F# is a multi-paradigm programming language that encompasses object-oriented, imperative, and functional programming language properties. The F# functional programming language enables developers to write simple code to solve complex problems. Starting with the fundamental concepts of F# and functional programming, this book will walk you through basic problems, helping you to write functional and maintainable code. Using easy-to-understand examples, you will learn how to design data structures and algorithms in F# and apply these concepts in real-life projects. The book will cover built-in data structures and take you through enumerations and sequences. You will gain knowledge about stacks, graph-related algorithms, and implementations of binary trees. Next, you will understand the custom functional implementation of a queue, review sets and maps, and explore the implementation of a vector. Finally, you will find resources and references that will give you a comprehensive overview of F# ecosystem, helping you to go beyond the fundamentals.
Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook Description Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is for Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.
Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn ethical AI principles, frameworks, and governance Understand the concepts of fairness assessment and bias mitigation Introduce explainable AI and transparency in your machine learning models Book DescriptionResponsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations. By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.What you will learn Understand explainable AI fundamentals, underlying methods, and techniques Explore model governance, including building explainable, auditable, and interpretable machine learning models Use partial dependence plot, global feature summary, individual condition expectation, and feature interaction Build explainable models with global and local feature summary, and influence functions in practice Design and build explainable machine learning pipelines with transparency Discover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platforms Who this book is for This book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.
Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday developers, by providing a combination of tools such as cognitive services APIs, machine learning platforms, and libraries. Along with an overview of the contemporary technology landscape, Machine Learning and Deep Learning with Cognitive Computing Recipes covers the business case for machine learning and deep learning. Covering topics such as digital assistants, computer vision, text analytics, speech, and robotics process automation this book offers a comprehensive toolkit that you can apply quickly and easily in your own projects. With its focus on Microsoft Cognitive Services offerings, you’ll see recipes using multiple different environments including TensowFlow and CNTK to give you a broader perspective of the deep learning ecosystem. What You Will LearnBuild production-ready solutions using Microsoft Cognitive Services APIs Apply deep learning using TensorFlow and Microsoft Cognitive Toolkit (CNTK) Solve enterprise problems in natural language processing and computer vision Discover the machine learning development life cycle – from formal problem definition to deployment at scale Who This Book Is For Software engineers and enterprise architects who wish to understand machine learning and deep learning by building applications and solving real-world business problems.
Use this hands-on guide book to learn and explore cognitive APIs developed by Microsoft and provided with the Azure platform. This book gets you started working with Azure Cognitive Services. You will not only become familiar with Cognitive Services APIs for applications, but you will also be exposed to methods to make your applications intelligent for deployment in businesses. The book starts with the basic concepts of Azure Cognitive Services and takes you through its features and capabilities. You then learn how to work inside the Azure Marketplace for Bot Services, Cognitive Services, and Machine Learning. You will be shown how to build an application to analyze images and videos, and you will gain insight on natural language processing (NLP). Speech Services and Decision Services are discussed along with a preview of Anomaly Detector. You will go through Bing Search APIs and learn how to deploy and host services by using containers. And you will learn how to use Azure Machine Learning and create bots for COVID-19 safety, using Azure Bot Service. After reading this book, you will be able to work with datasets that enable applications to process various data in the form of images, videos, and text. What You Will Learn Discover the options for training and operationalizing deep learning models on Azure Be familiar with advanced concepts in Azure ML and the Cortana Intelligence Suite architecture Understand software development kits (SKDs) Deploy an application to Azure Kubernetes Service Who This Book Is For Developers working on a range of platforms, from .NET and Windows to mobile devices, as well as data scientists who want to explore and learn more about deep learning and implement it using the Microsoft AI platform
Which Cloud? is the only comprehensive introduction to cloud computing that compares the top three cloud platforms: AWS, Microsoft Azure, and Google Cloud. It’s for all disciplines in software (anyone who wants to understand how the cloud platforms are similar and different), for anyone who is new to cloud development and management, and for anyone who is seasoned with cloud development, management, and architecture. This book will help you understand the cloud platforms, make the best decisions, convince others of your decisions, create cross-cloud solutions, migrate between the cloud platforms, and simply communicate to others who have more experience in a different cloud platform! The book addresses three key challenges: Understanding equivalent services to migrate to from a different cloud platform. Understanding the service and feature-level values gained across the different cloud platforms. Understanding effective ways to plan for or architect a cross-cloud solution. The authors begin with service-level comparisons, but they also dig into feature-level comparisons. They compare all three platforms together, to make it simple to understand the differences between all three cloud platforms. They use matrices, diagrams, and conceptual explanations to walk the reader through the comparisons. By the end of this book, you’ll have reached the next point in your cloud solution journey. Maybe that means you’re a student… you’ll now have a much stronger high-level view of all the platforms (and understand how to compare them). Or, if you’re a seasoned architect or developer, you’ll now have a better understanding of how to do your job… whether it’s for cross-cloud solutions, migrations, decision-making, or simply improved communication with coworkers.
Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday developers, by providing a combination of tools such as cognitive services APIs, machine learning platforms, and libraries. Along with an overview of the contemporary technology landscape, Machine Learning and Deep Learning with Cognitive Computing Recipes covers the business case for machine learning and deep learning. Covering topics such as digital assistants, computer vision, text analytics, speech, and robotics process automation this book offers a comprehensive toolkit that you can apply quickly and easily in your own projects. With its focus on Microsoft Cognitive Services offerings, you’ll see recipes using multiple different environments including TensowFlow and CNTK to give you a broader perspective of the deep learning ecosystem. What You Will LearnBuild production-ready solutions using Microsoft Cognitive Services APIs Apply deep learning using TensorFlow and Microsoft Cognitive Toolkit (CNTK) Solve enterprise problems in natural language processing and computer vision Discover the machine learning development life cycle – from formal problem definition to deployment at scale Who This Book Is For Software engineers and enterprise architects who wish to understand machine learning and deep learning by building applications and solving real-world business problems.
