This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve. In addition, this book includes numerous case studies, ranging from simple time-series forecasting to object recognition and recommender systems using massive databases. Lastly, this book also provides practical implementation examples and assignments for the readers to practice and improve their programming capabilities for the ML applications.
1141704857
Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective
This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve. In addition, this book includes numerous case studies, ranging from simple time-series forecasting to object recognition and recommender systems using massive databases. Lastly, this book also provides practical implementation examples and assignments for the readers to practice and improve their programming capabilities for the ML applications.
89.99
In Stock
5
1
Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective
458Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective
458Paperback(1st ed. 2022)
$89.99
89.99
In Stock
Product Details
ISBN-13: | 9783030967581 |
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Publisher: | Springer International Publishing |
Publication date: | 07/10/2022 |
Edition description: | 1st ed. 2022 |
Pages: | 458 |
Product dimensions: | 6.10(w) x 9.25(h) x (d) |
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