The third edition of this handbook is designed to provide a broad coverage of the concepts, implementations, and applications in metaheuristics.
It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more.
This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. The book covers the... Læs mere
The authors present deep learning case studies on all data described.Deep learning models: Neural network models are a class of machine learning methods with a long history.
The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML... Læs mere
This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine... Læs mere
This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work.
This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists,... Læs mere
This hands-on guide presents three problem-focused ways to learn ML: no code using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. You'll learn key ML concepts by using real-world datasets with realistic problems.