F# is a multi-paradigm programming language that encompasses object-oriented, imperative, and functional programming language properties. The F# functional programming language enables developers to write simple code to solve complex problems. Starting with the fundamental concepts of F# and functional programming, this book will walk you through basic problems, helping you to write functional and maintainable code. Using easy-to-understand examples, you will learn how to design data structures and algorithms in F# and apply these concepts in real-life projects. The book will cover built-in data structures and take you through enumerations and sequences. You will gain knowledge about stacks, graph-related algorithms, and implementations of binary trees. Next, you will understand the custom functional implementation of a queue, review sets and maps, and explore the implementation of a vector. Finally, you will find resources and references that will give you a comprehensive overview of F# ecosystem, helping you to go beyond the fundamentals.
Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook Description Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is for Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.
Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn ethical AI principles, frameworks, and governance Understand the concepts of fairness assessment and bias mitigation Introduce explainable AI and transparency in your machine learning models Book DescriptionResponsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations. By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.What you will learn Understand explainable AI fundamentals, underlying methods, and techniques Explore model governance, including building explainable, auditable, and interpretable machine learning models Use partial dependence plot, global feature summary, individual condition expectation, and feature interaction Build explainable models with global and local feature summary, and influence functions in practice Design and build explainable machine learning pipelines with transparency Discover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platforms Who this book is for This book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.
Arab Spring sparked to bring down dictatorships, but soon succumbed to old blood and political wrongs. The most venomous being wrongs of the Iraq War, which opened a tin of worms of old blood on a fragile world order. Again, desperate or fainthearted players quickly doctored it to test big players' resolve in committing to justice. Veering right wing reactionaries in racism and nationalism deepened disintegration and divide in universal human relations in a borderless globe, with remnants of precipitates from the past. This brought afloat old blood and its bitterness, blown in religious violence and terrorism. The book examines a live example of militarism, spawning terrorism through inflaming religious sentiments and ethnic residual tensions via faint-hearted democracies. Sudan served as a microscopic slide to examine, by virtue of its plague of militarism armed conflicts, lucrative riches, projected disharmony and dark slavery history, helpful for how past follies must be heeded
Patchwork States argues that the subnational politics of conflict and competition in South Asian countries have roots in the history of uneven state formation under colonial rule. Colonial India contained a complex landscape of different governance arrangements and state-society relations. After independence, postcolonial governments revised colonial governance institutions, but only with partial success. The book argues that contemporary India and Pakistan can be usefully understood as patchwork states, with enduring differences in state capacity and state-society relations within their national territories. The complex nature of territorial governance in these countries shapes patterns of political violence, including riots and rebellions, as well as variations in electoral competition and development across the political geography of the Indian subcontinent. By bridging past and present, this book can transform our understanding of both the legacies of colonial rule and the historical roots of violent politics, in South Asia and beyond.
Patchwork States argues that the subnational politics of conflict and competition in South Asian countries have roots in the history of uneven state formation under colonial rule. Colonial India contained a complex landscape of different governance arrangements and state-society relations. After independence, postcolonial governments revised colonial governance institutions, but only with partial success. The book argues that contemporary India and Pakistan can be usefully understood as patchwork states, with enduring differences in state capacity and state-society relations within their national territories. The complex nature of territorial governance in these countries shapes patterns of political violence, including riots and rebellions, as well as variations in electoral competition and development across the political geography of the Indian subcontinent. By bridging past and present, this book can transform our understanding of both the legacies of colonial rule and the historical roots of violent politics, in South Asia and beyond.
How do we understand the nature and diversity of populist politics, in developed and developing countries? Righteous Demagogues provides a novel approach grounded in democratic theory, inequality, and party competition. It argues that populists are successful when they evoke the moral contract--that states are obligated to redress certain types of inequality--and promise its restoration, in ways that resonate across the normal lines of social division and partisanship. These changes in political competition can spur confrontations with the opposition and state institutions, leading to populist rejection or authoritarian governance.
This research revolves around the transformations in the life and thought of radical Islamist Sayyid Qutb of Egypt (1906-1966), a prolific writer, a poet, an educator, a literary critic, and a highly controversial ideologue of contemporary Islamism who was executed by the late-President Nasser regime of Egypt on August 29, 1966. His posthumous impact on radical Islamists was profound on some leaders in Iran and Afghanistan and on al-Qaeda and its leaders, especially the late Dr. Ayman al-Zawahiri and fellow global jihadist Abdallah Azzam and many others, including the late-blind cleric Sheikh Omar Abd al-Rahman who immigrated and died in the United States.
The book covers a wide range of topics of relevance to policymakers in countries that have sovereign wealth funds (SWFs) and those that receive SWF investments. Renowned experts in the field have contributed chapters. The book is organized around four themes: (1) the role and macrofinancial linkages of SWFs, (2) institutional factors, (3) investment approaches and financial markets, and (4) the postcrisis outlook. The book also discusses the challenges facing sovereign wealth funds in the coming years, from an inside perspective on countries, including Canada, Chile, China, Norway, Russia, and New Zealand. Economics of Sovereign Wealth Funds will contribute to a further understanding of the nature, strategies and behavior of SWFs and the environment in which they operate, as their importance is likely to grow in the coming years.
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