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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 briefly introduced many important concepts and terminologies that are vital to execute an ML project in general. These are going to be helpful throughout this book.

We started with what AI is and its three major types. We took a look at the factors that are responsible for the AI explosion that is happening around us. We then took a quick tour of several components of ML and how they contribute to an ML project. We saw what DL is and how AI, ML, and DL are connected.

Toward the very end of this chapter, we saw some examples where AI is being merged with web technologies to make intelligent applications that promise to solve complex problems. Behind almost all of the AI-enabled applications sits DL.

In the next chapters, we are going to leverage DL to make smart web applications.

You have been reading a chapter from
Hands-On Python Deep Learning for the Web
Published in: May 2020
Publisher: Packt
ISBN-13: 9781789956085
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