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Practical Deep Learning for Cloud, Mobile, and Edge. Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow (ebook)(audiobook)(audiobook)Książka w języku angielskim

Okładka książki/ebooka Practical Deep Learning for Cloud, Mobile, and Edge. Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

Okładka książki Practical Deep Learning for Cloud, Mobile, and Edge. Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

Okładka książki Practical Deep Learning for Cloud, Mobile, and Edge. Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

Okładka książki Practical Deep Learning for Cloud, Mobile, and Edge. Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
620
2w1 w pakiecie:
     ePub
     Mobi

Ebook

269,00 zł
228,65 zł

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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
0 Anirudh Koul, Siddha Ganju, Meher Kasam

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