We covered a lot of different things in this chapter. We started by learning the basics of a neural network and then we gradually proceeded. We learned the two most powerful types of neural networks used today—CNNs and RNNs—and we also learned about them on a high level, but without skipping their foundational units. We learned that as the complexity in a neural network increases, it requires a lot of computational power, which standard computers may fail to cater for we saw how this problem can be overcome by configuring a deep learning development environment using two different providers—AWS and Crestle. We explored Jupyter Notebooks, a powerful tool for performing deep learning tasks. We learned about the usage of two very popular Python libraries—NumPy and pandas. Both of these libraries are extensively used when performing deep learning...
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Table Of Contents
Hands-On Python Deep Learning for the Web
By :
Hands-On Python Deep Learning for the Web
By:
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)
Preface
Artificial Intelligence on the Web
Free Chapter
Demystifying Artificial Intelligence and Fundamentals of Machine Learning
Using Deep Learning for Web Development
Getting Started with Deep Learning Using Python
Creating Your First Deep Learning Web Application
Getting Started with TensorFlow.js
Getting Started with Different Deep Learning APIs for Web Development
Deep Learning through APIs
Deep Learning on Google Cloud Platform Using Python
DL on AWS Using Python: Object Detection and Home Automation
Deep Learning on Microsoft Azure Using Python
Deep Learning in Production (Intelligent Web Apps)
A General Production Framework for Deep Learning-Enabled Websites
Securing Web Apps with Deep Learning
DIY - A Web DL Production Environment
Creating an E2E Web App Using DL APIs and Customer Support Chatbot
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Appendix: Success Stories and Emerging Areas in Deep Learning on the Web
