Hands-On Python Deep Learning for the Web: Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow

Hands-On Python Deep Learning for the Web: Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow

English | 2020 | ISBN: 978-1789956085 | 404 Pages | PDF, EPUB, MOBI | 113 MB

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