Hands-On Python Deep Learning for the Web

Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow

Anubhav Singh , Sayak Paul 지음

Packt(GCO Science) 2020년 05월 15일 출간

작품소개

책소개

▶Book 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.

▶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

▶Key Features
- 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

▶Who This Book Is 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.

목차

▶TABLE of CONTENTS
▷ Section 1: Artificial Intelligence on the Web
1. Demystifying Artificial Intelligence and Fundamentals of Machine Learning
▷ Section 2: Using Deep Learning for Web Development
2. Getting Started with Deep Learning Using Python
3. Creating Your First Deep Learning Web Application
4. Getting Started with TensorFlow.js
▷ Section 3: Getting Started with Different Deep Learning APIs for Web Development
5. Deep Learning through APIs
6. Deep Learning on Google Cloud Platform Using Python
7. DL on AWS Using Python: Object Detection and Home Automation
8. Deep Learning on Microsoft Azure Using Python
▷ Section 4: Deep Learning in Production (Intelligent Web Apps)
9. A General Production Framework for Deep Learning-Enabled Websites
10. Securing Web Apps with Deep Learning
11. DIY - A Web DL Production Environment
12. Creating an E2E Web App Using DL APIs and Customer Support Chatbot

책 속으로

▶What this book covers
- Chapter 1, Demystifying Artificial Intelligence and Fundamentals of Machine Learning, briefly introduces machine learning, deep learning, and other forms of artificial intelligence methodologies related to web development. This chapter quickly goes over fundamental topics of the machine learning pipeline, such as exploratory data analysis, data preprocessing, feature engineering, training and testing, models of evaluation, and more. Toward the end, a comparison between the interactivity and user experience offered by websites before AI became popular and how they are in the modern day is presented. We also study the usage of AI on the web by some of the biggest firms and how AI has revolutionized their products.

- Chapter 2, Getting Started with Deep Learning Using Python, introduces basic concepts and terminologies related to deep learning and how to use deep learning to build a simple web app with different deep learning libraries in Python.

- Chapter 3, Creating Your First Deep Learning Web Application, discusses several important concepts regarding the structure of web applications specifically for leveraging deep learning. It then proceeds to discuss the approaches to understanding a dataset. The chapter also shows how to implement and improve a simple neural network and how it can be wrapped into an API for the development of a simple web application. We then proceed to showcase how the API can be implemented using different standard web tech stacks.

- Chapter 4, Getting Started with TensorFlow.js, introduces the most popular JavaScript library for deep learning―TensorFlow.js (Tf.js). It gives a brief overview of what TensorFlow.js is and the things it is capable of doing in a browser. Furthermore, this chapter shows how you can use pre-trained models using TensorFlow.js and build a simple web application with it.

- Chapter 5, Deep Learning through APIs, introduces the concept of APIs and their importance in software development. Further, the chapter proceeds to show examples of different deep learning APIs. Toward the very end, the chapter presents an approach to choosing deep learning API providers to suit particular use cases. The deep learning APIs covered are the Vision API, the Text API, and others.

- Chapter 6, Deep Learning on Google Cloud Platform Using Python, introduces the offerings by Google Cloud Platform for web developers to integrate into their websites. The focus is on Dialogflow, which can be used to make chatbots and conversational AIs; the Cloud Inference API, which can be used to build a good recommendation system; and also the Translation API, which is used to provide users in different locales with website content in their languages. The chapter discusses their applications at length and also demonstrates a basic how-to for using them with Python.

- Chapter 7, DL on AWS Using Python: Object Detection and Home Automation, introduces Amazon Web Services and talks briefly about the various offerings, including the Alexa API and the Rekognition API. The Alexa API can be used to build home automation web apps and other interactive interfaces, while the Rekognition API can be used to detect people and objects in photos and videos.

- Chapter 8, Deep Learning on Microsoft Azure Using Python, introduces Microsoft Azure Cloud Services, focusing on the Cognitive Toolkit, which is Microsoft's alternative to TensorFlow's Emotion API, which can be used to determine the emotion of a person from a photograph of their face, and the Text-to-Speech API, which can be used to generate natural-sounding voice from text.

- Chapter 9, A General Production Framework for Deep Learning-Enabled Websites, introduces the general framework to be set up for the efficient deployment of deep learning on the web in a production environment. Strategies for reducing computing resources, converting raw datasets to datasets for training deep learning models, and...

출판사 서평

▶ Preface
Deep learning techniques can be used to develop intelligent web apps. Over the last few years, tremendous growth in the number of companies adopting deep learning techniques in their products and businesses has been observed. There has been a significant surge in the number of start-ups providing AI and deep learning-based solutions for niche problems. This book introduces numerous tools and technological practices used to implement deep learning in web development using Python.

To start with, you will learn about the fundamentals of machine learning, with a focus on deep learning and the basics of neural networks, along with their common variants, such as convolutional neural networks, and how you can integrate them into websites with frontends built with different standard web tech stacks. You will create your deep learningenabled web application using Python libraries such as Django and Flask by creating REST APIs for custom models. You will set up a cloud environment for deep learning-based web deployments on Google Cloud and AWS, and get guidance on how to use their battletested deep learning APIs. Further, you will use Microsoft's Intelligent Emotion API, which can detect human emotions from a picture of a face. You will also get to grips with deploying real-world websites, and you will get great insights into securing those websites using reCaptcha and Cloudflare for a robust experience. Finally, you will use natural language processing to recommend restaurants from user reviews and to integrate a voice UX on your web pages through Dialogflow.

By the end of this book, you'll be able to deploy your intelligent web apps and websites with the help of the best tools and practices.

작가정보

저자(글) Anubhav Singh

Anubhav Singh, a web developer since before Bootstrap was launched, is an explorer of technologies, often pulling off crazy combinations of uncommon tech. An international rank holder in the Cyber Olympiad, he started off by developing his own social network and search engine as his first projects at the age of 15, which stood among the top 500 websites of India during their operational years. He's continuously developing software for the community in domains with roads less walked on. You can often catch him guiding students on how to approach ML or the web, or both together. He's also the founder of The Code Foundation, an AI-focused start-up. Anubhav is a Venkat Panchapakesan Memorial Scholarship awardee and an Intel Software Innovator.

저자(글) Sayak Paul

Sayak Paul is currently with PyImageSearch, where he applies deep learning to solve real-world problems in computer vision and bring solutions to edge devices. He is responsible for providing Q&A support to PyImageSearch readers. His areas of interest include computer vision, generative modeling, and more. Previously at DataCamp, Sayak developed projects and practice pools. Prior to DataCamp, Sayak worked at TCS Research and Innovation (TRDDC) on data privacy. There, he was a part of TCS's critically acclaimed GDPR solution called Crystal Ball. Outside of work, Sayak loves to write technical articles and speak at developer meetups and conferences.