Rahul Khanna – författare

Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions.Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms.Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

The intensive care unit (ICU) is a specialised hospital ward where the ‘sickest-of-the-sick’ patients, often with life-threatening illness, receive around-the-clock monitoring and life support. There is a wide spectrum of conditions managed, and these present unique challenges for those who work in this field. Written in lay language by experienced ICU doctors (Intensivists), Psychiatrists, healthcare professionals outside of medicine and other stakeholders, “Stories from ICU doctors” provides insight and commentary around the nature and management of stressors for senior doctors working in the ICU. The first five sections of the book describe the distinctive nature of the ICU environment: the human factors involved, the characteristics of thriving Intensivists, the emotions they experience, and how they behave in response. The final three sections provide a synthesis of the advice of these clinicians for both current and future Intensivists, the advice from those who surround the Intensivist both at work and at home; and some concluding remarks about trainee suitability and selection for intensive care medicine.This book is for ICU patients and their families, it is for the families of ICU doctors, it is for doctors from other specialities, it is for the nurses and others that work alongside ICU doctors, and for anyone considering a career in ICU medicine and those who care for them.  We share these stories in hope that readers might better appreciate the human side of these doctors, better understand the complex working environment in which they practice, and perhaps better empathize with their stressors and struggles. Most importantly, this book is also for the ICU doctors who currently work within the specialty, to validate their feelings and experiences, and ultimately provide support by reminding them that they are not alone in navigating the difficulties they face.