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)
Artificial Intelligence on the Web
Using Deep Learning for Web Development
Getting Started with Different Deep Learning APIs for Web Development
Deep Learning in Production (Intelligent Web Apps)
Appendix: Success Stories and Emerging Areas in Deep Learning on the Web

The relation between AI, ML, and DL

To make sure that our basics are clear regarding the distinction between AI, ML, and DL, let's refer to the following diagram, which elegantly captures the relationship between these three big names:

The diagram is quite self-explanatory and it has been referred to in many books in the field of DL. Let's try drawing an interesting conclusion from this diagram.

All DL systems are ML systems and therefore all DL systems are AI systems as well. But the converse is not truenot all AI systems are DL systems. 

The statement may appear slightly confusing at first glance, but if we got our basics right, then this captures the distinction between AI, ML, and DL beautifully. We will proceed toward revisiting some of the necessary ML terminologies and concepts that will be required in the latter parts of this book.