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.

Related Content you might be interested in

Current Title:

Small book image
Hands-On Python Deep Learning for the Web
Anubhav Singh | Sayak Paul
May, 2020 | 404 pages
Small book image
Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter
Anubhav Singh | Rimjhim Bhadani
Apr, 2020 | 380 pages
Small book image
Hands-On Transfer Learning with Python
Dipanjan Sarkar | Raghav Bali | Tamoghna Ghosh
Aug, 2018 | 438 pages
Small book image
Deep Learning with R Cookbook
Swarna Gupta | Rehan Ali Ansari | Dipayan Sarkar
Feb, 2020 | 328 pages
Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Artificial Intelligence on the Web
Artificial Intelligence on the Web
Free Chapter
2
Demystifying Artificial Intelligence and Fundamentals of Machine Learning
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
3
Using Deep Learning for Web Development
Using Deep Learning for Web Development
4
Getting Started with Deep Learning Using Python
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
5
Creating Your First Deep Learning Web Application
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
6
Getting Started with TensorFlow.js
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
7
Getting Started with Different Deep Learning APIs for Web Development
Getting Started with Different Deep Learning APIs for Web Development
8
Deep Learning through APIs
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
9
Deep Learning on Google Cloud Platform Using Python
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
10
DL on AWS Using Python: Object Detection and Home Automation
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
11
Deep Learning on Microsoft Azure Using Python
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
12
Deep Learning in Production (Intelligent Web Apps)
Deep Learning in Production (Intelligent Web Apps)
13
A General Production Framework for Deep Learning-Enabled Websites
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
14
Securing Web Apps with Deep Learning
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 
15
DIY - A Web DL Production Environment
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
16
Creating an E2E Web App Using DL APIs and Customer Support Chatbot
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
17
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Appendix: Success Stories and Emerging Areas in Deep Learning on the Web
Appendix: Success Stories and Emerging Areas in Deep Learning on the Web
Success stories
Key emerging areas
Summary

Creating your first project on GCP

A project helps you organize all your GCP resources systematically. Creating a project on GCP can be done in just a matter of a few clicks:

  1. After signing in to your Google account, open up your GCP console using https://console.cloud.google.com. In the top-left corner, you should see Google Cloud Platform and just beside that, you can see a drop-down list, as shown:

  1. If you did create any projects while signing up for GCP or previously, then one of your projects will appear in the marked area (fast-ai-exploration and gcp-api are two projects that I created on GCP). Now, click on the down arrow and a popup should appear:

  1. Click on NEW PROJECT to proceed. You should end up on a page, shown in the following screenshot, that will ask you to specify the project's name. GCP automatically generates an ID for the project that you are creating...
Previous Section
End of Section 4
Next Section