Whether you are a device engineer aspiring to go into the sector of deep Studying, a veteran knowledge scientist, or a hobbyist with a easy dream of creating the next viral AI app, you’ll have questioned the place to start. This step by step information teaches you the right way to construct sensible deep Studying programs for the cloud, cell, browsers, and side units The usage of a arms-on way.

Relying on years of trade enjoy remodeling deep Studying analysis into award-successful programs, Anirudh Koul, Siddha Ganju, and Meher Kasam information you in the course of the technique of changing an concept into one thing that individuals in the true global can use.

  • Train, track, and set up Pc Imaginative and prescient fashions with Keras, TensorFlow, Middle ML, and TensorFlow Lite.
  • Develop AI for a spread of units together with Raspberry Pi, Jetson Nano, and Google Coral.
  • Explore a laugh tasks, from Silicon Valley’s No longer Hotdog app to forty+ trade case research.
  • Simulate an Self sufficient Automobile in a online game surroundings and construct a miniature model with reinforcement Studying.
  • Use Switch Studying to coach fashions in mins.
  • Discover 50+ sensible Guidelines for maximizing type accuracy and Pace, debugging, and scaling to tens of millions of customers.
List of Chapters
  1. Exploring the Panorama of Synthetic Intelligence
  2. What’s within the Image: Symbol Category with Keras
  3. Cats As opposed to Canine: Switch Studying in 30 Strains with Keras
  4. Building a Opposite Symbol Seek Engine: Figuring out Embeddings
  5. From Beginner to Grasp Predictor: Maximizing Convolutional Neural Community Accuracy
  6. Maximizing Pace and Efficiency of TensorFlow: A To hand Checklist
  7. Practical Gear, Guidelines, and Tricks
  8. Cloud APIs for Pc Imaginative and prescient: Up and Working in 15 Minutes
  9. Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow
  10. AI within the Browser with TensorFlow.js and ml5.js
  11. Real-Time Item Category on iOS with Middle ML
  12. Not Hotdog on iOS with Middle ML and Create ML
  13. Shazam for Meals: Growing Android Apps with TensorFlow Lite and ML Kit
  14. Building the Purrfect Cat Locator App with TensorFlow Item Detection API
  15. Becoming a Maker: Exploring Embedded AI on the Edge
  16. Simulating a Self-Using Automobile The usage of Finish-to-Finish Deep Studying with Keras
  17. Building an Self sufficient Automobile in Beneath an Hour: Reinforcement Studying with AWS DeepRacer
Guest-contributed Content
The ebook options chapters from the next trade mavens:
  • Sunil Mallya (Amazon AWS DeepRacer)
  • Aditya Sharma and Mitchell Spryn (Microsoft Self sufficient Using Cookbook)
  • Sam Sterckval (Edgise)
  • Zaid Alyafeai (TensorFlow.js)
The ebook additionally options content material contributed via a couple of trade veterans together with François Chollet (Keras, Google), Jeremy Howard (, Pete Warden (TensorFlow Mobile), Anima Anandkumar (NVIDIA), Chris Anderson (3D Robotics), Shanqing Cai (TensorFlow.js), Daniel Smilkov (TensorFlow.js), Cristobal Valenzuela (ml5.js), Daniel Shiffman (ml5.js), Hart Woolery (CV 2020), Dan Abdinoor (Fritz), Chitoku Yato (NVIDIA Jetson Nano), John Welsh (NVIDIA Jetson Nano), and Danny Atsmon (Cognata).