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

ML – the most popular form of AI

Without taking any mathematical notations or too many theoretical details, let's try to approach the term Machine Learning (ML) from an intuitive perspective. For doing this, we will have to take a look at how we actually learn. Do you recollect, at school, when we were taught to identify the parts of speech in a sentence? We were presented with a set of rules to identify the part of the speeches in a sentence. We were given many examples and our teachers in the first place used to identify the parts of speeches in sentences for us to train us effectively so that we could use this learning experience to identify the parts of speeches in sentences that were not taught to us. Moreover, this learning process is fundamentally applicable to anything that we learn.  

What if we could similarly train the machines? What if we...