Bimal Kujur (37 results)

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Paperback. Condition: new. Paperback. Machine learning is revolutionizing industries by enabling computers to learn from data and make intelligent decisions. At the heart of machine learning lies linear algebra - a fundamental mathematical framework that powers algorithms, optimizations, and data transformations. This book, Linear Algebra for Machine Learning: Foundations and Applications, aims to bridge the gap between theoretical concepts and practical applications by providing an intuitive understanding of linear algebra's role in machine learning models.This book is structured to cater to both beginners and experienced practitioners. It starts with foundational concepts of linear algebra, including vectors, matrices, and eigenvalues, before progressing to their applications in machine learning. Each includes theoretical explanations accompanied by hands-on coding demonstrations to reinforce learning through practical implementation.By the end of this book, readers will gain a solid grasp of how linear algebra is employed in machine learning algorithms such as Support Vector Machines, Neural Networks, and Principal Component Analysis. The combination of mathematical insights and code demonstrations will equip readers with the skills necessary to develop, optimize, and interpret machine learning models effectively.Whether you are a student, researcher, or professional, this book serves as a comprehensive guide to understanding and applying linear algebra in the field of machine learning. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. This book is designed to bridge the gap between the mathematical foundations of calculus and their practical applications in the rapidly evolving field of machine learning (ML). Whether you are a student, a researcher, or a practitioner, this book aims to provide you with a comprehensive understanding of how calculus underpins many of the algorithms and techniques that drive modern ML.The Intersection of Calculus and Machine LearningMachine learning has transformed the way we approach data, enabling us to build models that can learn from and make predictions on complex datasets. At the heart of many ML algorithms lies calculus, the branch of mathematics that deals with rates of change and accumulation. From optimizing loss functions to training neural networks, calculus provides the tools necessary to understand and improve these models.This book is structured to take you on a journey from the fundamental concepts of calculus to their advanced applications in ML. We begin with a review of essential calculus topics, ensuring that readers have a solid foundation. We then delve into more specialized areas, such as gradient descent, backpropagation, and optimization techniques, illustrating how these concepts are applied in real-world ML problems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. In an era where artificial intelligence shapes how we work, create, and connect, the ability to communicate effectively with AI systems has become a transformative skill. The Prompt Engineer's Handbook: Mastering AI Conversations is born from the recognition that prompt engineering-the art and science of crafting precise, intentional instructions for AI-is not just a technical discipline but a gateway to unlocking AI's vast potential.This book is designed for anyone eager to harness AI conversations, whether you're a developer automating workflows, a marketer crafting campaigns, an educator designing lessons, or a creative exploring new frontiers. It distills years of practical experience and cutting-edge insights into a comprehensive guide, offering strategies, templates, and ethical frameworks to create impactful AI interactions. From text and voice to multimodal prompting, we cover the tools and techniques to make AI a powerful ally.Our journey began with a simple question: How can we make AI not just responsive, but truly collaborative? The answer lies in thoughtful prompt design, iterative refinement, and a commitment to fairness and responsibility. This handbook empowers you to ask better questions, elicit better responses, and shape AI's role in a rapidly evolving world.As you turn these pages, you'll find a blend of practical tutorials, real-world examples, and forward-looking insights, all grounded in the realities of 2025's AI landscape. My hope is that this book inspires you to experiment boldly, think critically, and lead ethically in the age of AI conversations. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Reinforcement Learning (RL) is one of the most exciting and transformative fields in artificial intelligence. From mastering complex games like Go and Chess to enabling autonomous vehicles and revolutionizing healthcare, RL has demonstrated its potential to solve some of the most challenging problems across diverse domains. This book, AI's Decision-Making Engine: Reinforcement Learning Explained, is designed to take you on a comprehensive journey through the world of RL, from its fundamental principles to its cutting-edge advancements and real-world applications.When I first began exploring RL, I was captivated by its elegance and power. The idea that an agent could learn to make optimal decisions through trial and error, much like humans do, was both inspiring and profound. However, I also quickly realized that RL is a complex and multifaceted field, requiring a deep understanding of mathematics, algorithms, and practical implementation. This book is my attempt to bridge that gap, providing a structured and accessible guide for students, researchers, and practitioners alike.The book is organized into 13 chapters, each building on the previous one to create a cohesive and comprehensive understanding of RL. We start with