Hands-On Python Deep Learning for the Web

Book Description

Use the power of deep learning with Python to build and deploy intelligent web applications

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

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

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.

Table of Contents

1. Demystifying Artificial Intelligence and Fundamentals of Machine Learning
2. Getting Started with Deep Learning Using Python
3. Creating Your First Deep Learning Web Application
4. Getting Started with TensorFlow.js
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
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
13. Appendix: Success Stories and Emerging Areas in Deep Learning on the Web

中文：

书名：面向Web的实践性Python深度学习

借助Python的深度学习能力构建和部署智能Web应用程序

主要特点

• 使用Flask库和Django库创建下一代智能Web应用程序
• 实施深度学习算法和技术以执行智能网络自动化
• 集成神经网络架构以创建功能强大的全栈Web应用程序

图书描述

如果有效地使用，深度学习技术可以帮助你开发智能网络应用程序。在这本书中，你将介绍最新的工具和技术实践，这些工具和技术实践被用来在使用Python的网络开发中实现深度学习。

从机器学习的基础开始，您将重点学习动态链接库和神经网络的基础，包括卷积神经网络(CNN)等常见变体。您将学习如何将它们集成到具有不同标准网络技术堆栈前端的网站中。然后，这本书通过为定制模型创建REST风格的API，帮助您获得使用Django和Flask等Python库开发支持深度学习的Web应用程序的实践经验。稍后，您将探索如何在Google Cloud和Amazon Web Services(AWS)上为基于深度学习的Web部署设置云环境。接下来，你将学习如何使用微软的智能情绪API，它可以通过一个人的脸部照片来检测他的情绪。除了学习如何使用reCAPTCHA和Cloudflare保护网站外，您还将掌握如何部署真实的网站。最后，您将使用NLP在您的网页上通过Dialogflow集成语音UX。

到本书结束时，您将学会如何在有效工具和实践的帮助下部署智能Web应用程序和网站。

What you will learn

• Explore deep learning models and implement them in your browser
• 使用Django和Flask语言设计基于Web的智能客户端
• 使用不同的基于Python的API来执行深度学习任务
• 用TensorFlow.js实现流行的神经网络模型
• 使用深度学习在云上设计和构建深度Web服务
• 熟悉将深度学习模型应用于生产的标准工作流

Who this book is for

这本深度学习书是为希望在网络上执行深度学习技术和方法的数据科学家、机器学习从业者和深度学习工程师编写的。如果您是一名Web开发人员，希望在浏览器中实现智能技术以使其更具互动性，那么您也会发现这本书很有用。具备Python编程语言的应用知识和基本的机器学习技术将是有益的。

Table of Contents

1. Demystifying Artificial Intelligence and Fundamentals of Machine Learning
2. Getting Started with Deep Learning Using Python
3. 创建您的第一个深度学习Web应用程序
4. TensorFlow.js入门
5. 通过API实现深度学习
6. 深度学习在Google Cloud平台上的实现
7. 使用Python的AWS上的数字图书馆：对象检测和家庭自动化
8. 使用Python在Microsoft Azure上进行深度学习
9. 支持深度学习的网站的通用生产框架
10. 使用深度学习保护Web应用程序
11. DIY&#8211；一个Web数字图书馆生产环境
12. 使用DLAPI和客户支持聊天机器人创建E2E Web应用程序
13. 附录：网上深度学习的成功案例和新兴领域