PDF/BOOK AI and Machine Learning for On-Device Development: A Programmer's Guide
COPY LINK: https://pdf.bookcenterapp.com/yumpu/109810174X 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
COPY LINK: https://pdf.bookcenterapp.com/yumpu/109810174X
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
- Keine Tags gefunden...
Erfolgreiche ePaper selbst erstellen
Machen Sie aus Ihren PDF Publikationen ein blätterbares Flipbook mit unserer einzigartigen Google optimierten e-Paper Software.
AI and Machine Learning for On-Device
Development: A Programmer's Guide
.
AI and Machine Learning for On-Device
Development: A Programmer's Guide
Simple Step to Read and Download:
1. Create a FREE Account
2. Choose from our vast selection of EBOOK and PDF
3. Please, see if you are eligible to Read or Download book AI and Machine Learning for On-Device Development: A
Programmer's Guide
4. Read Online by creating an account AI and Machine Learning for On-Device Development: A Programmer's Guide
READ [MAGAZINE]
AI and Machine Learning for On-Device
Development: A Programmer's Guide
DESCRIPTION
COPY LINK: https://pdf.bookcenterapp.com/yumpu/109810174X 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