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The Bleep Test Training Program is used by sports coaches and trainers to estimate and improve participant's VO2 max (maximum oxygen uptake). The Bleep Test Training Program is especially useful for players of sports like cross country, football, hockey, rugby, cricket, netball, soccer or tennis and is employed by many international sporting teams and government organisations such as the Police, Fire Service and the HM Forces as a training program to improve cardiovascular fitness, one of the all-important "Components of Fitness".
Designing Resistance Training Programs, Fourth Edition, is a guide to developing individualized training programs for both serious athletes and fitness enthusiasts. In this updated and expanded fourth edition, two of the world’s leading experts on strength training explore how to design scientifically based resistance training programs, modify and adapt programs to meet the needs of special populations, and apply the elements of program design in the real world. Fleck and Kraemer provide readers with a thorough understanding of the process of designing resistance training programs from both scientific and practical perspectives. As with previous editions, the fourth edition includes comprehensive tables that compare data and conclusions from research on core topics related to design of resistance training programs. By summarizing research and content for the reader, these tables offer a study guide, on-the-job reference, or starting point for further research. Designing Resistance Training Programs, Fourth Edition, is the only resource available that presents the body of research in the field in this organized and comprehensive format. The fourth edition has been thoroughly revised to present the most current information while retaining the studies that are the basis for concepts, guidelines, and applications in resistance training. Meticulously updated and heavily referenced, the fourth edition contains the following updates: • A full-color interior provides stronger visual appeal for the text. • Sidebars focus on a specific practical question or an applied research concept, allowing readers to connect research to real-life situations. • Multiple detailed tables summarize research from the text, offering an easy way to compare data and conclusions. • A glossary makes it simple to find key terms in one convenient location. • Newly added instructor ancillaries make the fourth edition a true learning resource for the classroom. Designing Resistance Training Programs, Fourth Edition, begins by outlining the principles of resistance training and exercise prescription, and examines the various types of strength training, including isometrics and eccentric training. This is followed by a discussion of resistance training from a physiological perspective and an overview of how resistance training programs interact with the other conditioning components such as aerobic, interval, plyometric, and flexibility training. Readers will then explore advanced training techniques, how to manipulate training variables in a long-term resistance training program, and ways to plan rest into long-term training that minimizes losses in fitness or performance gains. An important text for students, researchers, and practitioners, this textbook offers the information and tools to help readers evaluate resistance training programs and better understand the context and efficacy of new data findings in this ever-changing field. Designing Resistance Training Programs, Fourth Edition, is an essential resource for understanding the science behind resistance training and designing evidence-based resistance training programs for any population. This text provides the tools for understanding and designing resistance training programs for almost any situation or need.
Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.
Ch. 1 -- Introduction Ch. 2. Content and quality of entry-level driver training programs -- Ch. 3. Strategies and techniques to enhance training effectiveness -- Ch. 4. Survey inputs on the value of alternative training methods -- References -- Appendix A.