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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

Security measures, monitoring techniques, and performance optimization

In this section, we will talk about the security measures, monitoring techniques, and performance optimizations that can be integrated into a DL solution in production. These functionalities are essential to maintaining solutions that depend on AI backends. While we have discussed the security methods facilitated by DL in previous chapters, we will discuss the possible security threats that could be posed to an AI backend.

One of the largest security threats to AI backends is from noisy data. In most of the methodologies for having AI in production, it is important to regularly check for new types of noise in the dataset that it is trained on.

Here is a very important message for all developers who love the Python pickle library:

The preceding screenshot is taken from the official Python documentation at...
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