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Hands-On Python Deep Learning for the Web
Hands-On Python Deep Learning for the Web

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

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Hands-On Python Deep Learning for the Web

Demystifying Artificial Intelligence and Fundamentals of Machine Learning

"Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years."
                                                                                                                                                                              &...

Introduction to artificial intelligence and its types

In a simpler sense, artificial intelligence is all about giving machines the ability to perform intelligently. For example, many of us can play chess. Essentially, we do this first by learning the fundamentals of playing the game and then we engage ourselves in actually playing the game with others. But can machines do this? Can machines learn on their own and play the game of chess with us? 

AI attempts to make this possible by giving us the power to synthesize what we call intelligence in terms of some rules and instill it into machines. Machines as mentioned here can be anything that can compute. For example, it could be software or a robot. 

There are actually several types of AI. The popular ones are the following:

  • Fuzzy systems
  • Expert systems
  • ML systems

The final type sounds the most familiar here. We will...

ML – the most popular form of AI

Without taking any mathematical notations or too many theoretical details, let's try to approach the term Machine Learning (ML) from an intuitive perspective. For doing this, we will have to take a look at how we actually learn. Do you recollect, at school, when we were taught to identify the parts of speech in a sentence? We were presented with a set of rules to identify the part of the speeches in a sentence. We were given many examples and our teachers in the first place used to identify the parts of speeches in sentences for us to train us effectively so that we could use this learning experience to identify the parts of speeches in sentences that were not taught to us. Moreover, this learning process is fundamentally applicable to anything that we learn.  

What if we could similarly train the machines? What if we...

What is DL?

Now comes the most exciting part and probably the hottest technical term of this century. Reality apart, we now understand the learning to some extent, so let's get to the first part of the term deep learningdeep

DL is a type of machine learning but it is purely based on neural networks. We will take a look at neural networks too but in the next chapter. The basic objective of any machine learning system is to learn useful representations of the data given to it. But what makes DL different? It turns out that DL systems treat data as a representation of layers. For example, an image can be treated as a representation of layers of varying properties such as edges, contours, orientation, texture, and gradients. The following diagram from the book, Deep Learning with Python, by François Chollet captures this idea nicely:

In...

The relation between AI, ML, and DL

To make sure that our basics are clear regarding the distinction between AI, ML, and DL, let's refer to the following diagram, which elegantly captures the relationship between these three big names:

The diagram is quite self-explanatory and it has been referred to in many books in the field of DL. Let's try drawing an interesting conclusion from this diagram.

All DL systems are ML systems and therefore all DL systems are AI systems as well. But the converse is not truenot all AI systems are DL systems. 

The statement may appear slightly confusing at first glance, but if we got our basics right, then this captures the distinction between AI, ML, and DL beautifully. We will proceed toward revisiting some of the necessary ML terminologies and concepts that will be required in the latter parts of this book. 

...

Revisiting the fundamentals of ML

We have already seen what is meant by ML. In this section, we will focus on several terminologies such as supervised learning and unsupervised learning, and we will be taking a look at the steps involved in a standard ML workflow. But you may ask: why ML? We are supposed to learn about the applications of DL in this book. We just learned that DL is a type of ML only. Therefore, a quick overview of the basic ML-related concepts will certainly help. Let's start with several types of ML and how they differ from each other. 

Types of ML

ML encompasses a multitude of algorithms and topics. While every such algorithm that makes up an ML model is nothing but a mathematical computation on...

A standard ML workflow

Any project starts with a problem in mind and ML projects are no exception. Before starting an ML project, it is very important to have a clear understanding of the problem that you are trying to solve using ML. Therefore, problem formulation and mapping with respect to the standard ML workflow serve as good starting points in an ML project. But what is meant by an ML workflow? This section is all about that. 

Designing ML systems and employing them to solve complex problems requires a set of skills other than just ML. It is good to know that ML requires knowledge of several things such as statistics, domain knowledge, software engineering, feature engineering, and basic high-school mathematics in varying proportions. To be able to design such systems, certain steps are fundamental to almost any ML workflow and each of these steps requires a certain...

The web before and after AI

If you have been a regular user of the World Wide Web since 2014, you'd agree to a visible rapid flurry of changes in websites. From solving ReCaptcha challenges of increasingly illegible writing to being automatically marked as human in the background, web development has been one of the forerunners in the display of the wealth of artificial intelligence that has been created over the last two decades.

Sir Tim Berners-Lee, attributed as the inventor of the internet, has put forward his views on a Semantic Web:

"I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A "Semantic Web", which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy, and...

Biggest web-AI players and what are they doing with AI

The growth spurt of AI saw several contenders running to make the most out of it. Over the last two decades, several individuals, start-ups, and even huge industrialists have sought to reap the benefits offered by the applications of AI. There are products in the market to whom artificial intelligence serves as the very heart of their business.

"War is 90% information."                                       
                                                                                         ...

Summary

In this chapter, we briefly introduced many important concepts and terminologies that are vital to execute an ML project in general. These are going to be helpful throughout this book.

We started with what AI is and its three major types. We took a look at the factors that are responsible for the AI explosion that is happening around us. We then took a quick tour of several components of ML and how they contribute to an ML project. We saw what DL is and how AI, ML, and DL are connected.

Toward the very end of this chapter, we saw some examples where AI is being merged with web technologies to make intelligent applications that promise to solve complex problems. Behind almost all of the AI-enabled applications sits DL.

In the next chapters, we are going to leverage DL to make smart web applications.

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Key benefits

  • 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

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.

Who is this book 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.

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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Length: 404 pages
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Table of Contents

17 Chapters
Artificial Intelligence on the Web Chevron down icon Chevron up icon
Demystifying Artificial Intelligence and Fundamentals of Machine Learning Chevron down icon Chevron up icon
Using Deep Learning for Web Development Chevron down icon Chevron up icon
Getting Started with Deep Learning Using Python Chevron down icon Chevron up icon
Creating Your First Deep Learning Web Application Chevron down icon Chevron up icon
Getting Started with TensorFlow.js Chevron down icon Chevron up icon
Getting Started with Different Deep Learning APIs for Web Development Chevron down icon Chevron up icon
Deep Learning through APIs Chevron down icon Chevron up icon
Deep Learning on Google Cloud Platform Using Python Chevron down icon Chevron up icon
DL on AWS Using Python: Object Detection and Home Automation Chevron down icon Chevron up icon
Deep Learning on Microsoft Azure Using Python Chevron down icon Chevron up icon
Deep Learning in Production (Intelligent Web Apps) Chevron down icon Chevron up icon
A General Production Framework for Deep Learning-Enabled Websites Chevron down icon Chevron up icon
Securing Web Apps with Deep Learning Chevron down icon Chevron up icon
DIY - A Web DL Production Environment Chevron down icon Chevron up icon
Creating an E2E Web App Using DL APIs and Customer Support Chatbot Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Kaushik Jun 23, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very nice book, it contains a lot of knowledge that I ever wanted.Love at first sight for me🤭🤭💓
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Rebeka Mukherjee Jul 01, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Disclaimer: I was sent a copy by the publisher and was asked to review this book.Pros: I love that anybody with basic coding skills and an interest in exploring deep learning and web development can work through the projects in this book. The book begins with a nice refresher of introductory deep learning concepts and then moves on to the implementation of web-based deep learning projects that are easy to follow. I also like the variety of the projects in this book.Cons: Since this is a hand-on book and not a theory book, the mathematics behind deep learning concepts is kept to a minimum. If one wants deeper knowledge of deep learning, they have to look elsewhere. I also feel this book is a bit more focused on the web development part than the deep learning part.
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