Informatyka
GUNNALA GOVARDHAN, Daniele Tosatto
Mastering Cloud Development using Microsoft Azure. Click here to enter text
Roberto Freato, Marco Parenzan
Microsoft Azure is a cloud computing platform that supports many different programming languages, tools, and frameworks, including both Microsoft-specific and third-party software and systems.This book starts by helping you set up a professional development environments in the cloud and integrating them with your local environment to achieve improved efficiency. You will move on to create front-end and back-end services, and then build cross-platform applications using Azure. Next you’ll get to grips with advanced techniques used to analyze usage data and automate billing operations. Following on from that, you will gain knowledge of how you can extend your on-premise solution to the cloud and move data in a pipeline.In a nutshell, this book will show you how to build high-quality, end-to-end services using Microsoft Azure. By the end of this book, you will have the skillset needed to successfully set up, develop, and manage a full-stack Azure infrastructure.
Krishnendu Kar
Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.
Krishnendu Kar
Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.
Quan Nguyen
Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming.Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples.By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language
Javier Fernández González
Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. Java 9 includes a comprehensive API with lots of ready-to-use components for easily implementing powerful concurrency applications, but with high flexibility so you can adapt these components to your needs.The book starts with a full description of the design principles of concurrent applications and explains how to parallelize a sequential algorithm. You will then be introduced to Threads and Runnables, which are an integral part of Java 9's concurrency API. You will see how to use all the components of the Java concurrency API, from the basics to the most advanced techniques, and will implement them in powerful real-world concurrency applications.The book ends with a detailed description of the tools and techniques you can use to test a concurrent Java application, along with a brief insight into other concurrency mechanisms in JVM.
Mastering Customer Success. Discover tactics to decrease churn and expand revenue
Jeff Mar, Peter Armaly, Mark Stouse
The rapidly evolving customer success landscape has left many Customer Success Managers (CSMs) struggling to keep pace with the complexities of this dynamic role. This Customer Success book bridges the gap by providing a comprehensive framework for mastering the essential skills required to excel.You’ll find out why the Customer Success function is indispensable today and gain expert insights into account segmentation, financial tiering, ideal customer profiles, and the complexity of customer engagement process design. You’ll then delve into playbook development, where you’ll find practical guidance for CSMs and Customer Success Operations Managers who want to improve their ability to drive desired business outcomes. Through insightful case studies, the authors illustrate their own experience of successful Customer Success implementation, showing you what it takes to exceed customer expectations with well-designed, proactive services. The journey doesn’t end there—it extends to highlighting the resilience required to build and operate successful Customer Success organizations.By the end of this guide, you’ll be equipped with the tactics and mindset necessary to stand out as a world-class Customer Success leader in your organization, driving growth at every turn.
Megan Squire
Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy – without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding.If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get.By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.
Mastering Data Visualization with Microsoft Visio Professional 2016. Click here to enter text
David Parker
Microsoft Visio Professional is a data visualization application that is used by many different market sectors and many different departments to represent information visually, from network infrastructure to organization charts, from process diagrams to office layouts. Starting off with a brief introduction to Visio Professional 2016 and then moving on to data storage, linking data to shapes, and working with SQL Server to create a solid foundation. Then we’ll cover topics such as refreshing data, working with geographical data, working with various graphics, and diagrams, and more. Finally, you’ll find out how to deploy custom stencils, templates, and code.