Other books that might interest you

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  Working with Dinosaurs - How to...

  Marcus East

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  You already know what happens when dinosaurs and meteoric change come together. 
  Rapid change also impacts the digital world, and the choice for leaders is transformation or extinction. But adaptation can be challenging. Human teams and managers tend to resist change, and it takes effort and ingenuity to convince them to grow. 
  In Working with Dinosaurs, Marcus East, a leader in the digital technology industry for over two decades, shows other top executives how to disrupt outdated systems and mindsets and embrace new tech worlds like cloud computing and AI, so they can become next generation leaders of digital and tech transformation. 
  Ditching the dinosaurs means more than switching out old software and skills. It means re-envisioning the structure of an organization. It means creatively changing roles, capabilities, and culture. It means inspiring teams to move forward. 
  Digital transformation is here. Evolve to lead it.

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  Data Mesh Architecture -...

  Simon Winston

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  In an era where data reigns supreme, organizations are seeking innovative solutions to tame the ever-growing data landscape. "Demystifying Data Mesh" is your essential guide to understanding and implementing one of the most revolutionary concepts in data architecture: the Data Mesh. 
  With real-world case studies, practical examples, and actionable insights, "Demystifying Data Mesh" equips you with the knowledge and tools to implement Data Mesh in your organization. You'll discover how to ensure data quality, security, and governance while harnessing the full potential of your data assets. 
  As the data landscape continues to evolve, staying ahead of the curve is essential. This book not only provides a deep understanding of Data Mesh but also explores the future trends and possibilities it brings to the table, including its relationship with AI, machine learning, and the cloud. 
  Whether you're a data engineer, data scientist, data analyst, or a business leader looking to make data-driven decisions, this book is your gateway to unlocking the true potential of your data. "Demystifying Data Mesh" empowers you to revolutionize your data architecture and harness the power of data like never before. 
   

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  Interpretability and...

  Aruna Chakkirala

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  Demystify AI Decisions and Master Interpretability and Explainability Today
  Book Description
  Interpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust.
  Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models.
  You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals—powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems.
  Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape.
  Table of Contents
  1. Interpreting Interpretable Machine Learning
  2. Model Types and Interpretability Techniques
  3. Interpretability Taxonomy and Techniques
  4. Feature Effects Analysis with Plots
  5. Post-Hoc Methods
  6. Anchors and Counterfactuals
  7. Interpretability in Neural Networks
  8. Explainable Neural Networks
  9. Explainability in Transformers and Large Language Models
  10. Explainability and Responsible AI      
  
  Index

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  Freight broker business startup...

  Randy Russell

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  This comprehensive guide serves as your essential roadmap, meticulously crafted to demystify the complex landscape of freight brokerage, providing aspiring entrepreneurs with the foundational knowledge and practical insights needed to launch a successful venture. 
  Within the pages of "Freight Broker Business Startup," you'll explore: 
  - Introduction to Freight Brokerage: Gain a comprehensive understanding of the fundamentals of freight brokerage, from the role of a broker in the supply chain to key industry terminology. 
  - Application Processes: Navigate the intricacies of applying for and obtaining the necessary licenses and permits. This guide provides step-by-step instructions to streamline the application process and ensure compliance with regulatory requirements. 
  - Business Training Requirements: Delve into the essential training needed to excel in the freight brokerage industry. From negotiation skills to customer relations, this guide covers the foundational aspects that will set you on the path to becoming a successful freight broker. 
  - Insider Tips: Benefit from insider insights and practical tips from industry professionals, offering valuable perspectives to guide you through the early stages of your freight brokerage business. 
  - User-Friendly Format: Designed with clarity in mind, the user-friendly format makes complex concepts accessible to beginners, ensuring a smooth learning curve for those entering the world of freight brokerage. 
  Embark on your entrepreneurial journey in the logistics industry—pick up "Freight Broker Business Startup" and lay the foundation for a thriving career in freight brokerage.

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  Machine learning - Demystifying...

  Et Tu Code

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  Dive into the fascinating realm of artificial intelligence with 'Machine Learning: Demystifying the World of Intelligent Systems'. This comprehensive guide offers a clear and concise journey through the intricate landscape of machine learning. From its historical roots in the 1950s to cutting-edge advancements, the book covers everything from fundamental concepts to advanced techniques. 
  Explore the core pillars of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. Understand how machines learn from labeled and unlabeled data, model complex relationships, and make decisions to maximize rewards. 
  Discover the critical role of feature engineering and evaluation metrics in crafting high-performing models. Learn about the ethical considerations surrounding AI technologies and their real-world applications across diverse industries, from healthcare to finance. 
  Whether you're a novice eager to grasp the basics or a seasoned practitioner seeking to stay abreast of recent advances, this book equips you with the knowledge and tools essential to navigate the ever-evolving landscape of machine learning. With insights into popular algorithms, tools, and libraries, as well as discussions on recent trends like transfer learning and generative models, 'Machine Learning' is your indispensable companion in unraveling the mysteries of intelligent systems.

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  A Year of Ebola

  David Brett-Major

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  Who should read this book? Someone on their way out the door for their first or an early career health emergency and disaster risk management (HEDRM) experience, regardless of role, or someone who is trying to support one who is, or who does it all of the time and wants to know that they are not alone 
  What does this book tell the reader? It provides the shock of jumping into the cold water of health emergency and disaster risk management (HEDRM), without the risk of drowning; it focuses on practical aspects of the experience of doing the work that guidelines and playbooks (as important as they are) do not address 
  Who else might be interested in this book? People keen to understand how HEDRM happens in the context of highly communicable diseases, or about how global public health, emergencies, aid, and development mix, or medical history... the technical and contextual curious 
  Who should NOT read this book? Anyone who is looking for gory details 
  What is the main strength of this book? It is a raw view of the experience of conducting such responses at the clinical: public health interface, with some lessons durable across emergencies 
  What is the main weakness of this book? It carries all of the biases of such experiences. There is a lot not said; the book has snapshots and cannot relate the 10,000,000 events during the year not described 
  What is new in the 2024 edition? In print and e-book, overdue editing without loss of content, a forward that reflects on the COVID-19 pandemic in the context of themes in the book, and in the print edition available on www.amazon.com, a series of essays stepping back and exploring the ways in which people interact with the threat of infectious diseases that defines our willingness and readiness to meet them

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