£126.43

Morgan Kaufmann Multimodal Learning Using Heterogeneous Data

Name: Morgan Kaufmann Multimodal Learning Using Heterogeneous Data
Brand: Morgan Kaufmann
Price: 126.43 GBP
Availability: InStock

AI & Machine Learning

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Description

Expand your understanding of advanced computing with Multimodal Learning Using Heterogeneous Data. Published by Morgan Kaufmann, this book serves as a specialized resource for those studying the complex fields of AI and Machine Learning. As data becomes increasingly diverse, the ability to process different types of information simultaneously is becoming a fundamental part of computer science. This text addresses the technical requirements of working with heterogeneous data sets, providing a foundation for researchers and professionals in the AI community. Whether you are a student or a practitioner, this book offers a focused look at how multimodal learning functions within modern computational frameworks. By adding this title to your technical library, you gain access to academic insights from a respected publisher in the computing and internet sector. It is designed to support your professional development in machine learning and help you navigate the evolving landscape of artificial intelligence.

Key Features

Expertly published by Morgan Kaufmann to ensure high academic and professional standards for your technical library.

Focused content specifically designed for the AI and Machine Learning subject area.

Provides essential knowledge for professionals working within the Computing and Internet sector.

A valuable resource for students studying advanced Computer Science topics.

Covers the specialized topic of multimodal learning and heterogeneous data applications.

Product Specifications

Brand: Morgan Kaufmann
Format: Paperback
ASIN: 0443275289
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 19 December 2025
Listed Since: 16 April 2025

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Morgan Kaufmann Multimodal Learning Using Heterogeneous Data

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