While working on a project relating to deep learning, you must deal with a huge amount of variables of various types and arrays of various dimensions. Also, since the data contained in them is massive and keeps changing after nearly every step, we need a tool that helps us to observe the output produced by each step so that we can proceed accordingly. A Jupyter Notebook is one such tool. Jupyter Notebooks are known for their simplicity, and their wide support of features and platforms are currently the standard tool for developing deep-learning solutions. The reasons for their popularity can be understood by considering the fact that several of the top tech giants offer their own version of the tool, such as Google Colaboratory and Microsoft Azure Notebooks. Moreover, the popular code-hosting website GitHub has been providing a native rendering of Jupyter...
Hands-On Python Deep Learning for the Web
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Hands-On Python Deep Learning for the Web
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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
Customer Reviews