AI and machine learning for coders : a Programmer's guide to artificial intelligence By Laurence Moroney.

By:

Moroney, Laurence

Material type: Text

9789385889226

Subject(s):

Machine learning. Artificial intelligence

DDC classification:

006.31 MOR-A

Contents:

Building models. Introduction to TensorFlow -- Introduction to computer vision -- Going beyond the basics: detecting features in images -- Using public datasets with TensorFlow datasets -- Introduction to natural language processing -- Making sentiment programmable using embeddings -- Recurrent neural networks for natural language processing -- Using TensorFlow to create text -- Understanding sequence and time series data -- Creating ML models to predict sequences -- Using convolutional and recurrent methods for sequence models -- Using models. An introduction to TensorFlow Lite -- Using TensorFlow Lite in Android apps -- Using TensorFlow Lite in iOS apps -- An introduction to TensorFlow.js -- Coding techniques for computer vision in TensorFlow.js -- Reusing and converting Python models to JavaScript -- Transfer learning in JavaScript -- Deployment with TensorFlow serving -- AI ethics, fairness and privacy.

Summary: If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving.

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Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode
Book	Dept. of Computational Biology and Bioinformatics Processing Center	Dept. of Computational Biology and Bioinformatics	006.31 MOR-A (Browse shelf(Opens below))	Available		DCB3992

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Building models. Introduction to TensorFlow --
Introduction to computer vision --
Going beyond the basics: detecting features in images --
Using public datasets with TensorFlow datasets --
Introduction to natural language processing --
Making sentiment programmable using embeddings --
Recurrent neural networks for natural language processing --
Using TensorFlow to create text --
Understanding sequence and time series data --
Creating ML models to predict sequences --
Using convolutional and recurrent methods for sequence models --
Using models. An introduction to TensorFlow Lite --
Using TensorFlow Lite in Android apps --
Using TensorFlow Lite in iOS apps --
An introduction to TensorFlow.js --
Coding techniques for computer vision in TensorFlow.js --
Reusing and converting Python models to JavaScript --
Transfer learning in JavaScript --
Deployment with TensorFlow serving --
AI ethics, fairness and privacy.

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving.

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