000			01895nam a22002177a 4500
003			ZET-ke
005			20220125103438.0
008			220125b ||||| |||| 00| 0 eng d
020			_a9781492034865
040			_aDLC _beng _cDLC _dZET-ke
050			_aQA76.76.A65 _b.K68 2019
100			_aKoul, Anirudh
245			_aPractical deep learning for cloud, mobile, and edge : _bReal-world AI and computer-vision projects using python, keras, and tensorflow / _cAnirudh Koul, Siddha Ganju, and Meher Kasam.
260			_aBeijing : _bO'Reilly, _c2019.
300			_axxvi, 588 p. : _bill. ; _c24 cm
504			_aIncludes index.
505			_aExploring 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.
700			_aGanju, Siddha
710			_4Kasam, Meher
942			_2lcc _cBK _kQA76.76.A65 _m.K68 2019
999			_c4987 _d4987