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Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasetsKey FeaturesFamiliarize yourself with data processing, performance measuring, and model selection using various C++ librariesImplement practical machine learning and deep learning techniques to build smart modelsDeploy machine learning models to work on mobile and embedded devicesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWritten by a seasoned software engineer with several years of industry experience, this book will teach you the basics of machine learning (ML) and show you how to use C++ libraries, along with helping you create supervised and unsupervised ML models. You'll gain hands-on experience in tuning and optimizing a model for various use cases, enabling you to efficiently select models and measure performance. The chapters cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries. You'll also learn how to overcome production and deployment challenges on mobile platforms, and see how the ONNX model format can help you accomplish these tasks. This new edition has been updated with key topics such as sentiment analysis implementation using transfer learning and transformer-based models, as well as tracking and visualizing ML experiments with MLflow. An additional section shows you how to use Optuna for hyperparameter selection. The section on model deployment into mobile platform now includes a detailed explanation of real-time object detection for Android with C++. By the end of this C++ book, you'll have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.What you will learnEmploy key machine learning algorithms using various C++ librariesLoad and pre-process different data types to suitable C++ data structuresFind out how to identify the best parameters for a machine learning modelUse anomaly detection for filtering user dataApply collaborative filtering to manage dynamic user preferencesUtilize C++ libraries and APIs to manage model structures and parametersImplement C++ code for object detection using a modern neural networkWho this book is forThis book is for beginners looking to explore machine learning algorithms and techniques using C++. This book is also valuable for data analysts, scientists, and developers who want to implement machine learning models in production. Working knowledge of C++ is needed to make the most of this book.

Bridge the gap between business and data science by learning how to interpret machine learning and AI models, manage data teams, and achieve impactful results Key FeaturesMaster the concepts of statistics and ML to interpret models and guide decisionsIdentify valuable AI use cases and manage data science projects from start to finishEmpower top data science teams to solve complex problems and build AI productsPurchase of the print Kindle book includes a free PDF eBookBook DescriptionAs data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI. This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements. By the end of this book, you'll be able to characterize the data within your organization and frame business problems as data science problems.What you will learnDiscover how to interpret common statistical quantities and make data-driven decisionsExplore ML concepts as well as techniques in supervised, unsupervised, and reinforcement learningFind out how to evaluate statistical and machine learning modelsUnderstand the data science lifecycle, from development to monitoring of models in productionKnow when to use ML, statistical modeling, or traditional BI methodsManage data teams and data science projects effectivelyWho this book is forThis book is designed for executives who want to understand and apply data science methods to enhance decision-making. It is also for individuals who work with or manage data scientists and machine learning engineers, such as chief data officers (CDOs), data science managers, and technical project managers.

Unlock the power of APIs with expert guidance and techniques for successfully creating, testing, and maintaining products that align with customers and stand out in the marketKey FeaturesUnderstand the complete API product lifecycle, from planning to implementation, release, and maintenanceDesign and support API products that distinguish themselves on the market by meeting user needsImplement actionable techniques for developing, testing, and securing a successful API productPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe exponential increase in the number of APIs is evidence of their widespread adoption by companies seeking to deliver value to users across diverse industries, making the art of building successful APIs an invaluable skill for anyone involved in product development. With this comprehensive guide, you'll walk through the entire process of planning, designing, implementing, releasing, and maintaining successful API products. You'll start by exploring all aspects of APIs, including their types, technologies, protocols, and lifecycle stages. Next, you'll learn how to define an API strategy and identify business objectives, user personas, and jobs-to-be-done (JTBD). With these skills, you'll delve into designing and validating API capabilities to create a machine-readable API definition. As you advance, the book helps you understand how to choose the right language and framework for securely releasing an API server and offers insights into analyzing API usage metrics, improving performance, and creating compelling documentation that users love. Finally, you'll discover ways to support users, manage versions, and communicate changes or the retirement of an API. By the end of this API development book, you'll have the confidence and skills to create API products that truly stand out in the market.What you will learnMaster each stage of the API lifecycleDiscover technologies and protocols employed in building an API productUnderstand the different API design definition and validation techniquesGenerate an API server from a machine-readable definitionUnderstand how to set up and analyze API monitorsFamiliarize yourself with the different gateways for releasing an APIFind out how to create an API portal that attracts usersGain insights into planning and communicating API retirement to usersWho this book is forBuilding an API Product is a guide for product managers and software developers navigating the world of APIs to build programmable products. You don't have to be an experienced professional to learn from this book as long as you have basic knowledge of internet technologies and how users interact with a product.

This book, part of the best-selling Pocket Primer series, offers a comprehensive introduction to essential Python tools for data scientists. It begins with an overview of Python basics, followed by in-depth coverage of NumPy and Pandas, focusing on their features and applications. The text also addresses the critical tasks of writing regular expressions and performing data cleaning.Further sections delve into data visualization techniques and the use of Sklearn and SciPy, providing practical knowledge and skills for handling complex data analysis tasks. This structured approach ensures that readers gain a complete understanding of the tools and techniques necessary for effective data science.Designed to be accessible yet thorough, this book includes numerous code samples to reinforce learning. Companion files with source code are available for download, making it an invaluable resource for anyone looking to master Python for data science and enhance their data analysis capabilities.

