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Springer Machine Learning for Computer Scientists & Analysts

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Description

Master the theoretical foundations and practical applications of machine learning with this comprehensive textbook from Springer. Designed specifically for computer scientists and data analysts, this book guides readers through the progression of ML algorithms, starting from basic neuron concepts and advancing to complex neural networks. You will explore sophisticated models including generative adversarial neural networks and graph convolution networks. Beyond theory, this text focuses on building the practical skills needed to select the most effective ML algorithm for specific problems. The content is supported by diverse case studies that cover real-world applications such as time-series forecasting, object recognition, and recommender systems. This applied perspective ensures that readers can bridge the gap between mathematical concepts and functional data science solutions.

Key Features

This Springer textbook provides a deep dive into the theoretical aspects of machine learning algorithms for professionals.

Learn to navigate complex architectures including generative adversarial neural networks and graph convolution networks.

Develop the technical skill to choose the most appropriate machine learning algorithm for any given problem.

Study real-world applications through case studies covering object recognition and recommender systems.

Gain practical experience with diverse models ranging from simple neuron basics to time-series forecasting.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
10 July 2023
Listed Since
13 June 2023

Barcode

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