This new, third edition of Jack Phillips's classic Handbook of Training Evaluation and Measurement Methods shows the reader not only how to design, implement, and assess the effectiveness of HRD programs, but how to ultimately measure their return on investment (ROI). Each chapter has been revised and updated to include additional research, expanded coverage, and new examples of Dr. Phillips's case studies. Seven entirely new chapters have also been added, focusing largely on ROI.

This book reviews the main principles of resistance training, from basics to modern insights. It includes practical ways to develop most of the strength training methods, including monitoring and testing procedures. It merges practical tips with knowledge about the scientific background concerning program and periodization. It describes procedures for special populations, such as elderly or women. Gathering contributions by authoritative researchers and professors in the fields of sport science and biomechanics, this book provides an integrated view of strength training programming, and describes the most important biological factors associated with this type of training. The evidence-based and detailed description of each single mechanism to be trained to enhance performance is covered in depth. Thanks to its strong academic background, an being self-contained, this book offers a valuable reference guide for advanced undergraduate and graduate students in sports science, as well as an inspiring guide for sport and health researchers and professional trainers alike.

This book covers the essentials of needs analysis from the emerging trainer's perspective by providing just the right amount of support and knowledge without going too deep into the subject. The topics covered include when and how to do a training needs analysis; using informal and formal analysis techniques; goal, task and population analysis; and how to develop and present a training plan for management approval. Each chapter includes appropriate data gathering tools. The Skilled Trainer series provides practical guidance for those who've had some exposure to training and would like to take their career to the next level.

Before thinking about which exercises, frequency and intensity should be used in strength training programs to achieve muscle strength, promote a healthy lifestyle, hypertrophy or improve athletic performance, we should understand firstly how a muscle contraction happens. The aim of this book is to provide you with a basic to advanced understanding about several physiological systems involved in muscle contraction: physiological adaptations for exercises, especially strength exercises; molecular responses; methods of training; nutrition and muscle performance; muscle recovery; benefits of strength exercises to human health; and which drugs are commonly used for doping in different sports. Chapter One of this book will review all of the most important cells, structures and processes involved in muscle contraction, such as neurons cells, synapses, motor units, general and molecular mechanisms of muscle contraction. Chapter Two will describe the most important bioenergetics pathways of energy supplies required for muscle contraction. After understanding how muscle contraction happens and how energy is supplied, the reader will discover principles, methods and physiological adaptations of skeletal muscles to strength trainings within Chapters Three, Four and Five. In particular, Chapter Four will provide a friendly and detailed account of molecular adaptation in skeletal muscles to different exercises, discussing differential gene expression and the effects of different exercises in different populations and disorders. This book also points out a very important and detailed factor concerning cryotherapy in Chapter Six, one of most common strategies used to promote muscle recovery. Specific orientations regarding nutrition and muscle performance are presented in Chapter Seven, while doping and different drugs used to increase muscle performance in several sports introduce Chapter Eight. Finally, Chapter Nine describes the benefits of strength training to diabetic, hypertensive, obese and myopathic patients. We strongly believe that all knowledge within this book is essential for all professionals working to promote the health of patients and increase physical performance of athletes or their clients, as well as for all people linked to sports and physical activity in some way.

For generations, The Koehler Method of Dog Training has been a beacon of wisdom for dog owners seeking effective techniques to build a strong bond with their furry companions. William Koehler’s approach, rooted in respect for dogs’ intelligence, remains as relevant today as it was when the book first graced the shelves. Koehler believed that dogs could make choices and be held accountable for their behavior. His method follows the pattern of Action → Memory → Desire: dogs act, remember the outcomes, and form desires based on those experiences. Correcting behavior becomes fair, reasonable, and expected once the training has been successfully imparted. In this classic guide, you’ll find practical guidance for creating a well-behaved and happy canine companion. From basic obedience to advanced skills, The Koehler Method covers it all. Whether you’re a seasoned dog owner or a first-time puppy parent, discover the joy of training your dog using a method that respects their intelligence and fosters a lifelong partnership.

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

After you have learned the rules of chess and developed some tactical abilities, how do you go from there? You are now ready to tackle basic issues of strategy, but what is the best way to improve and win more games? Of course, you have to train. But chess training only makes sense if it fits your level of play and if it is structured in an accessible way. Experienced chess trainer Yaroslav Srokovski has developed a practical, well-structured, compact first course in positional understanding. You will learn two fundamental skills: how to assess a position on the board and how to decide which long-term objectives you should aim for in what sort of positions. In 12 chapters Srokovksi teaches you things like: how to handle your pawns, what weak squares and strong squares are, bad pieces and good pieces, why it is important if your king is in the middle or not, why and how to get an open line, the problem of knight against bishop, what piece coordination means and why everyone talks about the bishop pair. This course, which includes many exercises, is tried and tested and ideally suited to bring post-beginners at their next level. ,