This book is designed as a self-teaching introduction to the fundamental concepts of artificial intelligence (AI). It begins with the history of AI, the Turing test, and early applications, providing a strong foundation. Later chapters cover the basics of searching, game playing, and knowledge representation. The journey continues with detailed explorations of expert systems and machine learning, equipping readers with essential AI techniques.As the course progresses, you will delve into separate programming chapters on Prolog and Python, learning how to implement AI concepts in these languages. These chapters offer practical coding experience, enhancing your understanding of AI programming. The book culminates with a comprehensive chapter on AI machines and robotics, showcasing numerous modern applications and providing a glimpse into the future of AI technology.Understanding these AI concepts is crucial as they form the basis of many modern technologies and applications. This book ensures a smooth transition from a beginner to a proficient AI practitioner, equipped with both theoretical knowledge and practical skills. By the end of the book, you will have a thorough understanding of AI's history, core principles, and practical implementations, ready to apply this knowledge to real-world problems and projects.

This book offers an in-depth exploration of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Beginning with foundational concepts like the attention mechanism and tokenization techniques, it delves into the intricacies of Transformer and BERT architectures. Advanced topics cover the latest developments in the GPT series, including ChatGPT. Key chapters provide insights into the evolution and significance of attention in deep learning, the nuances of Transformer architecture, a detailed exploration of the BERT family, and hands-on guidance on working with GPT-3.The journey continues with a comprehensive overview of ChatGPT, GPT-4, and visualization using generative AI. The book also discusses influential AI organizations such as DeepMind, OpenAI, Cohere, and Hugging Face. Readers will gain a thorough understanding of the current landscape of NLP models, their underlying architectures, and practical applications.Companion files with numerous code samples and figures from the book enhance the learning experience, providing practical tools and resources. This book is an essential guide for those seeking to master the latest advancements in natural language processing and generative AI.

This updated edition introduces computational physics for performing experiments on the computer. It provides a grounding in scientific programming with examples in the context of physics problems. Using C++ as the primary language, it covers interpolation, integration, and solving differential equations, from simple concepts to advanced topics. The book includes a chapter on high-performance computing and parallel programming.Understanding computational physics is crucial for data manipulation and real-world simulations. The book starts with introductory topics and progresses to advanced material, including a C++ library and sample programs. Readers will learn problem-solving methods and constructing models.This journey equips readers with skills for scientific tasks, demonstrating efficient problem-solving and model construction. The practical approach ensures users can apply these concepts in real-world scenarios, making it an essential resource for those interested in computational physics.

Gain a deeper understanding of AWS services by building eight real-world projectsKey FeaturesGain practical skills in architecting, deploying, and managing applications on AWS from seasoned expertsGet hands-on experience by building different architectures in an easy-to-follow mannerUnderstand the purpose of different aspects in AWS, and how to make the most of themPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTired of resumes that get lost in the pile? This book is your roadmap to creating an in-demand AWS portfolio that grabs attention and gets you hired. This comprehensive guide unlocks the vast potential of AWS for developers of all levels. Inside, you'll find invaluable guidance for crafting stunning websites with S3, CloudFront, and Route53. You'll build robust and scalable applications, such as recipe-sharing platforms, using DynamoDB and Elastic Load Balancing. For streamlined efficiency, the book will teach you how to develop serverless architectures with AWS Lambda and Cognito. Gradually, you'll infuse your projects with artificial intelligence by creating a photo analyzer powered by Amazon Rekognition. You'll also automate complex workflows for seamless content translation using Translate, CodePipeline, and CodeBuild. Later, you'll construct intelligent virtual assistants with Amazon Lex and Bedrock to answer web development queries. The book will also show you how to visualize your data with insightful dashboards built using Athena, Glue, and QuickSight. By the end of this book, you'll be ready to take your projects to the next level and succeed in the dynamic world of cloud computing.What you will learnDevelop a professional CV website and gain familiarity with the core aspects of AWSBuild a recipe-sharing application using AWS's serverless toolkitLeverage AWS AI services to create a photo friendliness analyzer for professional profilesImplement a CI/CD pipeline to automate content translation across languagesDevelop a web development Q&A chatbot powered by cutting-edge LLMsBuild a business intelligence application to analyze website clickstream data and understand user behavior with AWSWho this book is forIf you're a student who wants to start your career in cloud computing or a professional with experience in other technical areas like software development who wants to embrace a new professional path or complement your technical skills in cloud computing, this book is for you. A background in computer science or engineering and basic programming skills is recommended. All the projects in the book have theoretical explanations of the services used and do not assume any previous AWS knowledge.

