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题名/责任者:

Introducing machine learning / Dino Esposito, Francesco Esposito = 机器学习开发实战 / [意]迪诺·埃斯波西托, 弗朗西斯科·埃斯波西托著.

出版发行项:

北京 : China Machine Press, 2021.

ISBN:

9787111671817

载体形态项:

xxv, 349 pages : illustrations ; 24 cm.

丛编说明:

经典原版书库

个人责任者:

Esposito, Dino, author.

附加个人名称:

Esposito, Francesco, author.

论题主题:

Machine learning.

论题主题:

Artificial intelligence.

中图法分类号:

TP181

一般附注:

"HZ books"

一般附注:

"华章教育"

一般附注:

"英文版"

内容附注:

How humans learn -- Intelligent software -- Mapping problems and algorithms -- General steps for a machine learning solution -- The data factor -- The .NET way -- Implementing the ML.NET pipeline -- ML.NET tasks and algorithms -- Math foundations of machine learning -- Metrics of machine learning -- How to make simple predictions: linear regression -- How to make complex predictions and decisions: trees -- How to make better decisions: ensemble methods -- Probabilistic methods: Naive Bayes -- How to group data: classification and clustering -- Feed-forward neural networks -- Design of a neural network -- Other types of neural networks -- Sentiment analysis: an end-to-end solution -- AI cloud services for the real world -- The business perception of AI.

摘要附注:

Today, machine learning offers software professionals unparalleled opportunity for career growth. In Introducing Machine Learning, best-selling software development author, trainer, and consultant Dino Esposito offers a complete introduction to the field for programmers, architects, lead developers, and managers alike. Esposito begins by illuminating what's known about how humans and machines learn, introducing the most important classes of machine learning algorithms, and explaining what each of them can do. Esposito demystifies key concepts ranging from neural networks to supervised and unsupervised learning. Next, he explains each step needed to build a successful machine learning solution, from collecting and fine-tuning source data to building and testing your solution. Then, building on these essentials, he guides you through constructing two complete solutions with ML.NET, Microsoft's powerful open source and cross-platform machine learning framework. Step by step, you'll create systems for performing sentiment analysis on social feeds, and analyzing traffic to predict accidents. By the time you're finished, you'll be ready to participate in data science projects and build working solutions of your own.

原版附注:

Reprint. Originally published: Hoboken : Pearson Education, [2020]. 9780135565667.

语种附注:

Text in English; with Chinese publisher's note, preface and contents.

豆瓣简介：