If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.
You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code.
You'll learn: how to build models with TensorFlow using skills that employers desire; the basics of machine learning by working with code samples; how to implement computer vision, including feature detection in images; how to use NLP to tokenize and sequence words and sentences; methods for embedding models in Android and iOS; and how to serve models over the web and in the cloud with TensorFlow Serving.
AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating and running models on popular mobile platforms such as iOS and Android.
Laurence Moroney, lead AI advocate at Google, offers an introduction to machine learning techniques and tools, then walks you through writing Android and iOS apps powered by common ML models like computer vision and text recognition, using tools such as ML Kit, TensorFlow Lite, and Core ML. If you're a mobile developer, this book will help you take advantage of the ML revolution today.
- Explore the options for implementing ML and AI on mobile devices
- Create ML models for iOS and Android
- Write ML Kit and TensorFlow Lite apps for iOS and Android, and Core ML/Create ML apps for iOS
- Choose the best techniques and tools for your use case, such as cloud-based versus on-device inference and high-level versus low-level APIs
- Learn privacy and ethics best practices for ML on devices
