Data Science Manuale Italiano Advanced Machine Learning E Deployment
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Data Science Manuale Italiano Advanced Machine Learning E Deployment
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Author : Mario A. B. Capurso
language : en
Publisher: Mario Capurso
Release Date : 2023-09-08
Data Science Manuale Italiano Advanced Machine Learning E Deployment written by Mario A. B. Capurso and has been published by Mario Capurso this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-08 with Computers categories.
Questa opera segue il curriculum 2021 della Association for Computing Machinery per specialisti in Scienze dei Dati, con l’obiettivo di costituire un “Bignami” della Scienza ed Ingegneria dei Dati e facilitare il percorso di formazione personale a partire da competenze specialistiche in Informatica o Matematica o Statistica per un lettore di lingua madre italiana. Parte di una serie di testi, riepiloga prima di tutto la metodologia di lavoro standard CRISP DM utilizzata in questa opera e in progetti di Scienza dei Dati. Poichè questo testo utilizza Orange per gli aspetti applicativi, ne descrive l’installazione ed i widget. La fase di modellizzazione dei dati viene considerata nell’ottica dell’apprendimento automatico riepilogando i tipi di apprendimento automatico, i tipi di modelli, i tipi di problemi e i tipi di algoritmi. Sono descritti gli aspetti avanzati associati alla modellizzazione quali le funzioni di perdita e di ottimizzazione come la gradient descent, le tecniche per analizzare le prestazioni dei modelli come il Bootstrapping e la Cross Validation. Vengono analizzati gli scenari di deployment e le più comuni piattaforme, con esempi applicativi. Vengono proposti i meccanismi per automatizzare l’apprendimento automatico e per supportare l’interpretabilità dei modelli e dei risultati come Partial Dependence Plot, Permuted Feature Importance e altre. Gli esercizi sono descritti con Orange e Python con l’uso della libreria Keras/Tensorflow. Il testo è corredato di materiale di supporto ed è possibile scaricare gli esempi in Orange e i dati di prova.
Data Science Quick Reference Manual Advanced Machine Learning And Deployment
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Author : Mario A. B. Capurso
language : en
Publisher: Mario Capurso
Release Date : 2023-09-08
Data Science Quick Reference Manual Advanced Machine Learning And Deployment written by Mario A. B. Capurso and has been published by Mario Capurso this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-08 with Computers categories.
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.
Data Science Manuale Italiano Deep Learning
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Author : Mario A. B. Capurso
language : it
Publisher: Mario Capurso
Release Date : 2023-09-04
Data Science Manuale Italiano Deep Learning written by Mario A. B. Capurso and has been published by Mario Capurso this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-04 with Computers categories.
Questa opera segue il curriculum 2021 della Association for Computing Machinery per specialisti in Scienze dei Dati, con l’obiettivo di costituire un “Bignami” della Scienza ed Ingegneria dei Dati e facilitare il percorso di formazione personale a partire da competenze specialistiche in Informatica o Matematica o Statistica per un lettore di lingua madre italiana. Parte di una serie di testi, riepiloga prima di tutto la metodologia di lavoro standard CRISP DM utilizzata in questa opera e in progetti di Scienza dei Dati. Poichè questo testo utilizza Orange per gli aspetti applicativi, ne descrive l’installazione ed i widget. La fase di modellizzazione dei dati viene considerata nell’ottica dell’apprendimento automatico riepilogando i tipi di apprendimento automatico. Sono descritte le tecniche di Deep Learning considerando le architetture del Perceptron, Neocognitron, il neurone con Backpropagation e le funzioni di attivazione, le Feed Forward Networks, gli Autoencoders, le reti ricorrenti e le LSTM e GRU, le Transformer Neural Networks, le Convolutional Neural Networks e le Generative Adversarial Networks ed analizzati i blocchi costruttivi. Gli esercizi sono descritti con Orange e Python con l’uso della libreria Keras/Tensorflow. Il testo è corredato di materiale di supporto ed è possibile scaricare gli esempi in Orange e i dati di prova.
The Data Science Design Manual
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Author : Steven S. Skiena
language : en
Publisher: Springer
Release Date : 2017-07-01
The Data Science Design Manual written by Steven S. Skiena and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-01 with Computers categories.
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
Exploring Splunk
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Author : David Carasso
language : en
Publisher: Cito Research
Release Date : 2012
Exploring Splunk written by David Carasso and has been published by Cito Research this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
Big data has incredible business value, and Splunk is the best tool for unlocking that value. Exploring Splunk shows you how to pinpoint answers and find patterns obscured by the flood of machinegenerated data. This book uses an engaging, visual presentation style that quickly familiarizes you with how to use Splunk. You'll move from mastering Splunk basics to creatively solving real-world problems, finding the gems hidden in big data.
Local Economic And Employment Development Leed Culture And Local Development
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Author : OECD
language : en
Publisher: OECD Publishing
Release Date : 2005-04-21
Local Economic And Employment Development Leed Culture And Local Development written by OECD and has been published by OECD Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-04-21 with categories.
This publication highlights the impact of culture on local economies and the methodological issues related to its identification.
Tax Administration 2021 Comparative Information On Oecd And Other Advanced And Emerging Economies
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Author : OECD
language : en
Publisher: OECD Publishing
Release Date : 2021-09-15
Tax Administration 2021 Comparative Information On Oecd And Other Advanced And Emerging Economies written by OECD and has been published by OECD Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-15 with categories.
This report is the ninth edition of the OECD's Tax Administration Series. It provides internationally comparative data on aspects of tax systems and their administration in 59 advanced and emerging economies.
Machine Learning
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Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2012-08-24
Machine Learning written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-24 with Computers categories.
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Handbook Of Natural Language Processing
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Author : Nitin Indurkhya
language : en
Publisher: CRC Press
Release Date : 2010-02-22
Handbook Of Natural Language Processing written by Nitin Indurkhya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-22 with Business & Economics categories.
The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater
Data Science Foundations Tools And Techniques
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Author : Michael Freeman
language : en
Publisher: Addison-Wesley Professional
Release Date : 2018-11-16
Data Science Foundations Tools And Techniques written by Michael Freeman and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-16 with Application software categories.
The Foundational Hands-On Skills You Need to Dive into Data Science "Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills." -From the foreword by Jared Lander, series editor Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience. Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you've uncovered. Step by step, you'll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything's focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to Install your complete data science environment, including R and RStudio Manage projects efficiently, from version tracking to documentation Host, manage, and collaborate on data science projects with GitHub Master R language fundamentals: syntax, programming concepts, and data structures Load, format, explore, and restructure data for successful analysis Interact with databases and web APIs Master key principles for visualizing data accurately and intuitively Produce engaging, interactive visualizations with ggplot and other R packages Transform analyses into sharable documents and sites with R Markdown Create interactive web data science applications with Shiny Collaborate smoothly as part of a data science team Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.