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. As of early 2022, the supplemental code files are available at https: //oreil.ly/XuIQ4. 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

If you're a developer looking to supplement your own data tools and services, this concise ebook covers the most useful sources of public data available today. You'll find useful information on APIs that offer broad coverage, tie their data to the outside world, and are either accessible online or feature downloadable bulk data. You'll also find code and helpful links.

This guide organizes APIs by the subjects they cover--such as websites, people, or places--so you can quickly locate the best resources for augmenting the data you handle in your own service. Categories include: Website tools such as WHOIS, bit.ly, and Compete Services that use email addresses as search terms, including Github Finding information from just a name, with APIs such as WhitePages Services, such as Klout, for locating people with Facebook and Twitter accounts Search APIs, including BOSS and Wikipedia Geographical data sources, including SimpleGeo and U.S. Census Company information APIs, such as CrunchBase and ZoomInfo APIs that list IP addresses, such as MaxMind Services that list books, films, music, and products

There's been a massive amount of innovation in data tools over the last few years, thanks to a few key trends: * *Learning from the web*. Techniques originally developed by website developers coping with scaling issues are increasingly being applied to other domains. * *CS+?=$$$*. Google have proven that research techniques from computer science can be effective at solving problems and creating value in many real-world situations. That's led to increased interest in cross-pollination and investment in academic research from commercial organizations. * *Cheap hardware*. Now that machines with a decent amount of processing power can be hired for just a few cents an hour, many more people can afford to do large-scale data processing. They can't afford the traditional high prices of professional data software though, so they've turned to open-source alternatives. These trends have led to a Cambrian Explosion of new tools, which means when you're planning a new data project you have a lot to choose from. This guide aims to help you make those choices by describing each tool from the perspective of a developer looking to use them in an application. Wherever possible, this will be from my first-hand experiences, or from colleagues who have used the systems in production environments. I've made a deliberate choice to include my own opinions and impressions, so you should see this guide as a starting point for exploring the tools, not the final word. I'll do my best to explain what I like about each service but your tastes and requirements may well be quite different. Since the goal is to help experienced engineers navigate the new data landscape, the guide only covers tools that have been created or risen to prominence in the last few years. For example, PostGres is not covered because it's been widely used for over a decade, but its Greenplum derivative is newer and less well-known, so it is included.