Machine Learning for Streaming Data with Python
Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream ...
Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream ...
Practical C++ Design, 2nd Edition
Go from competent C++ developer to skilled designer or architect using this book as your personal C++ design master class. Updated for the C++20 standard, this title will guide you through the design and implementation of an engaging case study that forms the backdrop for learning the art of applying design patterns and modern C++ techniques to create a high quality, robust application. Starting with a quick exploration of the requirements for building the application, you'll delve into selecting an appropriate architecture, eventually designing and implementing all of the necessary modules to meet the project's requirements. By the conclusion of Practical C++ 20 Design, you'll have constructed a fully functioning calculator capable of building and executing on any platform that supports both Qt and C++20. Access to the complete source code will help speed your learning. Utilize the Model-View-Controller pattern as the basis for the architecture of the calculator; the observer ...
Go from competent C++ developer to skilled designer or architect using this book as your personal C++ design master class. Updated for the C++20 standard, this title will guide you through the design and implementation of an engaging case study that forms the backdrop for learning the art of applying design patterns and modern C++ techniques to create a high quality, robust application. Starting with a quick exploration of the requirements for building the application, you'll delve into selecting an appropriate architecture, eventually designing and implementing all of the necessary modules to meet the project's requirements. By the conclusion of Practical C++ 20 Design, you'll have constructed a fully functioning calculator capable of building and executing on any platform that supports both Qt and C++20. Access to the complete source code will help speed your learning. Utilize the Model-View-Controller pattern as the basis for the architecture of the calculator; the observer ...
Effective Data Science Infrastructure
Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you'll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You'll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from ...
Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you'll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You'll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from ...
Modern Java EE Design Patterns
With the ascent of DevOps, microservices, containers, and cloud-based development platforms, the gap between state-of-the-art solutions and the technology that enterprises typically support has greatly increased. But as Markus Eisele explains in this O'Reilly report, some enterprises are now looking to bridge that gap by building microservice-based architectures on top of Java EE. Can it be done? Is it even a good idea? Eisele thoroughly explores the possibility and provides savvy advice for enterprises that want to move ahead. The issue is complex: Java EE wasn't built with the distributed application approach in mind, but rather as one monolithic server runtime or cluster hosting many different applications. If you're part of an enterprise development team investigating the use of microservices with Java EE, this book will help you: - Understand the challenges of starting a greenfield development vs tearing apart an existing brownfield application into services; - Examine your b ...
With the ascent of DevOps, microservices, containers, and cloud-based development platforms, the gap between state-of-the-art solutions and the technology that enterprises typically support has greatly increased. But as Markus Eisele explains in this O'Reilly report, some enterprises are now looking to bridge that gap by building microservice-based architectures on top of Java EE. Can it be done? Is it even a good idea? Eisele thoroughly explores the possibility and provides savvy advice for enterprises that want to move ahead. The issue is complex: Java EE wasn't built with the distributed application approach in mind, but rather as one monolithic server runtime or cluster hosting many different applications. If you're part of an enterprise development team investigating the use of microservices with Java EE, this book will help you: - Understand the challenges of starting a greenfield development vs tearing apart an existing brownfield application into services; - Examine your b ...
Python for Data Science
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You'll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Python's rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket ...
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You'll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Python's rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket ...
3D Deep Learning with Python
With this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time. Complete with step-by-step explanations of essential concepts and practical examples, this book lets you explore and gain a thorough understanding of state-of-the-art 3D deep learning. You'll see how to use PyTorch3D for basic 3D mesh and point cloud data processing, including loading and saving ply and obj files, projecting 3D points into camera coordination using perspective camera models or orthographic camera models, rendering point clouds and meshes to images, and much more. As you implement some of the latest 3D deep learning algorithms, such as differential rendering, Nerf, synsin, and mesh RCNN, you'll realize how coding for these deep learning models becomes easier using the PyTorch3D library. By the end of this deep learning book, you'll be ready to implement your own 3D deep learning models confidently. ...