Explore ChatGPT technologies to create state-of-the-art chatbots and voice assistants, and prepare to lead the AI revolutionKey FeaturesLearn how to leverage ChatGPT to create innovative conversational AI solutions for your organizationHarness LangChain and delve into step-by-step LLM application development for conversational AIGain insights into security, privacy, and the future landscape of large language models and conversational AIPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionChatGPT for Conversational AI and Chatbots is a definitive resource for exploring conversational AI, ChatGPT, and large language models. This book introduces the fundamentals of ChatGPT and conversational AI automation. You'll explore the application of ChatGPT in conversation design, the use of ChatGPT as a tool to create conversational experiences, and a range of other practical applications. As you progress, you'll delve into LangChain, a dynamic framework for LLMs, covering topics such as prompt engineering, chatbot memory, using vector stores, and validating responses. Additionally, you'll learn about creating and using LLM-enabling tools, monitoring and fine tuning, LangChain UI tools such as LangFlow, and the LangChain ecosystem. You'll also cover popular use cases, such as using ChatGPT in conjunction with your own data. Later, the book focuses on creating a ChatGPT-powered chatbot that can comprehend and respond to queries directly from your unique data sources. The book then guides you through building chatbot UIs with ChatGPT API and some of the tools and best practices available. By the end of this book, you'll be able to confidently leverage ChatGPT technologies to build conversational AI solutions.What you will learnGain a solid understanding of ChatGPT and its capabilities and limitationsUnderstand how to use ChatGPT for conversation designDiscover how to use advanced LangChain techniques, such as prompting, memory, agents, chains, vector stores, and toolsCreate a ChatGPT chatbot that can answer questions about your own dataDevelop a chatbot powered by ChatGPT APIExplore the future of conversational AI, LLMs, and ChatGPT alternativesWho this book is forThis book is for tech-savvy readers, conversational AI practitioners, engineers, product owners, business analysts, and entrepreneurs wanting to integrate ChatGPT into conversational experiences and explore the possibilities of this game-changing technology. Anyone curious about using internal data with ChatGPT and looking to stay up to date with the developments in large language models will also find this book helpful. Some expertise in coding and standard web design concepts would be useful, along with familiarity with conversational AI terminology, though not essential.

Embark on a transformative journey to becoming a cloud solution architect with a roadmap, expert insights, and practical knowledge to excel in your careerKey FeaturesGain clarity on where to start your journey into cloud architectureDebunk common misconceptions about cloud platforms for informed decision-makingEquip yourself with strategies for career success, skill enhancement, and certificationsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the fast-paced tech world where businesses rely ever more on cloud infrastructure, the role of a cloud solution architect serves as the backbone of operations. The Solution Architects Career Masterplan isn't just informative; it's an actionable roadmap to thriving in this role, providing the knowledge and strategies necessary to build a successful career in cloud computing. You'll dive headfirst into mastering the role's core principles, strategically charting your career trajectory, and expanding your network within the cloud community. As you advance to the practical aspects, you'll explore tailored education options, gain hands-on experience, and prepare to seize strategic opportunities. Finally, you'll prepare for success by arming yourself with interview strategies, staying updated with evolving cloud technologies, and actively contributing to the cloud community. By the end of this book, you'll be on your path to a rewarding career in cloud architecture with this trusted companion.What you will learnGain insights into the core responsibilities of a cloud solution architectDetermine the impact of different certifications on your career pathDevelop a compelling profile and resume strategy to elevate your professional presenceEngage with the community and contribute to open-source projectsEnhance your public speaking skills and receive guidance for advancing your careerMaster problem-solving and decision-making to achieve success as a cloud solution architectWho this book is forIf you're a self-motivated IT professional aiming to pursue a career as a solution architect, this book is for you. While a strong foundation in traditional software architecture is assumed, deep knowledge of cloud concepts and design considerations is not required. This book is also for professionals considering the solution architect role but uncertain where to get started. No experience in the cloud architect role is needed to get started.

Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3 Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn to diagnose the need for regularization in any machine learning modelRegularize different ML models using a variety of techniques and methodsEnhance the functionality of your models using state of the art computer vision and NLP techniquesBook DescriptionRegularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations. After an introduction to regularization and methods to diagnose when to use it, you'll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You'll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you'll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you'll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you'll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E. By the end of this book, you'll be armed with different regularization techniques to apply to your ML and DL models.What you will learnDiagnose overfitting and the need for regularizationRegularize common linear models such as logistic regressionUnderstand regularizing tree-based models such as XGBoosUncover the secrets of structured data to regularize ML modelsExplore general techniques to regularize deep learning modelsDiscover specific regularization techniques for NLP problems using transformersUnderstand the regularization in computer vision models and CNN architecturesApply cutting-edge computer vision regularization with generative modelsWho this book is forThis book is for data scientists, machine learning engineers, and machine learning enthusiasts, looking to get hands-on knowledge to improve the performances of their models. Basic knowledge of Python is a prerequisite.

