Numerical Methods Using Java
Implement numerical algorithms in Java using the NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes. Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. ...
Implement numerical algorithms in Java using the NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes. Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. ...
Data Analysis with Python and PySpark
Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you've learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark's core engine with a Python-based API. It helps simplify Spark's steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You'll learn how to scale your processing capabilities across multiple machines while ingesting data from any source - whether that's Had ...
Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you've learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark's core engine with a Python-based API. It helps simplify Spark's steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You'll learn how to scale your processing capabilities across multiple machines while ingesting data from any source - whether that's Had ...
Practical AI for Healthcare Professionals
Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You'll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You'll then learn the basics of computer science algorithms, neural networks, and when each should be applied. Then you'll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The Tensorflow/Keras library along with Numpy and Scikit-Learn are covered as well. Once you've mastered those basic computer science and programming concepts, you ...
Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You'll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You'll then learn the basics of computer science algorithms, neural networks, and when each should be applied. Then you'll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The Tensorflow/Keras library along with Numpy and Scikit-Learn are covered as well. Once you've mastered those basic computer science and programming concepts, you ...
Modern Parallel Programming with C++ and Assembly Language
Learn the fundamentals of x86 Single instruction multiple data (SIMD) programming using C++ intrinsic functions and x86-64 assembly language. This book emphasizes x86 SIMD programming topics and technologies that are relevant to modern software development in applications which can exploit data level parallelism, important for the processing of big data, large batches of data and related important in data science and much more. Modern Parallel Programming with C++ and Assembly Language is an instructional text that explains x86 SIMD programming using both C++ and assembly language. The book's content and organization are designed to help you quickly understand and exploit the SIMD capabilities of x86 processors. It also contains an abundance of source code that is structured to accelerate learning and comprehension of essential SIMD programming concepts and algorithms. After reading this book, you will be able to code performance-optimized AVX, AVX2, and AVX-512 algorithms using ...
Learn the fundamentals of x86 Single instruction multiple data (SIMD) programming using C++ intrinsic functions and x86-64 assembly language. This book emphasizes x86 SIMD programming topics and technologies that are relevant to modern software development in applications which can exploit data level parallelism, important for the processing of big data, large batches of data and related important in data science and much more. Modern Parallel Programming with C++ and Assembly Language is an instructional text that explains x86 SIMD programming using both C++ and assembly language. The book's content and organization are designed to help you quickly understand and exploit the SIMD capabilities of x86 processors. It also contains an abundance of source code that is structured to accelerate learning and comprehension of essential SIMD programming concepts and algorithms. After reading this book, you will be able to code performance-optimized AVX, AVX2, and AVX-512 algorithms using ...
Build a Weather Station with Elixir and Nerves
The Elixir programming language has become a go-to tool for creating reliable, fault-tolerant, and robust server-side applications. Thanks to Nerves, those same exact benefits can be realized in embedded applications. This book will teach you how to structure, build, and deploy production grade Nerves applications to network-enabled devices. The weather station sensor hub project that you will be embarking upon will show you how to create a full stack IoT solution in record time. You will build everything from the embedded Nerves device to the Phoenix backend and even the Grafana time-series data visualizations. Elixir as a programming language has found its way into many different software domains, largely in part to the rock-solid foundation of the Erlang virtual machine. Thanks to the Nerves framework, Elixir has also found success in the world of embedded systems and IoT. Having access to all of the Elixir and OTP constructs such as concurrency, supervision, and immutability mak ...
The Elixir programming language has become a go-to tool for creating reliable, fault-tolerant, and robust server-side applications. Thanks to Nerves, those same exact benefits can be realized in embedded applications. This book will teach you how to structure, build, and deploy production grade Nerves applications to network-enabled devices. The weather station sensor hub project that you will be embarking upon will show you how to create a full stack IoT solution in record time. You will build everything from the embedded Nerves device to the Phoenix backend and even the Grafana time-series data visualizations. Elixir as a programming language has found its way into many different software domains, largely in part to the rock-solid foundation of the Erlang virtual machine. Thanks to the Nerves framework, Elixir has also found success in the world of embedded systems and IoT. Having access to all of the Elixir and OTP constructs such as concurrency, supervision, and immutability mak ...
Microsoft Excel Data Analysis and Business Modeling, 7th Edition
Master business modeling and analysis techniques with Microsoft Excel and transform data into bottom-line results. Award-winning educator Wayne Winstons hands-on, scenario-focused guide helps you use todays Excel to ask the right questions and get accurate, actionable answers. More extensively updated than any previous edition, new coverage ranges from one-click data analysis to STOCKHISTORY, dynamic arrays to Power Query, and includes six new chapters. Practice with over 900 problems, many based on real challenges faced by working analysts. Solve real problems with Microsoft Exceland build your competitive advantage. Quickly transition from Excel basics to sophisticated analytics; Use recent Power Query enhancements to connect, combine, and transform data sources more effectively; Use the LAMBDA and LAMBDA helper functions to create Custom Functions without VBA; Use New Data Types to import data including stock prices, weather, information on geographic areas, universities, movi ...
