image top
Giỏ hàng Giỏ hàng 0
Không có sản phẩm trong giỏ hàng.
Email cho bạn bè

Practical Deep Learning for Cloud Mobile and Edge

320,000₫

This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.

  • ✪ Miễn phí GIAO HÀNG đơn hàng từ 399.000đ
  • ✪ Giao hàng COD toàn quốc nhanh chóng từ 2 - 4 ngày
  • ✪ Giao hàng HOẢ TỐC trong nội thành Hà Nội
  • ✪ Hỗ trợ xuất hóa đơn VAT theo yêu cầu
Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow ( sách gia công, bìa mềm )

Practical Deep Learning for Cloud Mobile and Edge
 

Thể loại:Computers - Computer Science
Năm:2019
In lần thứ:1
Ngôn ngữ:English
Trang:959
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. If your goal is to build something creative, useful, scalable, or just plain cool, this book is for you.
Relying on decades of combined industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.
Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite.
Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral.
Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies.
Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning.
Use transfer learning to train models in minutes.
Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.