£131.00

Springer Multi-faceted Deep Learning: Models and Data Book

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Description

Explore the wide range of Artificial Intelligence methods with Multi-faceted Deep Learning: Models and Data by Springer. This book provides a detailed look at Deep Learning applied to real-world problems, making it an essential resource for those studying machine learning. Starting with the fundamentals of the Deep Learning approach, the text summarizes different types of Deep Neural Networks (DNNs). This summary serves as a comprehensive preamble for the more specific, problem-oriented chapters that follow. Readers will find discussions on the most interesting and open problems in machine learning within the Deep Learning framework, along with proposed solutions. One of the key technical highlights includes a demonstration of how to implement zero-shot learning using Deep Neural Network C. Whether you are looking to understand the basics of DNNs or seeking solutions to complex machine learning challenges, this book offers a structured path through modern AI methodologies.

Key Features

Covers a large set of Artificial Intelligence methods focused on applying Deep Learning to real-world problems.

Provides a clear summary of Deep Learning fundamentals and various types of Deep Neural Networks (DNNs).

Explores the most interesting and open problems currently facing machine learning in the Deep Learning framework.

Offers proposed solutions to complex challenges found in modern machine learning research.

Includes practical guidance on implementing zero-shot learning with Deep Neural Network C.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
20 October 2021
Listed Since
19 March 2021

Barcode

No barcode data available

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