Search results for ""author pete warden""

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 a search term, including Github * APIs for finding information from just a name, including WhitePages * Services that help you locate people with accounts, such as Klout * Search APIs, including BOSS and Wikipedia * Geographical data sources, including SimpleGeo and US Census * Company information APIs, such as CrunchBase and ZoomInfo * APIs that list IP address, such as MaxMind * Services that list books, films, music, and products

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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

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