the basics, introducing the core concepts of RL and its mathematical foundations. From there, we explore key algorithms, including value-based methods, policy-based methods, and advanced techniques like Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC). Along the way, we delve into practical applications, from game AI and robotics to finance and healthcare, showcasing how RL is transforming industries and solving real-world problems.But this book is not just about algorithms and applications. It also addresses the challenges and ethical considerations that come with developing and deploying RL systems. From sample efficiency and generalization to fairness, safety, and accountability, we examine the critical issues that must be addressed to ensure that RL technologies are not only effective but also responsible and beneficial to society.Finally, we look to the future, exploring emerging trends in RL research and its potential role in the development of Artificial General Intelligence (AGI). By understanding these directions, we can envision how RL will shape the future of AI and its impact on our world.This book is the culmination of years of research, teaching, and practical experience in RL. It is my hope that it will serve as a valuable resource for anyone interested in learning about RL, whether you are a student just starting out, a researcher pushing the boundaries of the field, or a practitioner applying RL to solve real-world problems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. "Mastering Azure Machine Learning: From Fundamentals to Deployment" is designed to bridge the gap between foundational ML concepts and the complexities of deploying production-ready systems. While Azure ML's intuitive interface and robust APIs simplify many tasks, mastery of the platform requires an understanding of the underlying concepts, tools, and workflows. This book equips you with the skills and knowledge to navigate every stage of the machine learning lifecycle using Azure ML-from data preparation and model development to deployment, monitoring, and scaling.This book caters to a broad audience: Beginners: Individuals new to machine learning who want a structured guide to mastering Azure ML's features while understanding fundamental ML concepts.Intermediate Practitioners: Those with some experience in ML who wish to deepen their knowledge of Azure ML's advanced capabilities, such as AutoML, distributed training, and pipeline orchestration.Professionals: Industry experts seeking insights into implementing scalable and efficient ML solutions in real-world scenarios.Regardless of your starting point, this book emphasizes practical examples, clear explanations, and actionable insights to accelerate your Azure ML journey.What You Will LearnThe book's modular structure ensures a comprehensive learning experience: Foundations: Introduction to Azure ML, its architecture, and core capabilities.Model Development: Hands-on guidance for building and training models, including data preprocessing, experimentation, and hyperparameter tuning.AutoML and Responsible AI: Insights into automation, interpretability, and fairness to ensure ethical and impactful solutions.Advanced Techniques: Distributed training, embedding models, and working with large language models.Deployment: Real-world approaches to deploying, monitoring, and scaling ML systems on Azure.Case Studies and Best Practices: Success stories across industries and actionable best practices for leveraging Azure ML effectively. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Taschenbuch. Condition: Neu. Neuware - 'Mastering Azure Machine Learning: From Fundamentals to Deployment' is designed to bridge the gap between foundational ML concepts and the complexities of deploying production-ready systems. While Azure ML's intuitive interface and robust APIs simplify many tasks, mastery of the platform requires an understanding of the underlying concepts, tools, and workflows. This book equips you with the skills and knowledge to navigate every stage of the machine learning lifecycle using Azure ML-from data preparation and model development to deployment, monitoring, and scaling.This book caters to a broad audience:

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Paperback. Condition: new. Paperback. Machine learning is revolutionizing industries by enabling computers to learn from data and make intelligent decisions. At the heart of machine learning lies linear algebra - a fundamental mathematical framework that powers algorithms, optimizations, and data transformations. This book, Linear Algebra for Machine Learning: Foundations and Applications, aims to bridge the gap between theoretical concepts and practical applications by providing an intuitive understanding of linear algebra's role in machine learning models.This book is structured to cater to both beginners and experienced practitioners. It starts with foundational concepts of linear algebra, including vectors, matrices, and eigenvalues, before progressing to their applications in machine learning. Each includes theoretical explanations accompanied by hands-on coding demonstrations to reinforce learning through practical implementation.By the end of this book, readers will gain a solid grasp of how linear algebra is employed in machine learning algorithms such as Support Vector Machines, Neural Networks, and Principal Component Analysis. The combination of mathematical insights and code demonstrations will equip readers with the skills necessary to develop, optimize, and interpret machine learning models effectively.Whether you are a student, researcher, or professional, this book serves as a comprehensive guide to understanding and applying linear algebra in the field of machine learning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.