Take your machine learning expertise to the next level with this essential guide, utilizing libraries like imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy to maximize model performance and tackle imbalanced dataKey FeaturesUnderstand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samplesLearn cutting-edge deep learning techniques to overcome data imbalanceExplore different methods for dealing with skewed data in ML and DL applicationsPurchase of the print or Kindle book includes a free eBook in the PDF formatBook DescriptionAs machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance. Machine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods. As you progress, you'll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that'll demonstrate the practical implementation of each technique. By the end of this book, you'll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models.What you will learnUse imbalanced data in your machine learning models effectivelyExplore the metrics used when classes are imbalancedUnderstand how and when to apply various sampling methods such as over-sampling and under-samplingApply data-based, algorithm-based, and hybrid approaches to deal with class imbalanceCombine and choose from various options for data balancing while avoiding common pitfallsUnderstand the concepts of model calibration and threshold adjustment in the context of dealing with imbalanced datasetsWho this book is forThis book is for machine learning practitioners who want to effectively address the challenges of imbalanced datasets in their projects. Data scientists, machine learning engineers/scientists, research scientists/engineers, and data scientists/engineers will find this book helpful. Though complete beginners are welcome to read this book, some familiarity with core machine learning concepts will help readers maximize the benefits and insights gained from this comprehensive resource.

Unlock the true potential of software testing to achieve seamless software performance with this comprehensive guideKey FeaturesGain a solid understanding of software testing and master its multifaceted strategiesEmpower yourself to effectively overcome software testing challengesDevelop actionable real-world testing skills for succeeding in any rolePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn today's world, software is everywhere—from entertainment apps to mission-critical systems that support our health, finance, and infrastructure. Testing plays a vital role in ensuring these systems work reliably. Whether you're a software developer, hobbyist, or IT professional, this book will guide you in mastering the art of testing. It's about asking the right'What if?'questions, uncovering vulnerabilities, and ensuring software performs as expected throughout its lifecycle. Testing isn't just about automation; it's a human-driven, creative process that requires skill, and a deep understanding of software behavior. With practical examples and expert insights, this book helps you craft your own test strategies and explore novel approaches to problem-solving in the testing world. With its help, you'll hone your testing skills with techniques and methodologies rather than tool-based solutions. Authored by experts Matt Heusser and Michael Larson, the book provides valuable strategies for making testing both effective and engaging. Matt is known for his leadership in project rescue initiatives, while Michael's work in accessibility testing has helped shape industry standards. By the end of this book, you'll be equipped to enhance your testing practices and ensure high-quality software in an ever-evolving tech landscape.What you will learnExplore accessibility, functional testing, performance testing, and more as an integral part of testingFind out how to implement a wide range of testing approachesDevelop the skills needed to create effective testing strategies tailored to your project's needsDiscover how to prioritize and execute the most impactful test ideasGain insight into when and how to apply different testing elementsDefend your chosen testing strategy with a comprehensive understanding of its componentsWho this book is forThis book is for a broad spectrum of professionals engaged in software development, including programmers, testers, and DevOps specialists. Tailored to those who aspire to elevate their testing practices beyond the basics, the book caters to anyone seeking practical insights and strategies to master the nuanced interplay between human intuition and automation. Whether you are a seasoned developer, meticulous tester, or DevOps professional, this comprehensive guide offers a transformative roadmap to become an adept strategist in the dynamic realm of software quality assurance.

Discover what the metaverse can do for your business by exploring AR and VR, core support technologies, and use cases, while developing an understanding of its benefits, dangers, and futureKey FeaturesUnderstand the metaverse and learn how augmented reality and virtual reality are integral to itGet a solid understanding of core metaverse technologiesBecome a metaverse business thought leader by learning from real-world use casesPurchase of the print or Kindle book includes a free PDF eBookBook Description“The metaverse” has become a widely known term within a very short time span. The Immersive Metaverse Playbook for Business Leaders explicitly explains what it really refers to and shows you how to plot your business road map using the metaverse. This book helps you understand the concept of the metaverse, along with the implementation of generative AI in it. You'll not only get to grips with the underlying concepts, but also take a closer look at key technologies that power the metaverse, enabling you to plan your business road map. The chapters include use cases on social interaction, work, entertainment, art, and shopping to help you make better decisions when it comes to metaverse product and service development. You'll also explore the overall societal benefits and dangers related to issues such as privacy encroachment, technology addiction, and sluggishness. The concluding chapters discuss the future of AR and VR roles in the metaverse and the metaverse as a whole to enable you to make long-term business plans. By the end of this book, you'll be able to successfully invest, build, and market metaverse products and services that set you apart as a progressive technology leader.What you will learnGet to grips with the concept of the metaverse, its origin, and its present stateUnderstand how AR and VR strategically fit into the metaverseDelve into core technologies that power the metaverseDig into use cases that enable finer strategic decision-makingUnderstand the benefits and possible dangers of the metaversePlan further ahead by understanding the future of the metaverseWho this book is forIf you are a C-suite technology and business executive, this book is for you. Investors, entrepreneurs, and other tech professionals will also find it beneficial. This book does not require any previous understanding of the metaverse or immersive technologies.

Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world successKey FeaturesLearn how to improve performance of your models and eliminate model biasesStrategically design your machine learning systems to minimize chances of failure in productionDiscover advanced techniques to solve real-world challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDebugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies. By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.What you will learnEnhance data quality and eliminate data flawsEffectively assess and improve the performance of your modelsDevelop and optimize deep learning models with PyTorchMitigate biases to ensure fairnessUnderstand explainability techniques to improve model qualitiesUse test-driven modeling for data processing and modeling improvementExplore techniques to bring reliable models to productionDiscover the benefits of causal and human-in-the-loop modelingWho this book is forThis book is for data scientists, analysts, machine learning engineers, Python developers, and students looking to build reliable, high-performance, and explainable machine learning models for production across diverse industrial applications. Fundamental Python skills are all you need to dive into the concepts and practical examples covered. Whether you're new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills.

