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  • Sách/Book

  • Modeling and optimization of signals using machine learning techniques

    • Authors: Chandra Singh (2024)

  • This book addresses the role of machine learning in transforming vast signal databases from sensor networks, internet services, and communication systems into actionable decision systems. It explores the development of computational solutions and novel models to handle complex real-world signals such as speech, music, biomedical data, and multimedia.
  • Sách/Book

  • Effective machine learning teams : best practices for ML practitioners

    • Authors: David Tan (2024)

  • "Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to apply time-tested software engineering skills and Lean product delivery practices to reduce toil and waste, shorten feedback loops, and improve your team's flow when building ML systems and products. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common traps in the ML world, so you can iterate and scale more quickly and reliably. You'll learn how...
  • Sách/Book

  • Applied artificial intelligence in data science, cloud computing and IoT frameworks/

    • Authors: Nilayam Kumar Kamila (2024)

  • This volume reviews cutting-edge innovations in blockchain technology that are propelling the healthcare industry into a new era of efficiency and security. It brings 14 reviews contributed by experts in blockchain and Web3 technologies into a single volume. Each contribution includes a summary for easy understanding and scientific references for advanced readers.
  • Sách/Book

  • Deep learning for finance : creating machine and deep learning models for trading in Python

    • Authors: Sofien Kaabar (2024)

  • "Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar--financial author, trading consultant, and institutional market strategist--introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analy...
  • Sách/Book

  • Machine learning with python programming: 2023 a beginners guide The Definitive Guide to Mastering Machine Learning in Python and a Problem-Guide Solver to Creating Real-World Intelligent Systems

    • Authors: James Harrison (2024)

  • "Machine Learning With Python Programming : 2023 A Beginners Guide" is the book you've been waiting for. This comprehensive guide takes you on an exciting journey from the basics of Python programming to the depths of neural networks and deep learning. It demystifies the complex world of machine learning, making it accessible and understandable, regardless of your background. James begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques...
  • Sách/Book

  • Python Machine Learning A Step-by-Step Journey with Scikit-Learn and Tensorflow for Beginners

    • Authors: Chloe Annable (2024)

  • This book is crafted with beginners in mind, providing clear, step-by-step instructions and straightforward language, making it an ideal starting point for anyone intrigued by this captivating subject. Python, with its immense capabilities, opens up a world of possibilities, and this guide will set you on the path to harnessing its potential.
  • Sách/Book

  • Machine learning for decision makers : cognitive computing fundamentals for better decision making

    • Authors: Patanjali Kashyap (2024)

  • This new and updated edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry problems. This second edition builds upon the foundation of the first book, revises all of the chapters, and updates the research, case studies, and practical examples to bring the book up to date with changes that have occurred in machine learning. A new chapter on quantum computers and machine learning is included to prepare you for future challenges.
  • Sách/Book

  • Machine learning production systems engineering machine learning models and pipelines

    • Authors: Robert Crowe (2024)

  • This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines
  • Sách/Book

  • Java and Algorithmic Thinking for the Complete Beginner learn to think like a programmer

    • Authors: Aristides S. Bouras (2024)

  • What you will learn Understand the fundamentals of how computers work Master Java programming basics and IDEs Develop proficiency in handling operators, and trace tables Implement sequence and decision control structures in programming Manipulate numbers, strings, and complex expressions Utilize arrays, HashMaps, and other data structures effectively Who this book is for This course is perfect for complete beginners with no prior programming experience, including high school students and hobbyists. It is also suitable for those with a basic understanding of computers who wish to deepen their knowledge of Java and algorithmic thinking