Tag Archives: Machine Learning

AI and Machine Learning for On-Device Development

Description: AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it’s essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating and running models on popular mobile platforms such as iOS and Android. Laurence Moroney, lead […]

Designing Machine Learning Systems

Description: Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart. In this book, […]

Training Data for Machine Learning

Your training data has as much to do with the success of your data project as the algorithms themselves–most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how […]

MACHINE LEARNING WITH PYTORCH AND SCIKIT-LEARN

Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book Description: Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to […]

Graph Machine Learning: Take graph data to the next level

Key Features Implement machine learning techniques and algorithms in graph data Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. You […]

Machine Learning and AI for Healthcare

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Description: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and […]

Computer Vision and Machine Learning in Agriculture

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About the book: This book discusses computer vision, a noncontact as well as a nondestructive technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition which finds huge applications in agricultural productions. It also entails how rendering of machine learning techniques to computer vision algorithms is boosting this sector with […]

Machine Learning With Python For Everyone

Description of the books: The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for […]

Machine Learning for Financial Risk Management

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You’ll learn how to compare results from ML models with results obtained by traditional financial risk models. Author […]

Machine Learning for High-Risk Applications

Book description The past decade has witnessed a wide adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight into their widespread implementation has resulted in harmful outcomes that could have been avoided with proper oversight. Before we can realize AI/ML’s true benefit, practitioners must understand how to mitigate its risks. […]