Vettigli Giuseppe (20 results)

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Digital. Condition: New. Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualizationAbout This Book. Learn how to set up an optimal Python environment for data visualization. Understand how to import, clean and organize your data. Determine different approaches to data visualization and how to choose the most appropriate for your needsWho This Book Is ForIf you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you.What You Will Learn. Introduce yourself to the essential tooling to set up your working environment. Explore your data using the capabilities of standard Python Data Library and Panda Library. Draw your first chart and customize it. Use the most popular data visualization Python libraries. Make 3D visualizations mainly using mplot3d. Create charts with images and maps. Understand the most appropriate charts to describe your data. Know the matplotlib hidden gems. Use plot.ly to share your visualization onlineIn DetailPython Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts.Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.Style and approachA step-by-step recipe based approach to data visualization. The topics are explained sequentially as cookbook recipes consisting of a code snippet and the resulting visualization.

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Paperback. Condition: Brand New. 132 pages. 8.66x5.91x0.30 inches. In Stock.

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Taschenbuch. Condition: Neu. Optimization for networks and object recognition | Index coding and image analysis | Giuseppe Vettigli | Taschenbuch | 132 S. | Englisch | 2019 | Edizioni Accademiche Italiane | EAN 9786202087551 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Digital. Condition: New. Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualizationAbout This Book. Learn how to set up an optimal Python environment for data visualization. Understand how to import, clean and organize your data. Determine different approaches to data visualization and how to choose the most appropriate for your needsWho This Book Is ForIf you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you.What You Will Learn. Introduce yourself to the essential tooling to set up your working environment. Explore your data using the capabilities of standard Python Data Library and Panda Library. Draw your first chart and customize it. Use the most popular data visualization Python libraries. Make 3D visualizations mainly using mplot3d. Create charts with images and maps. Understand the most appropriate charts to describe your data. Know the matplotlib hidden gems. Use plot.ly to share your visualization onlineIn DetailPython Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts.Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.Style and approachA step-by-step recipe based approach to data visualization. The topics are explained sequentially as cookbook recipes consisting of a code snippet and the resulting visualization.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The present book explores two different application areas of combinatorial optimization, the work presented here, indeed, is two fold, since it deals with two distinct problems, one related to data transfer in networks and the other to object recognition. Caching is an essential technique to improve throughput and latency in a vast variety of applications. The core idea is to duplicate content in memories distributed across the network, which can then be exploited to deliver requested content with less congestion and delay. Automatic object recognition has become, over the last decades, a central topic in the artificial intelligence research, with a significant burt over the last new year with the advent of the deep learning paradigm; the objective of the work discussed in the Part 2 of this book is an attempt at improving the performance of a natural images classifier introducing in the loop knowledge coming from the real world, expressed in terms of probability of a set of spatial relations between the objects in the images. 132 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The present book explores two different application areas of combinatorial optimization, the work presented here, indeed, is two fold, since it deals with two distinct problems, one related to data transfer in networks and the other to object recognition. Caching is an essential technique to improve throughput and latency in a vast variety of applications. The core idea is to duplicate content in memories distributed across the network, which can then be exploited to deliver requested content with less congestion and delay. Automatic object recognition has become, over the last decades, a central topic in the artificial intelligence research, with a significant burt over the last new year with the advent of the deep learning paradigm; the objective of the work discussed in the Part 2 of this book is an attempt at improving the performance of a natural images classifier introducing in the loop knowledge coming from the real world, expressed in terms of probability of a set of spatial relations between the objects in the images.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The present book explores two different application areas of combinatorial optimization, the work presented here, indeed, is two fold, since it deals with two distinct problems, one related to data transfer in networks and the other to object recognition. Caching is an essential technique to improve throughput and latency in a vast variety of applications. The core idea is to duplicate content in memories distributed across the network, which can then be exploited to deliver requested content with less congestion and delay. Automatic object recognition has become, over the last decades, a central topic in the artificial intelligence research, with a significant burt over the last new year with the advent of the deep learning paradigm; the objective of the work discussed in the Part 2 of this book is an attempt at improving the performance of a natural images classifier introducing in the loop knowledge coming from the real world, expressed in terms of probability of a set of spatial relations between the objects in the images.