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

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