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

Deep Learning through APIs

So far, we have become familiar with the basic pipeline that is followed in a deep learning project. We have completed two basic end-to-end projects in previous chapters using the Keras and TensorFlow.js libraries. We have become familiar with Python libraries such as NumPy, pandas, and Keras, and we have also seen how deep learning models can be developed using JavaScript. We have also used the Flask framework to create an API out of a deep learning model. In chapter 4, Getting Started with TensorFlow.js, we used third-party Application Programming Interfaces (APIs) to create a web application.

In this chapter, we are going to study the whole concept of APIs in detail. Starting with a more informal definition of APIs, we are going to take a look at all APIs that are relevant to deep learning. We will first look at some of the most widely known...