With this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time. Complete with step-by-step explanations of essential concepts and practical examples, this book lets you explore and gain a thorough understanding of state-of-the-art 3D deep learning. You'll see how to use PyTorch3D for basic 3D mesh and point cloud data processing, including loading and saving ply and obj files, projecting 3D points into camera coordination using perspective camera models or orthographic camera models, rendering point clouds and meshes to images, and much more. As you implement some of the latest 3D deep learning algorithms, such as differential rendering, Nerf, synsin, and mesh RCNN, you'll realize how coding for these deep learning models becomes easier using the PyTorch3D library. By the end of this deep learning book, you'll be ready to implement your own 3D deep learning models confidently. ...
Learning Tableau 2022, 5th Edition
Learning Tableau 2022 helps you get started with Tableau and data visualization, but it does more than just cover the basic principles. It helps you understand how to analyze and communicate data visually, and articulate data stories using advanced features. This new edition is updated with Tableau's latest features, such as dashboard extensions, Explain Data, and integration with CRM Analytics (Einstein Analytics), which will help you harness the full potential of artificial intelligence (AI) and predictive modeling in Tableau. After an exploration of the core principles, this book will teach you how to use table and level of detail calculations to extend and alter default visualizations, build interactive dashboards, and master the art of telling stories with data. You'll learn about visual statistical analytics and create different types of static and animated visualizations and dashboards for rich user experiences. We then move on to interlinking different data sources wit ...
Learning Tableau 2022 helps you get started with Tableau and data visualization, but it does more than just cover the basic principles. It helps you understand how to analyze and communicate data visually, and articulate data stories using advanced features. This new edition is updated with Tableau's latest features, such as dashboard extensions, Explain Data, and integration with CRM Analytics (Einstein Analytics), which will help you harness the full potential of artificial intelligence (AI) and predictive modeling in Tableau. After an exploration of the core principles, this book will teach you how to use table and level of detail calculations to extend and alter default visualizations, build interactive dashboards, and master the art of telling stories with data. You'll learn about visual statistical analytics and create different types of static and animated visualizations and dashboards for rich user experiences. We then move on to interlinking different data sources wit ...
Deep Learning for Natural Language Processing
Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You'll learn to use ...
Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You'll learn to use ...
Generative Deep Learning, 2nd Edition
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos; Train GANs to generate images based on your own dataset; Build diffusion models to produce new varieties of flowers; Train your own GPT for text generation; Learn how large language models like ChatGPT are trained; Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN; Compose polyphonic music using Transformers and Mu ...
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos; Train GANs to generate images based on your own dataset; Build diffusion models to produce new varieties of flowers; Train your own GPT for text generation; Learn how large language models like ChatGPT are trained; Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN; Compose polyphonic music using Transformers and Mu ...
Ernst Denert Award for Software Engineering 2019
This open book provides an overview of the dissertations of the five nominees for the Ernst Denert Award for Software Engineering in 2019. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice.The book contains five papers describing the works by Sebastian Baltes (U Trier) on Software Developers'Work Habits and Expertise, Timo Greifenberg's thesis on Artefaktbasierte Analyse modellgetriebener Softwareentwicklungsprojekte, Marco Konersmann's (U Duisburg-Essen) work on Explicitly Integrated Architecture, Marija Selakovic's (TU Darmstadt) research about Actionable Program Analyses for Improving Software Performance, and Johannes Sp�th's (Paderborn U) thesis on Synchronized Pushdown Syste ...
This open book provides an overview of the dissertations of the five nominees for the Ernst Denert Award for Software Engineering in 2019. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice.The book contains five papers describing the works by Sebastian Baltes (U Trier) on Software Developers'Work Habits and Expertise, Timo Greifenberg's thesis on Artefaktbasierte Analyse modellgetriebener Softwareentwicklungsprojekte, Marco Konersmann's (U Duisburg-Essen) work on Explicitly Integrated Architecture, Marija Selakovic's (TU Darmstadt) research about Actionable Program Analyses for Improving Software Performance, and Johannes Sp�th's (Paderborn U) thesis on Synchronized Pushdown Syste ...