Master the art of mathematical modeling through practical examples, use cases, and machine learning techniquesKey FeaturesGain a profound understanding of various mathematical models that can be integrated with machine learningLearn how to implement optimization algorithms to tune machine learning modelsBuild optimal solutions for practical use casesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMathematical modeling is the art of transforming a business problem into a well-defined mathematical formulation. Its emphasis on interpretability is particularly crucial when deploying a model to support high-stake decisions in sensitive sectors like pharmaceuticals and healthcare. Through this book, you'll gain a firm grasp of the foundational mathematics underpinning various machine learning algorithms. Equipped with this knowledge, you can modify algorithms to suit your business problem. Starting with the basic theory and concepts of mathematical modeling, you'll explore an array of mathematical tools that will empower you to extract insights and understand the data better, which in turn will aid in making optimal, data-driven decisions. The book allows you to explore mathematical optimization and its wide range of applications, and concludes by highlighting the synergetic value derived from blending mathematical models with machine learning. Ultimately, you'll be able to apply everything you've learned to choose the most fitting methodologies for the business problems you encounter.What you will learnUnderstand core concepts of mathematical models and their relevance in solving problemsExplore various approaches to modeling and learning using PythonWork with tested mathematical tools to gather meaningful insightsBlend mathematical modeling with machine learning to find optimal solutions to business problemsOptimize ML models built with business data, apply them to understand their impact on the business, and address critical questionsApply mathematical optimization for data-scarce problems where the objective and constraints are knownWho this book is forIf you are a budding data scientist seeking to augment your journey with mathematics, this book is for you. Researchers and R&D scientists will also be able to harness the concepts covered to their full potential. To make the best use of this book, a background in linear algebra, differential equations, basics of statistics, data types, data structures, and numerical algorithms will be useful.

Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insights Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesGain expertise in identifying and modeling patterns that generate successExplore the concepts with Python using important libraries such as stats modelsLearn how to build models on real-world data sets and find solutions to practical challengesBook DescriptionThe ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you'll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you'll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you'll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.What you will learnExplore the use of statistics to make decisions under uncertaintyAnswer questions about data using hypothesis testsUnderstand the difference between regression and classification modelsBuild models with stats models in PythonAnalyze time series data and provide forecastsDiscover Survival Analysis and the problems it can solveWho this book is forIf you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you'll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.

Conquer data hurdles, supercharge your ML journey, and become a leader in your field with synthetic data generation techniques, best practices, and case studiesKey FeaturesAvoid common data issues by identifying and solving them using synthetic data-based solutionsMaster synthetic data generation approaches to prepare for the future of machine learningEnhance performance, reduce budget, and stand out from competitors using synthetic dataPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe machine learning (ML) revolution has made our world unimaginable without its products and services. However, training ML models requires vast datasets, which entails a process plagued by high costs, errors, and privacy concerns associated with collecting and annotating real data. Synthetic data emerges as a promising solution to all these challenges. This book is designed to bridge theory and practice of using synthetic data, offering invaluable support for your ML journey. Synthetic Data for Machine Learning empowers you to tackle real data issues, enhance your ML models'performance, and gain a deep understanding of synthetic data generation. You'll explore the strengths and weaknesses of various approaches, gaining practical knowledge with hands-on examples of modern methods, including Generative Adversarial Networks (GANs) and diffusion models. Additionally, you'll uncover the secrets and best practices to harness the full potential of synthetic data. By the end of this book, you'll have mastered synthetic data and positioned yourself as a market leader, ready for more advanced, cost-effective, and higher-quality data sources, setting you ahead of your peers in the next generation of ML.What you will learnUnderstand real data problems, limitations, drawbacks, and pitfallsHarness the potential of synthetic data for data-hungry ML modelsDiscover state-of-the-art synthetic data generation approaches and solutionsUncover synthetic data potential by working on diverse case studiesUnderstand synthetic data challenges and emerging research topicsApply synthetic data to your ML projects successfullyWho this book is forIf you are a machine learning (ML) practitioner or researcher who wants to overcome data problems, this book is for you. Basic knowledge of ML and Python programming is required. The book is one of the pioneer works on the subject, providing leading-edge support for ML engineers, researchers, companies, and decision makers.

Elevate your natural language processing skills with Gensim and become proficient in handling a wide range of NLP tasks and projectsKey FeaturesAdvance your NLP skills with this comprehensive guide covering detailed explanations and code practicesBuild real-world topical modeling pipelines and fine-tune hyperparameters to deliver optimal resultsAdhere to the real-world industrial applications of topic modeling in medical, legal, and other fieldsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNavigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios. You'll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy. Next, you'll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you'll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications. By the end of this book, you'll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes.What you will learnConvert text into numerical values such as bag-of-word, TF-IDF, and word embeddingUse various NLP techniques with Gensim, including Word2Vec, Doc2Vec, LSA, FastText, LDA, and Ensemble LDABuild topical modeling pipelines and visualize the results of topic modelsImplement text summarization for legal, clinical, or other documentsApply core NLP techniques in healthcare, finance, and e-commerceCreate efficient chatbots by harnessing Gensim's NLP capabilitiesWho this book is forThis book is for data scientists and professionals who want to become proficient in topic modeling with Gensim. NLP practitioners can use this book as a code reference, while students or those considering a career transition will find this a valuable resource for advancing in the field of NLP. This book contains real-world applications for biomedical, healthcare, legal, and operations, making it a helpful guide for project managers designing their own topic modeling applications.

