior experience with cloud platforms. With the inclusion of numerous code examples throughout the book, you will be equipped with the necessary skills and confidence to create machine learning solutions using Vertex AI. WHAT YOU WILL LEARN ? Learn how to create projects, store data in GCP, and manage access permissions effectively. ? Discover how AutoML can be utilized for streamlining workflows. ? Learn how to construct pipelines using TFX (TensorFlow Extended) and Kubeflow components. ? Gain an overview of the purpose and significance of the Feature Store. ? Explore the concept of explainable AI and its role in understanding machine learning models. WHO THIS BOOK IS FOR This book is designed for data scientists and advanced AI practitioners who are interested in learning how to perform machine learning tasks on the Google Cloud Platform. Having prior knowledge of machine learning concepts and proficiency in Python programming would greatly benefit readers. TABLE OF CONTENTS 1. Basics of Google Cloud Platform 2. Introduction to Vertex AI and AutoML Tabular 3. AutoML Image, Text, and Pre-built Models 4. Vertex AI Workbench and Custom Model Training 5. Vertex AI Custom Model Hyperparameter and Deployment 6. Introduction to Pipelines and Kubeflow 7. Pipelines using Kubeflow for Custom Models 8. Pipelines using TensorFlow Extended 9. Vertex AI Feature Store 10. Explainable AI

Machine Learning and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to machine learning with noisy labels that is suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching, machine learning methods. Most of the modern machine learning models based on deep learning techniques depend on carefully curated and cleanly labeled training sets to be reliably trained and deployed. However, the expensive labeling process involved in the acquisition of such training sets limits the number and size of datasets available to build new models, slowing down progress in the field. This book defines the different types of label noise, introduces the theory behind the problem, presents the main techniques that enable the effective use of noisy-label training sets, and explains the most accurate methods. Shows how to design and reproduce regression, classification and segmentation models using large-scale noisy-label training sets Gives an understanding of the theory of, and motivation for, noisy-label learning Shows how to classify noisy-label learning methods into a set of core techniques

Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectively

In the relentless battle against escalating cyber threats, data security faces a critical challenge – the need for innovative solutions to fortify encryption and decryption processes. The increasing frequency and complexity of cyber-attacks demand a dynamic approach, and this is where the intersection of cryptography and machine learning emerges as a powerful ally. As hackers become more adept at exploiting vulnerabilities, the book stands as a beacon of insight, addressing the urgent need to leverage machine learning techniques in cryptography. Machine Learning and Cryptographic Solutions for Data Protection and Network Security unveil the intricate relationship between data security and machine learning and provide a roadmap for implementing these cutting-edge techniques in the field. The book equips specialists, academics, and students in cryptography, machine learning, and network security with the tools to enhance encryption and decryption procedures by offering theoretical frameworks and the latest empirical research findings. Its pages unfold a narrative of collaboration and cross-pollination of ideas, showcasing how machine learning can be harnessed to sift through vast datasets, identify network weak points, and predict future cyber threats.

Over the past few years, Internet of Things has brought great changes to the world. Reports show that, the number of IoT devices is expected to reach 10 billion units within the next three years. The number will continue to rise and wildly use as infrastructure and housewares with each passing day, Therefore, ensuring the safe and stable operation of IoT devices has become more important for IoT manufacturers. Generally, four key aspects are involved in security risks when users use typical IoT products such as routers, smart speakers, and in-car entertainment systems, which are cloud, terminal, mobile device applications, and communication data. Security issues concerning any of the four may lead to the leakage of user sensitive data. Another problem is that most IoT devices are upgraded less frequently, which leads it is difficult to resolve legacy security risks in short term. In order to cope with such complex security risks,Security Companies in China, such as Qihoo 360, Xiaomi, Alibaba and Tencent, and companies in United States, e.g. Amazon, Google, Microsoft and some other companies have invested in security teams to conduct research and analyses, the findings they shared let the public become more aware of IoT device security-related risks. Currently, many IoT product suppliers have begun hiring equipment evaluation services and purchasing security protection products. As a direct participant in the IoT ecological security research project, I would like to introduce the book to anyone who is a beginner that is willing to start the IoT journey, practitioners in the IoT ecosystem, and practitioners in the security industry. This book provides beginners with key theories and methods for IoT device penetration testing; explains various tools and techniques for hardware, firmware and wireless protocol analysis; and explains how to design a secure IoT device system, while providing relevant code details.

In the past decades, cyber-physical systems (CPSs) have been widely applied to fields such as smart grids, environment monitoring, aerospace, smart transportation, and industrial automation. Great strides have been made in CPSs to improve the computing mechanism, communication, and quality of service by applying optimization algorithms. Currently, these efforts are integrated with the applications of machine learning (ML) and artificial intelligence (AI). To maintain system reliability and stability, CPSs such as smart grids face numerous challenges, including large-scale Internet-of-Things (IoT) device adaptation, ever-increasing demands of electrical energy, and the rise of a wide range of security threats. These challenges bring forth the need to find sustainable and advanced solutions to guarantee reliable and secure operations in these systems.

This book presents the latest developments in deep learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with deep learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of deep learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with deep learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, Alzheimer's disease, and genomes. Presents practical approaches of using the IoT with deep learning vision and how it deals with human dynamics Offers novel solution for medical imaging including skin lesion detection, cancer detection, enhancement techniques for MRI images, automated disease prediction, fraud detection, genomes, and many more Includes the latest technological advances in the IoT and deep learning with their implementations in healthcare Combines deep learning and analysis in the unified framework to understand both IoT and deep learning applications Covers the challenging issues related to data collection by sensors, detection and tracking of moving objects and solutions to handle relevant challenges Postgraduate students and researchers in the departments of computer science, working in the areas of the IoT, deep learning, machine learning, image processing, big data, cloud computing, and remote sensing will find this book useful.

This book provides essential future directions for IoT and Big Data research. Thanks to rapid advances in sensors and wireless technology, Internet of Things (IoT)-related applications are attracting more and more attention. As more devices are connected, they become potential components for smart applications. Thus, there is a new global interest in these applications in various domains such as health, agriculture, energy, security and retail. The main objective of this book is to reflect the multifaceted nature of IoT and Big Data in a single source. Accordingly, each chapter addresses a specific domain that is now being significantly impacted by the spread of soft computing.

This reference text discusses intelligent robotic and drone technology with embedded Internet of Things (IoT) for smart applications. The text discusses future directions of optimization methods with various engineering and science fundamentals used in robotics and drone-based applications. Its emphasis is on covering deep learning and similar models of neural network-based learning techniques employed in solving optimization problems of different engineering and science applications. It covers important topics including sensors and actuators in the internet of things (IoT), internet-of-robotics-things (IoRT), IoT in agriculture and food processing, routing challenges in flying Ad-hoc networks, and smart cities. The book will serve as a useful text for graduate students and professionals in the fields of electrical engineering, electronics engineering, computer science, and mechanical engineering.