Hands-On Python Deep Learning for the Web

Use the power of deep learning with Python to build and deploy intelligent web applications

Key Features

• Create next-generation intelligent web applications using Python libraries such as Flask and Django
• Implement deep learning algorithms and techniques for performing smart web automation
• Integrate neural network architectures to create powerful full-stack web applications

Book Description

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.

What you will learn

• Explore deep learning models and implement them in your browser
• Design a smart web-based client using Django and Flask
• Work with different Python-based APIs for performing deep learning tasks
• Implement popular neural network models with TensorFlow.js
• Design and build deep web services on the cloud using deep learning
• Get familiar with the standard workflow of taking deep learning models into production

Who this book is for

This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you're a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.

Table of Contents

1. Demystifying Artificial Intelligence and Fundamentals of Machine Learning
2. Getting Started with Deep Learning Using Python
3. Creating Your First Deep Learning Web Application
4. Getting Started with TensorFlow.js
5. Deep Learning through APIs
6. Deep Learning on Google Cloud Platform Using Python
7. DL on AWS Using Python: Object Detection and Home Automation
8. Deep Learning on Microsoft Azure Using Python
9. A General Production Framework for Deep Learning-Enabled Websites
10. Securing Web Apps with Deep Learning
11. DIY - A Web DL Production Environment
12. Creating an E2E Web App Using DL APIs and Customer Support Chatbot
13. Appendix: Success Stories and Emerging Areas in Deep Learning on the Web

Anubhav Singh, a web developer since before Bootstrap was launched, is an explorer of technologies, often pulling off crazy combinations of uncommon tech. An international rank holder in the Cyber Olympiad, he started off by developing his own social network and search engine as his first projects at the age of 15, which stood among the top 500 websites of India during their operational years. He's continuously developing software for the community in domains with roads less walked on. You can often catch him guiding students on how to approach ML or the web, or both together. He's also the founder of The Code Foundation, an AI-focused start-up. Anubhav is a Venkat Panchapakesan Memorial Scholarship awardee and an Intel Software Innovator.

Sayak Paul is currently with PyImageSearch, where he applies deep learning to solve real-world problems in computer vision and bring solutions to edge devices. He is responsible for providing Q&A support to PyImageSearch readers. His areas of interest include computer vision, generative modeling, and more. Previously at DataCamp, Sayak developed projects and practice pools. Prior to DataCamp, Sayak worked at TCS Research and Innovation (TRDDC) on data privacy. There, he was a part of TCS's critically acclaimed GDPR solution called Crystal Ball. Outside of work, Sayak loves to write technical articles and speak at developer meetups and conferences.