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

Improving the deep learning backend

The simple model we have trained here is hardly one that we can claim is close to a perfect model. There are several methods that we can use to extend this model to make it better. For instance, some of the most basic steps that we can take to improve our deep learning model are as follows:

  • Increase training epochs: We have only trained our model for 10 epochs, which is usually a very small value for any deep learning model. Increasing the number of training epochs can improve the accuracy of the model. However, it can also lead to overfitting and so the number of epochs must be experimented with.
  • More training samples: Our web client currently doesn't do much more than show the predicted value. However, we could extend it to get feedback from the user on whether the prediction we made was correct. We can then add the user's input...