Master business modeling and analysis techniques with Microsoft Excel and transform data into bottom-line results. Award-winning educator Wayne Winstons hands-on, scenario-focused guide helps you use todays Excel to ask the right questions and get accurate, actionable answers. More extensively updated than any previous edition, new coverage ranges from one-click data analysis to STOCKHISTORY, dynamic arrays to Power Query, and includes six new chapters. Practice with over 900 problems, many based on real challenges faced by working analysts. Solve real problems with Microsoft Exceland build your competitive advantage. Quickly transition from Excel basics to sophisticated analytics; Use recent Power Query enhancements to connect, combine, and transform data sources more effectively; Use the LAMBDA and LAMBDA helper functions to create Custom Functions without VBA; Use New Data Types to import data including stock prices, weather, information on geographic areas, universities, movi ...
Microsoft Azure Data Solutions
Cloud technologies are advancing at an accelerating pace, supplanting traditional relational and data warehouse storage solutions with novel, high-value alternatives. Now, three pioneering Azure Data consultants offer an expert introduction to the relational, non-relational, and data warehouse solutions offered by the Azure platform. Drawing on their extensive experience helping organizations get more value from the Microsoft Data Platform, the authors guide you through decision-making, implementation, operations, security, and more. Throughout, step-by-step tutorials and hands-on exercises prepare you to succeed, even if you have no cloud data experience. Three leading experts in Microsoft Azure Data Solutions show how to: Master essential concepts of data storage and processing in cloud environments; Handle the changing responsibilities of data engineers moving to the cloud; Get started with Azure data storage accounts and other data facilities; Walk through implementing relationa ...
Cloud technologies are advancing at an accelerating pace, supplanting traditional relational and data warehouse storage solutions with novel, high-value alternatives. Now, three pioneering Azure Data consultants offer an expert introduction to the relational, non-relational, and data warehouse solutions offered by the Azure platform. Drawing on their extensive experience helping organizations get more value from the Microsoft Data Platform, the authors guide you through decision-making, implementation, operations, security, and more. Throughout, step-by-step tutorials and hands-on exercises prepare you to succeed, even if you have no cloud data experience. Three leading experts in Microsoft Azure Data Solutions show how to: Master essential concepts of data storage and processing in cloud environments; Handle the changing responsibilities of data engineers moving to the cloud; Get started with Azure data storage accounts and other data facilities; Walk through implementing relationa ...
Programming ML.NET
ML.NET brings the power of machine learning to all .NET developers and Programming ML.NET helps you apply it in real production solutions. Modeled on Dino Espositos best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsofts team used to build ML.NET itself. After a foundational overview of ML.NETs libraries, the authors illuminate mini-frameworks (ML Tasks) for regression, classification, ranking, anomaly detection, and more. For each ML Task, they offer insights for overcoming common real-world challenges. Finally, going far beyond shallow learning, the authors thoroughly introduce ML.NET neural networking. They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network showing how to leverage popular Python tools within .NET. 14-time Microsoft MVP Dino Esposito and son Francesco Esposito show how to: Build smarter machine learning solutions that are closer to your users nee ...
ML.NET brings the power of machine learning to all .NET developers and Programming ML.NET helps you apply it in real production solutions. Modeled on Dino Espositos best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsofts team used to build ML.NET itself. After a foundational overview of ML.NETs libraries, the authors illuminate mini-frameworks (ML Tasks) for regression, classification, ranking, anomaly detection, and more. For each ML Task, they offer insights for overcoming common real-world challenges. Finally, going far beyond shallow learning, the authors thoroughly introduce ML.NET neural networking. They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network showing how to leverage popular Python tools within .NET. 14-time Microsoft MVP Dino Esposito and son Francesco Esposito show how to: Build smarter machine learning solutions that are closer to your users nee ...
Spring Data Programming Cookbook
Spring Data's mission is to provide a familiar and consistent, Spring-based programming model for data access while still retaining the special traits of the underlying data store. It makes it easy to use data access technologies, relational and non-relational databases, map-reduce frameworks, and cloud-based data services. This is an umbrella project which contains many subprojects that are specific to a given database. The projects are developed by working together with many of the companies and developers that are behind these exciting technologies. In this open book, we provide a compilation of Spring Data examples that will help you kick-start your own projects. We cover a wide range of topics, from setting up the environment and creating a basic project, to handling the various modules (e.g. JPA, MongoDB, Redis etc.). With our straightforward tutorials, you will be able to get your own projects up and running in minimum time. ...
Spring Data's mission is to provide a familiar and consistent, Spring-based programming model for data access while still retaining the special traits of the underlying data store. It makes it easy to use data access technologies, relational and non-relational databases, map-reduce frameworks, and cloud-based data services. This is an umbrella project which contains many subprojects that are specific to a given database. The projects are developed by working together with many of the companies and developers that are behind these exciting technologies. In this open book, we provide a compilation of Spring Data examples that will help you kick-start your own projects. We cover a wide range of topics, from setting up the environment and creating a basic project, to handling the various modules (e.g. JPA, MongoDB, Redis etc.). With our straightforward tutorials, you will be able to get your own projects up and running in minimum time. ...
Interpretable AI
Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model. It's often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside "black box" models, designing accountable algorithms, and understanding the factors that cause skewed results. Interpretable AI teaches you to identify the patterns your model has learned and why it produces its results. As you read, you'll ...
Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model. It's often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside "black box" models, designing accountable algorithms, and understanding the factors that cause skewed results. Interpretable AI teaches you to identify the patterns your model has learned and why it produces its results. As you read, you'll ...