Practical deep learning for cloud, mobile, and edge : Real-world AI and computer-vision projects using python, keras, and tensorflow /
Koul, Anirudh
Practical deep learning for cloud, mobile, and edge : Real-world AI and computer-vision projects using python, keras, and tensorflow / Anirudh Koul, Siddha Ganju, and Meher Kasam. - Beijing : O'Reilly, 2019. - xxvi, 588 p. : ill. ; 24 cm
Includes index.
Exploring the landscape of Artificial Intelligence -- What's in the picture: Image classification with Keras -- Cats versus dogs: Transfer learning in 30 lines with Keras -- Building a reverse image search engine: Understanding embeddings -- From Novice to master predictor: Maximizing convolutional neural network accuracy -- Maximizing speed and performance of tensorflow: A handy checklist -- Practical tools, tips, and tricks -- Cloud APIs for computer vision: Up and running in 15 minutes -- Scalable inference serving on cloud with tensorflow serving and Kubeflow -- AI in the browser with tensorflow.Js and ml5.Js -- Real-time object classification on iOS with core ML -- Not hotdog on iOS with core ML and create ML -- Shazam for food: Developing android Apps with tensorflow lite and ML kit -- Building the purrfect Cat locator App with tensorflow object detection API -- Becoming a maker: Exploring embedded AI at the edge -- Simulating a self-driving car using end-to-end deep learning with Keras -- Building an autonomous car in under an hour: Reinforcement learning with AWS deepracer -- Appendix: A crash course in convolutional neutral networks.
9781492034865
QA76.76.A65 / .K68 2019
Practical deep learning for cloud, mobile, and edge : Real-world AI and computer-vision projects using python, keras, and tensorflow / Anirudh Koul, Siddha Ganju, and Meher Kasam. - Beijing : O'Reilly, 2019. - xxvi, 588 p. : ill. ; 24 cm
Includes index.
Exploring the landscape of Artificial Intelligence -- What's in the picture: Image classification with Keras -- Cats versus dogs: Transfer learning in 30 lines with Keras -- Building a reverse image search engine: Understanding embeddings -- From Novice to master predictor: Maximizing convolutional neural network accuracy -- Maximizing speed and performance of tensorflow: A handy checklist -- Practical tools, tips, and tricks -- Cloud APIs for computer vision: Up and running in 15 minutes -- Scalable inference serving on cloud with tensorflow serving and Kubeflow -- AI in the browser with tensorflow.Js and ml5.Js -- Real-time object classification on iOS with core ML -- Not hotdog on iOS with core ML and create ML -- Shazam for food: Developing android Apps with tensorflow lite and ML kit -- Building the purrfect Cat locator App with tensorflow object detection API -- Becoming a maker: Exploring embedded AI at the edge -- Simulating a self-driving car using end-to-end deep learning with Keras -- Building an autonomous car in under an hour: Reinforcement learning with AWS deepracer -- Appendix: A crash course in convolutional neutral networks.
9781492034865
QA76.76.A65 / .K68 2019