Book Image

Hands-On Python Deep Learning for the Web

By : Anubhav Singh, Sayak Paul
Book Image

Hands-On Python Deep Learning for the Web

By: Anubhav Singh, Sayak Paul

Overview of this book

When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages. By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices.
Table of Contents (19 chapters)
1
Artificial Intelligence on the Web
3
Using Deep Learning for Web Development
7
Getting Started with Different Deep Learning APIs for Web Development
12
Deep Learning in Production (Intelligent Web Apps)
Appendix: Success Stories and Emerging Areas in Deep Learning on the Web

Summary

In this chapter, we combined several technologies to come up with an end-to-end project that demonstrates one of the most rapidly growing aspects of applying deep learning to websites. We covered tools such as Dialogflow, Dialogflow Gateway, GCP IAM, Firebase Cloud Functions, and ngrok. We also demonstrated how to build a REST API-based UI and how to make it accessible using the Web Speech API. The Web Speech API, although presently at a nascent stage, is a cutting-edge piece of technology used in web browsers and is expected to grow rapidly in the coming years.

It is safe to say that deep learning on the web has huge potential and will be a key factor in the success of many upcoming businesses. In the next chapter, we'll explore some of the hottest research areas in deep learning for web development and how we can plan to progress in the best way.

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