Applied Machine Learning for Health and Fitness : A Practical Guide to Machine Learning with Deep Vision, Sensors and IoT

Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more. Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley—who happens to be both a machine learning expert and a professional ski instructor—has written an insightful book that takes you on a journey of modern sport science and AI. Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the author’s practical expertise in both tech and sports is an undeniable asset for your learning process. Today’s data scientists are the future of athletics, and __Applied Machine Learning for Health and Fitness__ hands you the knowledge you need to stay relevant in this rapidly growing space. **What You'll Learn** - Use multiple data science tools and frameworks - Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition - Build and train neural networks, reinforcement learning models and more - Analyze multiple sporting activities with deep learning - Use datasets available today for model training Use machine learning in the cloud to train and deploy models- Apply best practices in machine learning and data science **Who This Book Is For**Primarily aimed at data scientists, coaches, sports enthusiasts and athletes interested in connecting sports with technology and AI methods. Contents About the Author About the Technical Reviewers Foreword to AI for Health and Fitness Introduction Part I: Getting Started Chapter 1: Machine Learning in Sports 101 Getting Started Areas of Machine Learning Logic and Machine Learning Projects and Code Introducing Tools Neural Networks Deep Vision Sensors Reinforcement Learning Summary Chapter 2: Physics of Sports Overview Mechanics Kinetics: Explaining Motion First Law of Motion (Law of Inertia) Second Law of Motion Third Law of Motion Kinematics: Projectile Motion Angular Motion Angular First Law (Law of Inertia) Angular Second Law Angular Third Law Conservation Laws Energy, Work, and Power Physics and Deep Learning Models Mechanics and Reinforcement Learning Summary Chapter 3: Data Scientist’s Toolbox Overview Languages Data Science Tools Virtual Environments Notebooks Markdown, Text, and Math Notebooks in the Cloud Notebooks Magic Setting Up and Starting Notebooks Data NumPy Data Modeling and Pandas Visualization Matplotlib SciPy, scikit-image OpenCV Deep Learning Frameworks PyTorch TensorFlow Keras Reinforcement Learning OpenAI Gym Cloud, Automation, and Operationalization Summary Chapter 4: Neural Networks Defining a Neural Network Neurons Activation Perceptron Creating a Dataset Initializing the Model Training the Model Validating the Model Decision Line Multilayer Networks Backpropagation Summary Chapter 5: Sensors Types of Sensors Deep Vision Deep Vision Devices Basic Device Edge Devices for Machine Learning Inertial Movement Sensors Basic IMU Attitude and Heading Reference System Inertial and Navigation Systems Range Imaging Sensors Pressure Sensors EMG Sensors Heart Rate Summary Part II: Applying Machine Learning Chapter 6: Deep Computer Vision Neuroscience and Deep Learning Computer Vision in Health and Fitness Loading Visual Datasets Model Zoo Applying Models Classification Detection Segmentation Human Body Keypoints Detection Summary Chapter 7: 2D Body Pose Estimation Background Methods Datasets Benchmarks Tools Surfing: Practical Keypoints Analysis First Look Paddling Pop-up Riding the Surfboard Beginning a Pose Estimation Project Tracking Points Over Time Finding Similarities Getting Project Data Basic Idea Detecting a Skill Level Multi-person Pose Estimations Dealing with Loss and Occlusion Summary Chapter 8: 3D Pose Estimation Overview Cameras and 3D World Camera Matrix Using a Single Camera Multiview Depth Reconstruction 3D Reconstruction with Sensors Motion Capture 3D Datasets 3D Machine Learning Methods Sparse and Dense Reconstruction Summary Chapter 9: Video Action Recognition Background Video Data Datasets Models Video Classification QuickStart Loading Videos for Classification Visualizing Dataset Video Normalization Training the Model Summary Chapter 10: Reinforcement Learning in Sports Introduction Tools Action and Observation Spaces Visualizing Sample Motion Training the Model Model Zoo Pendulum Model Humanoid Models Joints and Action Spaces Human Motion Capture Reinforcement Learning for Humanoids Summary Chapter 11: Machine Learning in the Cloud Overview Containers Notebooks in the Cloud Data in the Cloud Labeling Data in the Cloud Preparing for Training Model Training in the Cloud Running Experiments in the Cloud Model Management Summary Chapter 12: Automating and Consuming Machine Learning Overview Managing Models Creating a Scoring Script Defining an Environment Deploying Models Calling Your Model Continuous Machine Learning Delivery Machine Learning Pipelines Source Code Automating Model Delivery Runtime Environment Creating Training Step Defining Deployment Step Running the Pipeline Next Steps Summary Index Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more. Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashleywho happens to be both a machine learning expert and a professional ski instructorhas written an insightful book that takes you on a journey of modern sport science and AI.  Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the authors practical expertise in both tech and sports is an undeniable asset for your learning process. Todays data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space. What You'll Learn Use multiple data science tools and frameworks Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition Build and train neural networks, reinforcement learning models and more Analyze multiple sporting activities with deep learning Use datasets available today for model training Use machine learning in the cloud to train and deploy models Apply best practices in machine learning and data science Who This Book Is For Primarily aimed at data scientists, coaches, sports enthusiasts and athletes interested in connecting sports with technology and AI methods.  The Recent Influx Of Technology In The World Of Fitness Has Provided A Breeding Ground Of Innovation And Industry. The Application Of This Collected Data Can Be Used To Create Solutions For Athletic Performance Improvement, Movement Analysis, Physics Discoveries, And More. Machine Learning Can Take Our Understanding Of Exercise And Health To New Heights, And That’s Where Applied Machine Learning For Health And Fitness Comes In. Author Kevin Ashley—who Happens To Be Both An Iot Expert And A Professional Ski Instructor—has Written An Insightful Book That Takes You On A Journey Of Modern Sports Science And Artificial Intelligence. Ashley Uses His Real-world Knowledge To Teach You How To Make Your Own Iot Fitness Devices Such As Arduino Sensors For Physical Activity And Holograms For Sports Data Visualization. He Walks You Through Connecting These To Mobile Apps That Apply Machine Learning To Analyze Movement, And How To Use Those Findings For Growth And Solutions. Filled With Thorough, Engaging Illustrations And Dozens Of Real-life Examples, Applied Machine Learning For Health And Fitness Is Your Next Step To Understanding The Implementation Of Tech Within The Sports World. Whether You Are A Data Scientist, A Coach, An Athlete, Or Simply A Personal Fitness Enthusiast Excited About Connecting Your Findings With Ai Methods, The Author’s Practical Expertise In Both Tech And Sports Is An Undeniable Asset For Your Learning Process. Today’s Data Scientists Are The Future Of Athletics, And Applied Machine Learning For Health And Fitness Hands You The Knowledge You Need To Stay Relevant In This Rapidly Growing Space. What Readers Will Learn: Apply Machine Learning And Ai Methods On Sports Data Make Use Of Connected Iot Devices And Sensors For Sports Analyze Multiple Sporting Activities Visualize Sports Data With Holograms And Vr Who This Book Is For: Primarily Aimed At Data Scientists, Coaches, Sports Enthusiasts And Athletes Interested In Connecting Sports With Technology And Ai Methods. With This Book Machine Learning Becomes Fun And Engaging, When Applied To Real Life Scenarios Of Athlete Performance. This Book Tells A Unique Story Of Combining Advanced Machine Learning, Ai Methods With Practical Questions Coaches And Sports Technologists Ask Every Day. Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more. Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley--who happens to be both a machine learning expert and a professional ski instructor--has written an insightful book that takes you on a journey of modern sport science and AI. Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the author's practical expertise in both tech and sports is an undeniable asset for your learning process. Today's data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space. You will: Use multiple data science tools and frameworks Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition Build and train neural networks, reinforcement learning models and more Analyze multiple sporting activities with deep learning Use datasets available today for model training Use machine learning in the cloud to train and deploy models Apply best practices in machine learning and data science