Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow por Anirudh Koul,Siddha Ganju,Meher Kasam

April 2, 2020

Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow de Anirudh Koul,Siddha Ganju,Meher Kasam está disponible para descargar en formato PDF y EPUB. Aquí puedes acceder a millones de libros. Todos los libros disponibles para leer en línea y descargar sin necesidad de pagar más.

Titulo del libro: Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow
Autor: Anirudh Koul,Siddha Ganju,Meher Kasam

Anirudh Koul,Siddha Ganju,Meher Kasam con Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

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.Relying on years of 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.

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