Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

This book is an effort by AI Technology & Systems to demystify the TinyML technology including market, applications, algorithms, tools and technology. the book dive deeper into the technology beyond common application and keep it light for the readers with varying background including students, hobbyists, managers, market researchers and developers. It starts with introduction to TinyML with benefits and scalability. It introduces no-code and low-code tinyML platform to develop production worthy solutions including audio wake word, visual wake word, American sign language and predictive maintenance. Last two chapters are devoted to sensor and hardware agnostic autoML and tinyML compiler technologies. More information at http://thetinymlbook.com/

Recently, Tiny Machine Learning (TinyML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated embedded IoT and low power wide area networks (LPWAN) nodes with very low on-board memory and computational capabilities are getting connected to the Internet each year, there is a critical need to have a special computational framework like TinyML. TinyML for Edge Intelligence in IoT and LPWAN Networks presents the evolution, developments, and advances in TinyML as applied to IoT and LPWANs. It starts by providing the foundations of IoT/LPWANs, low power embedded systems and hardware, the role of artificial intelligence and machine learning in communication networks in general and cloud/edge intelligence. It then presents the concepts, methods, algorithms and tools of TinyML. Practical applications of the use of TinyML are given from health and industrial fields which provide practical guidance on the design of applications and the selection of appropriate technologies. TinyML for Edge Intelligence in IoT and LPWAN Networks is highly suitable for academic researchers and professional system engineers, architects, designers, testers, deployment engineers seeking to design ultra-lower power and time-critical applications. It would also help in designing the networks for emerging and future applications for resource-constrained nodes. This book provides one-stop solutions for emerging TinyML for IoT and LPWAN applications. The principles and methods of TinyML are explained, with a focus on how it can be used for IoT, LPWANs, and 5G applications. Applications from the healthcare and industrial sectors are presented. Guidance on the design of applications and the selection of appropriate technologies is provided.

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

This book constitutes the refereed proceedings of five International Workshops held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, virtually and in Hersonissos, Crete, Greece, in June 2022: the 11th Mining Humanistic Data Workshop (MHDW 2022); the 7th 5G-Putting Intelligence to the Network Edge Workshop (5G-PINE 2022); the 1st workshop on AI in Energy, Building and Micro-Grids (AIBMG 2022); the 1st Workshop/Special Session on Machine Learning and Big Data in Health Care (ML@HC 2022); and the 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics (AIBEI 2022). The 35 full papers presented at these workshops were carefully reviewed and selected from 74 submissions.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Build easy and accessible solutions for automating mundane processes in marketing, sales, operations, and finance to enable teams to focus on core tasks Key FeaturesLearn Zapier and find solutions to specific problems with this comprehensive yet concise guideExplore various scenarios describing specific business problems and how they can be solved with ZapierDiscover expert tips and practical examples to harness the full potential of ZapierBook Description Zapier is an emerging no-code workflow automation technology that enables organizations to connect their cloud-based and web applications and automate data transfer between them. Zapier's built-in features and flexibility allow users to integrate thousands of business applications and create simple to complex automation to reduce time spent on repetitive tasks, thereby increasing productivity. This book is a must-have for business owners, their employees, and independent freelancers and contractors looking to use Zapier for business process automation. The book takes a hands-on approach to implementation and associated problem-solving methodologies that will have you up-and-running and productive in no time while leveling up your automation skills. You'll discover how to plan your automation building for optimal results, what are the native features available in Zapier, and the applications that connect with it, as well as how to optimally configure your workflows to automate your processes in as few steps as possible. Finally, you'll find out how to create advanced workflow automation from scratch and learn how to troubleshoot issues. By the end of this Zapier book, you'll be able to build your own advanced workflow automation using Zapier, addressing the key pain points encountered in businesses with manual and repetitive tasks. What you will learnThink creatively to plan your business workflows to overcome specific business problemsGet to grips with the native features and built-in applications available in ZapierExplore different types of third-party business applications that integrate with ZapierConfigure your workflows optimally to automate business processes and minimize task usageUse Zapier's library of pre-built workflows and create advanced workflows from scratchDiscover the extensive functionality and practical uses of Zapier's built-in appsWho this book is for This book is for solutions architects, process consultants, business analysts, virtual assistants, digital marketers, CRM consultants, online business managers, technical consultants, bookkeepers, and accountants who want to deploy effective automation techniques in Zapier. This book will help micro, small, or medium-sized businesses to increase their productivity using workflow automation with Zapier, as well as freelancers and contractors providing digital process improvement, systemizing, and automation services. No prior experience with business process automation or Zapier is required.

Low power wide area network (LPWAN) is a promising solution for long range and low power Internet of Things (IoT) and machine to machine (M2M) communication applications. The LPWANs are resource-constrained networks and have critical requirements for long battery life, extended coverage, high scalability, and low device and deployment costs. There are several design and deployment challenges such as media access control, spectrum management, link optimization and adaptability, energy harvesting, duty cycle restrictions, coexistence and interference, interoperability and heterogeneity, security and privacy, and others. LPWAN Technologies for IoT and M2M Applications is intended to provide a one-stop solution for study of LPWAN technologies as it covers a broad range of topics and multidisciplinary aspects of LPWAN and IoT. Primarily, the book focuses on design requirements and constraints, channel access, spectrum management, coexistence and interference issues, energy efficiency, technology candidates, use cases of different applications in smart city, healthcare, and transportation systems, security issues, hardware/software platforms, challenges, and future directions. One stop guide to the technical details of various low power long range technologies such as LoRaWAN, Sigfox, NB-IoT, LTE-M and others Describes the design aspects, network architectures, security issues and challenges Discusses the performance, interference, coexistence issues and energy optimization techniques Includes LPWAN based intelligent applications in diverse areas such as smart city, traffic management, health and others Presents the different hardware and software platforms for LPWANs Provides guidance on selecting the right technology for an application

The book presents high-quality research papers presented at the International Conference on Computational Intelligence and Data Analytics (ICCIDA 2022), organized by the Department of Information Technology, Vasavi College of Engineering, Hyderabad, India in January 2022. ICCIDA provides an excellent platform for exchanging knowledge with the global community of scientists, engineers, and educators. This volume covers cutting-edge research in two prominent areas – computational intelligence and data analytics, and allied research areas.

This book focuses on both theoretical and practical aspects of the “Device-Edge-Cloud continuum”, a development approach aimed at the seamless provision of next-generation cyber-physical services through the dynamic orchestration of heterogeneous computing resources, located at different distances to the user and featured by different peculiarities (high responsiveness, high computing power, etc.). The book specifically explores recent advances in paradigms, architectures, models, and applications for the “Device-Edge-Cloud continuum”, which raises many 'in-the-small' and 'in-the-large' issues involving device programming, system architectures and methods for the development of IoT ecosystem. In this direction, the contributions presented in the book propose original solutions and aim at relevant domains spanning from healthcare to industry, agriculture and transportation.