Take your software to the next level and solve real-world data science problems by building production-ready machine learning solutions using LightGBM and PythonKey FeaturesGet started with LightGBM, a powerful gradient-boosting library for building ML solutionsApply data science processes to real-world problems through case studiesElevate your software by building machine learning solutions on scalable platformsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMachine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems that are ready for release. This book will get you acquainted with the high-performance gradient-boosting LightGBM framework and show you how it can be used to solve various machine-learning problems to produce highly accurate, robust, and predictive solutions. Starting with simple machine learning models in scikit-learn, you'll explore the intricacies of gradient boosting machines and LightGBM. You'll be guided through various case studies to better understand the data science processes and learn how to practically apply your skills to real-world problems. As you progress, you'll elevate your software engineering skills by learning how to build and integrate scalable machine-learning pipelines to process data, train models, and deploy them to serve secure APIs using Python tools such as FastAPI. By the end of this book, you'll be well equipped to use various -of-the-art tools that will help you build production-ready systems, including FLAML for AutoML, PostgresML for operating ML pipelines using Postgres, high-performance distributed training and serving via Dask, and creating and running models in the Cloud with AWS Sagemaker.What you will learnGet an overview of ML and working with data and models in Python using scikit-learnExplore decision trees, ensemble learning, gradient boosting, DART, and GOSSMaster LightGBM and apply it to classification and regression problemsTune and train your models using AutoML with FLAML and OptunaBuild ML pipelines in Python to train and deploy models with secure and performant APIsScale your solutions to production readiness with AWS Sagemaker, PostgresML, and DaskWho this book is forThis book is for software engineers aspiring to be better machine learning engineers and data scientists unfamiliar with LightGBM, looking to gain in-depth knowledge of its libraries. Basic to intermediate Python programming knowledge is required to get started with the book. The book is also an excellent source for ML veterans, with a strong focus on ML engineering with up-to-date and thorough coverage of platforms such as AWS Sagemaker, PostgresML, and Dask.

Kickstart your emotion analysis journey with this step-by-step guide to data science successKey FeaturesDiscover the inner workings of the end-to-end emotional analysis workflowExplore the use of various ML models to derive meaningful insights from dataHone your craft by building and tweaking complex emotion analysis models with practical projectsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionArtificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially. With this book, you'll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you'll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions. The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you're set up for success, you'll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you'll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion. By the end of this book, you'll be well-equipped to use emotion mining and analysis to drive business decisions.What you will learnDistinguish between sentiment analysis and emotion analysisMaster data preprocessing and ensure high-quality inputExpand the use of data sources through data transformationDesign models that employ cutting-edge deep learning techniquesDiscover how to tune your models'hyperparametersExplore the use of naive Bayes, SVMs, DNNs, and transformers for advanced use casesPractice your newly acquired skills by working on real-world scenariosWho this book is forThis book is for data scientists and Python developers looking to gain insights into the customer feedback for their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming is a must.

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 eBookKey FeaturesLearn ethical AI principles, frameworks, and governanceUnderstand the concepts of fairness assessment and bias mitigationIntroduce explainable AI and transparency in your machine learning modelsBook 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 learnUnderstand explainable AI fundamentals, underlying methods, and techniquesExplore model governance, including building explainable, auditable, and interpretable machine learning modelsUse partial dependence plot, global feature summary, individual condition expectation, and feature interactionBuild explainable models with global and local feature summary, and influence functions in practiceDesign and build explainable machine learning pipelines with transparencyDiscover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platformsWho this book is forThis 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.

Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainability Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn risk assessment for machine learning frameworks in a global landscapeDiscover patterns for next-generation AI ecosystems for successful product designMake explainable predictions for privacy and fairness-enabled ML trainingBook DescriptionAI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it's necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you'll be able to make existing black box models transparent. You'll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You'll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You'll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics. By the end of this book, you'll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You'll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.What you will learnUnderstand the threats and risks involved in ML modelsDiscover varying levels of risk mitigation strategies and risk tiering toolsApply traditional and deep learning optimization techniques efficientlyBuild auditable and interpretable ML models and feature storesUnderstand the concept of uncertainty and explore model explainability toolsDevelop models for different clouds including AWS, Azure, and GCPExplore ML orchestration tools such as Kubeflow and Vertex AIIncorporate privacy and fairness in ML models from design to deploymentWho this book is forThis book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem.

