Learning TensorFlow.js: Powerful Machine Learning in JavaScript Paperback - 2021
by Laborde, Gant
- New
A$54.97
A$5.76
Delivery within USA
Standard delivery: 5 to 11 days
More delivery options
Standard delivery: 5 to 11 days
Ships from Ambis Enterprises LLC (Michigan, United States)
Details
- Title Learning TensorFlow.js: Powerful Machine Learning in JavaScript
- Author Laborde, Gant
- Binding Paperback
- Condition New
- Pages 338
- Volumes 1
- Language ENG
- Publisher O'Reilly Media
- Publication date 2021-06-15
- Features Bibliography, Index
- Bookseller's Inventory # OTF-S-9781492090793
- ISBN 9781492090793 / 1492090794
- Weight 1 lbs (0.45 kg)
- Dimensions 9.1 x 6.9 x 0.8 in (23.11 x 17.53 x 2.03 cm)
- Category Computers - General Information
- Library of Congress subjects Machine learning
- Dewey Decimal Code 006.31
- Quantity available 117
About Ambis Enterprises LLC Michigan, United States
Specialising in: New Books, Used Books
Biblio member since 2009
We love books, and love our customers. We underrate our book conditions to ensure you're happy, and handpack our shipments with pride!
30 day return guarantee, with full refund including shipping costs for up to 30 days after delivery if an item arrives damaged. Please Contand us at Admin@lakesidebooks.com
Reader reviews for Learning TensorFlow.js: Powerful Machine Learning in JavaScript
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information