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Hands-On Python Deep Learning for the Web

You're reading from   Hands-On Python Deep Learning for the Web Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781789956085
Length 404 pages
Edition 1st Edition
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Authors (2):
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Sayak Paul Sayak Paul
Author Profile Icon Sayak Paul
Sayak Paul
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Toc

Table of Contents (19) Chapters Close

Preface Artificial Intelligence on the Web
Demystifying Artificial Intelligence and Fundamentals of Machine Learning FREE CHAPTER Using Deep Learning for Web Development
Getting Started with Deep Learning Using Python Creating Your First Deep Learning Web Application Getting Started with TensorFlow.js Getting Started with Different Deep Learning APIs for Web Development
Deep Learning through APIs Deep Learning on Google Cloud Platform Using Python DL on AWS Using Python: Object Detection and Home Automation Deep Learning on Microsoft Azure Using Python Deep Learning in Production (Intelligent Web Apps)
A General Production Framework for Deep Learning-Enabled Websites Securing Web Apps with Deep Learning DIY - A Web DL Production Environment Creating an E2E Web App Using DL APIs and Customer Support Chatbot Other Books You May Enjoy Appendix: Success Stories and Emerging Areas in Deep Learning on the Web

Summary

In this chapter, we covered the offerings from Microsoft AI and the Azure cloud for performing deep learning on websites. We saw how the Face API can be used to predict the gender and age of people in images, as well as how the Text Analytics API can be used to predict the language of a given text and the key phrases in the provided text or the sentiment of any sentence. Finally, we created a deep learning model using CNTK on the MNIST dataset. We saw how the model can be saved and then deployed via a Django-based web application in the form of an API. This deployment of the saved model via Django can be easily adapted for other deep learning frameworks, such as TensorFlow or PyTorch.

In the next chapter, we will discuss a generalized framework for building production-grade deep learning applications using Python.

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