Build real-world artificial intelligence apps on AWS to overcome challenges faced by healthcare providers and payers, as well as pharmaceutical, life sciences research, and commercial organizationsKey FeaturesLearn about healthcare industry challenges and how machine learning can solve themExplore AWS machine learning services and their applications in healthcare and life sciencesDiscover practical coding instructions to implement machine learning for healthcare and life sciencesBook DescriptionWhile machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics. This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You'll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications. By the end of this book, you'll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence.What you will learnExplore the healthcare and life sciences industryFind out about the key applications of AI in different industry segmentsApply AI to medical images, clinical notes, and patient dataDiscover security, privacy, fairness, and explainability best practicesExplore the AWS ML stack and key AI services for the industryDevelop practical ML skills using code and AWS servicesDiscover all about industry regulatory requirementsWho this book is forThis book is specifically tailored toward technology decision-makers, data scientists, machine learning engineers, and anyone who works in the data engineering role in healthcare and life sciences organizations. Whether you want to apply machine learning to overcome common challenges in the healthcare and life science industry or are looking to understand the broader industry AI trends and landscape, this book is for you. This book is filled with hands-on examples for you to try as you learn about new AWS AI concepts.

Accelerate the adoption of machine learning by automating away the complex parts of the ML pipeline using H2O.aiKey FeaturesLearn how to train the best models with a single click using H2O AutoMLGet a simple explanation of model performance using H2O ExplainabilityEasily deploy your trained models to production using H2O MOJO and POJOBook DescriptionWith the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in – it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities. You'll begin by understanding how H2O's AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you'll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you'll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you'll take a hands-on approach to implementation using H2O that'll enable you to set up your ML systems in no time. By the end of this H2O book, you'll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science.What you will learnGet to grips with H2O AutoML and learn how to use itExplore the H2O Flow Web UIUnderstand how H2O AutoML trains the best models and automates hyperparameter optimizationFind out how H2O Explainability helps understand model performanceExplore H2O integration with scikit-learn, the Spring Framework, and Apache StormDiscover how to use H2O with Spark using H2O Sparkling WaterWho this book is forThis book is for engineers and data scientists who want to quickly adopt machine learning into their products without worrying about the internal intricacies of training ML models. If you're someone who wants to incorporate machine learning into your software system but don't know where to start or don't have much expertise in the domain of ML, then you'll find this book useful. Basic knowledge of statistics and programming is beneficial. Some understanding of ML and Python will be helpful.

Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning servicesKey FeaturesImplement end-to-end machine learning pipelines on AzureTrain deep learning models using Azure compute infrastructureDeploy machine learning models using MLOpsBook DescriptionAzure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you'll be able to combine all the steps you've learned by building an MLOps pipeline.What you will learnUnderstand the end-to-end ML pipelineGet to grips with the Azure Machine Learning workspaceIngest, analyze, and preprocess datasets for ML using the Azure cloudTrain traditional and modern ML techniques efficiently using Azure MLDeploy ML models for batch and real-time scoringUnderstand model interoperability with ONNXDeploy ML models to FPGAs and Azure IoT EdgeBuild an automated MLOps pipeline using Azure DevOpsWho this book is forThis book is for machine learning engineers, data scientists, and machine learning developers who want to use the Microsoft Azure cloud to manage their datasets and machine learning experiments and build an enterprise-grade ML architecture using MLOps. This book will also help anyone interested in machine learning to explore important steps of the ML process and use Azure Machine Learning to support them, along with building powerful ML cloud applications. A basic understanding of Python and knowledge of machine learning are recommended.

Learn cyber threat intelligence fundamentals to implement and operationalize an organizational intelligence programKey FeaturesDevelop and implement a threat intelligence program from scratchDiscover techniques to perform cyber threat intelligence, collection, and analysis using open-source toolsLeverage a combination of theory and practice that will help you prepare a solid foundation for operationalizing threat intelligence programsBook DescriptionWe're living in an era where cyber threat intelligence is becoming more important. Cyber threat intelligence routinely informs tactical and strategic decision-making throughout organizational operations. However, finding the right resources on the fundamentals of operationalizing a threat intelligence function can be challenging, and that's where this book helps. In Operationalizing Threat Intelligence, you'll explore cyber threat intelligence in five fundamental areas: defining threat intelligence, developing threat intelligence, collecting threat intelligence, enrichment and analysis, and finally production of threat intelligence. You'll start by finding out what threat intelligence is and where it can be applied. Next, you'll discover techniques for performing cyber threat intelligence collection and analysis using open source tools. The book also examines commonly used frameworks and policies as well as fundamental operational security concepts. Later, you'll focus on enriching and analyzing threat intelligence through pivoting and threat hunting. Finally, you'll examine detailed mechanisms for the production of intelligence. By the end of this book, you'll be equipped with the right tools and understand what it takes to operationalize your own threat intelligence function, from collection to production.What you will learnDiscover types of threat actors and their common tactics and techniquesUnderstand the core tenets of cyber threat intelligenceDiscover cyber threat intelligence policies, procedures, and frameworksExplore the fundamentals relating to collecting cyber threat intelligenceUnderstand fundamentals about threat intelligence enrichment and analysisUnderstand what threat hunting and pivoting are, along with examplesFocus on putting threat intelligence into productionExplore techniques for performing threat analysis, pivoting, and huntingWho this book is forThis book is for cybersecurity professionals, security analysts, security enthusiasts, and anyone who is just getting started and looking to explore threat intelligence in more detail. Those working in different security roles will also be able to explore threat intelligence with the help of this security book.

Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMakerKey FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook DescriptionMachine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.What you will learnExplore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is forThe book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.

Build real-world artificial intelligence applications across industries with the help of intelligent document processingKey FeaturesTackle common document processing problems to extract value from any type of documentUnlock deeper levels of insights on IDP in a more structured and accelerated way using AWS AI/MLApply your knowledge to solve real document analysis problems in various industry applicationsBook DescriptionWith the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You'll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you'll have mastered the fundamentals of document processing with machine learning through practical implementation.What you will learnUnderstand the requirements and challenges in deriving insights from a documentExplore common stages in the intelligent document processing pipelineDiscover how AWS AI/ML can successfully automate IDP pipelinesFind out how to write clean and elegant Python code by leveraging AIGet to grips with the concepts and functionalities of AWS AI servicesExplore IDP across industries such as insurance, healthcare, finance, and the public sectorDetermine how to apply business rules in IDPBuild, train, and deploy models with serverless architecture for IDPWho this book is forThis book is for technical professionals and thought leaders who want to understand and solve business problems by leveraging insights from their documents. If you want to learn about machine learning and artificial intelligence, and work with real-world use cases such as document processing with technology, this book is for you. To make the most of this book, you should have basic knowledge of AI/ML and python programming concepts. This book is also especially useful for developers looking to explore AI/ML with industry use cases.

Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model's finest detailsKey FeaturesGain a deep understanding of how hyperparameter tuning worksExplore exhaustive search, heuristic search, and Bayesian and multi-fidelity optimization methodsLearn which method should be used to solve a specific situation or problemBook DescriptionHyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements. You'll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter. By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.What you will learnDiscover hyperparameter space and types of hyperparameter distributionsExplore manual, grid, and random search, and the pros and cons of eachUnderstand powerful underdog methods along with best practicesExplore the hyperparameters of popular algorithmsDiscover how to tune hyperparameters in different frameworks and librariesDeep dive into top frameworks such as Scikit, Hyperopt, Optuna, NNI, and DEAPGet to grips with best practices that you can apply to your machine learning models right awayWho this book is forThis book is for data scientists and ML engineers who are working with Python and want to further boost their ML model's performance by using the appropriate hyperparameter tuning method. Although a basic understanding of machine learning and how to code in Python is needed, no prior knowledge of hyperparameter tuning in Python is required.

Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey FeaturesCompute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to use Python libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook DescriptionThe updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you've developed a solid base in these topics, you'll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.What you will learnBecome familiar with basic Python packages, tools, and libraries for solving mathematical problemsExplore real-world applications of mathematics to reduce a problem in optimizationUnderstand the core concepts of applied mathematics and their application in computer scienceFind out how to choose the most suitable package, tool, or technique to solve a problemImplement basic mathematical plotting, change plot styles, and add labels to plots using MatplotlibGet to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methodsWho this book is forWhether you are a professional programmer or a student looking to solve mathematical problems computationally using Python, this is the book for you. Advanced mathematics proficiency is not a prerequisite, but basic knowledge of mathematics will help you to get the most out of this Python math book. Familiarity with the concepts of data structures in Python is assumed.

Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next levelKey FeaturesDesign distributed systems that can be applied to real-world federated learning applications at scaleDiscover multiple aggregation schemes applicable to various ML settings and applicationsDevelop a federated learning system that can be tested in distributed machine learning settingsBook DescriptionFederated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you get to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples. FL is more than just aggregating collected ML models and bringing them back to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you'll be able to create a sustainable and resilient FL system that can constantly function in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of research-related literature. By the end of this book, you'll have an in-depth understanding of the FL system design and implementation basics and be able to create an FL system and applications that can be deployed to various local and cloud environments.What you will learnDiscover the challenges related to centralized big data ML that we currently face along with their solutionsUnderstand the theoretical and conceptual basics of FLAcquire design and architecting skills to build an FL systemExplore the actual implementation of FL servers and clientsFind out how to integrate FL into your own ML applicationUnderstand various aggregation mechanisms for diverse ML scenariosDiscover popular use cases and future trends in FLWho this book is forThis book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You'll need basic knowledge of Python programming and machine learning concepts to get started with this book.

Build a Kubernetes-based self-serving, agile data science and machine learning ecosystem for your organization using reliable and secure open source technologiesKey FeaturesBuild a complete machine learning platform on KubernetesImprove the agility and velocity of your team by adopting the self-service capabilities of the platformReduce time-to-market by automating data pipelines and model training and deploymentBook DescriptionMLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization. You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow. By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built.What you will learnUnderstand the different stages of a machine learning projectUse open source software to build a machine learning platform on KubernetesImplement a complete ML project using the machine learning platform presented in this bookImprove on your organization s collaborative journey toward machine learningDiscover how to use the platform as a data engineer, ML engineer, or data scientistFind out how to apply machine learning to solve real business problemsWho this book is forThis book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way.