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Hands-On Python Deep Learning for the Web

By : Anubhav Singh, Paul

4.5 (2)

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Hands-On Python Deep Learning for the Web

4.5 (2)

By: Anubhav Singh, Paul

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Artificial Intelligence on the Web

Free Chapter

Demystifying Artificial Intelligence and Fundamentals of Machine Learning

Introduction to artificial intelligence and its types

ML – the most popular form of AI

What is DL?

The relation between AI, ML, and DL

Revisiting the fundamentals of ML

A standard ML workflow

The web before and after AI

Biggest web-AI players and what are they doing with AI

Summary

Using Deep Learning for Web Development

Getting Started with Deep Learning Using Python

Demystifying neural networks

Different types of neural network

Exploring Jupyter Notebooks

Setting up a deep-learning-based cloud environment

Exploring NumPy and pandas

Summary

Creating Your First Deep Learning Web Application

Technical requirements

Structuring a deep learning web application

Understanding datasets

Implementing a simple neural network using Python

Creating a Flask API to work with server-side Python

Using the API via cURL and creating a web client using Flask

Improving the deep learning backend

Summary

Getting Started with TensorFlow.js

Technical requirements

The fundamentals of TF.js

Your first deep learning web application with TF.js

Advantages and limitations of TF.js

Summary

Getting Started with Different Deep Learning APIs for Web Development

Deep Learning through APIs

What is an API?

The importance of using APIs

How is an API different from a library?

Some widely known deep learning APIs

Some lesser-known deep learning APIs

Choosing a deep learning API provider

Summary

Deep Learning on Google Cloud Platform Using Python

Technical requirements

Setting up your GCP account

Creating your first project on GCP

Using the Dialogflow API in Python

Using the Cloud Vision API in Python

Using the Cloud Translation API in Python

Summary

DL on AWS Using Python: Object Detection and Home Automation

Technical requirements

Getting started in AWS

A short tour of the AWS offerings

Getting started with boto3

Using the Rekognition API in Python

Using the Alexa API in Python

Summary

Deep Learning on Microsoft Azure Using Python

Technical requirements

Setting up your account in Azure

A walk-through of the deep learning services provided by Azure

Object detection using the Face API and Python

Extracting text information using the Text Analytics API and Python

An introduction to CNTK

A brief introduction to Django web development

Making predictions using CNTK from the Django project

Summary

Deep Learning in Production (Intelligent Web Apps)

A General Production Framework for Deep Learning-Enabled Websites

Technical requirements

Defining the problem statement

How not to build an AI backend

A sample end-to-end AI-integrated web application

Building an interface

Summary

Securing Web Apps with Deep Learning

Technical requirements

The story of reCAPTCHA

Malicious user detection

An LSTM-based model for authenticating users

A Django-based app for using an API

Using reCAPTCHA in web applications with Python

Website security with Cloudflare

Summary

DIY - A Web DL Production Environment

Technical requirements

An overview of DL in production methods

Popular tools for deploying ML in production

Implementing a demonstration DL web environment

Deploying the project to Heroku

Security measures, monitoring techniques, and performance optimization

Summary

Creating an E2E Web App Using DL APIs and Customer Support Chatbot

Technical requirements

An introduction to NLP

An introduction to chatbots

Creating a Dialogflow bot with the personality of a customer support representative

Using ngrok to facilitate HTTPS APIs on localhost

Creating a testing UI using Django to manage orders

Speech recognition and speech synthesis on a web page using the Web Speech API

Summary

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Appendix: Success Stories and Emerging Areas in Deep Learning on the Web

Success stories

Key emerging areas

Summary

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Deep Learning on Google Cloud Platform Using Python

In the previous chapter, we saw a variety of deep learning APIs that are provided by various organizations. We also saw their applicability broadly grouped into two categories—the first was computer vision and image processing, and the second was natural language processing. We are going to continue exploring deep learning APIs in this chapter as well. This chapter introduces you to Google Cloud Platform (GCP) and three APIs offered by it in the area of deep learning.

In this chapter, we will cover the following topics: