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

Malicious user detection

A malicious user on a website is any user who attempts to perform tasks that they are not authorized to do. In today's world, the threats posed by malicious users are increasing exponentially, with huge databases of personal information from several global tech giants, government agencies, and other private firms being exposed to the public by hackers. It is important to have systems in place that can automatically mitigate these malicious attacks. 

In order to recognize the malicious users in our sample web app, we have created a model that is able to learn the usual behavior of a user and raises the alarm if the user behavior at any instance changes significantly from their past usage. 

Anomaly detection is a popular branch of machine learning. It is a collection of algorithms that are used to detect data samples in a given dataset...