Machine Learning Q and AI (Sebastian Raschka) (Z-Library)

Author： Sebastian Raschka

Machine learning and AI are moving at a rapid pace. Researchers and practitioners are constantly struggling to keep up with the breadth of concepts and techniques. This book provides bite-sized nuggets for your journey from machine learning beginner to expert, covering topics from various machine learning areas. Even experienced machine learning researchers and practitioners will encounter something new that they can add to their arsenal of techniques. The book is structured into five main chapters: Chapter 1, Deep Learning and Neural Networks covers questions about deep neural networks and deep learning that are not specific to a particular subdomain. For example, we discuss alternatives to supervised learning and techniques for reducing overfitting. Chapter 2, Computer Vision focuses on topics mainly related to deep learning but are specific to computer vision, many of which cover convolutional neural networks and vision transformers. Chapter 3, Natural Language Processing covers topics around working with text, many of which are related to transformer architectures and self-attention. Chapter 4, Production, Real-World, And Deployment Scenarios contains questions pertaining to practical scenarios, such as increasing inference speeds and various types of distribution shifts. Chapter 5, Predictive Performance and Model Evaluation dives a bit deeper into various aspects of squeezing out predictive performance, for example, changing the loss function, setting up k-fold cross-validation, and dealing with limited labeled data. It is for readers and machine learning practitioners who want to advance their understanding and learn about useful techniques that I consider significant and relevant but often overlooked in traditional and introductory textbooks and classes.