Hands-On Python Deep Learning for the Web: Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow (實作Python深度學習於網頁：整合神經網絡架構以使用Flask、Django和TensorFlow建構智慧網頁應用程式)

Singh, Anubhav, Paul, Sayak

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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商品描述

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.

商品描述(中文翻譯)

主要特點


  • 使用Python庫（如Flask和Django）創建下一代智能網絡應用程式

  • 實施深度學習算法和技術，以進行智能網絡自動化

  • 集成神經網絡架構，創建功能強大的全棧網絡應用程式

書籍描述

有效使用深度學習技術可以幫助您開發智能網絡應用程式。在本書中，您將學習使用Python在網絡開發中實施深度學習的最新工具和技術實踐。

從機器學習的基礎知識開始，您將專注於深度學習和神經網絡的基礎知識，包括常見的變體，如卷積神經網絡（CNN）。您將學習如何將它們與不同標準網絡技術堆棧的前端集成到網站中。然後，本書通過使用Python庫（如Django和Flask）為自定義模型創建RESTful API，幫助您獲得開發深度學習啟用的網絡應用程式的實踐經驗。隨後，您將探索如何在Google Cloud和Amazon Web Services（AWS）上設置基於深度學習的網絡部署的雲環境。接下來，您將學習如何使用Microsoft的智能情感API，該API可以通過一張人臉照片檢測出一個人的情感。您還將學習如何部署真實世界的網站，以及如何使用reCAPTCHA和Cloudflare保護網站。最後，您將使用自然語言處理（NLP）在網頁上通過Dialogflow集成語音UX。

通過閱讀本書，您將學習如何使用有效的工具和實踐方法部署智能網絡應用程式和網站。

您將學到什麼


  • 探索深度學習模型並在瀏覽器中實施它們

  • 使用Django和Flask設計智能的基於網絡的客戶端

  • 使用不同的基於Python的API執行深度學習任務

  • 使用TensorFlow.js實現流行的神經網絡模型

  • 使用深度學習在雲上設計和構建深度網絡服務

  • 熟悉將深度學習模型投入生產的標準工作流程

本書適合對象

本書適合數據科學家、機器學習從業者和深度學習工程師，他們希望在網絡上執行深度學習技術和方法。如果您是一名網絡開發人員，希望在瀏覽器中實施智能技術以使其更具互動性，則本書也對您有用。具備Python編程語言和基本機器學習技術的工作知識將是有益的。

作者簡介

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.

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.

作者簡介(中文翻譯)

Anubhav Singh是一位網頁開發者，早在Bootstrap推出之前就開始從事這個行業。他是一位技術探索者，經常嘗試結合不同的技術來進行創新。他在Cyber Olympiad中獲得國際排名，15歲時開始開發自己的社交網絡和搜索引擎作為他的第一個項目，這些項目在運營期間位居印度前500名網站之列。他一直在為社區開發軟件，涉足一些不太常見的領域。你經常可以看到他指導學生如何學習機器學習或網頁開發，或者兩者兼而有之。他還是The Code Foundation的創始人，這是一家專注於人工智能的初創公司。Anubhav是Venkat Panchapakesan紀念獎學金的獲獎者，也是Intel軟件創新者。

Sayak Paul目前在PyImageSearch工作，他將深度學習應用於解決計算機視覺中的實際問題，並將解決方案應用於邊緣設備。他負責為PyImageSearch讀者提供問答支持。他的興趣領域包括計算機視覺、生成建模等。在DataCamp之前，Sayak在TCS Research and Innovation (TRDDC)從事數據隱私方面的工作。在那裡，他是TCS廣受好評的GDPR解決方案Crystal Ball的一部分。在工作之外，Sayak喜歡撰寫技術文章並在開發者聚會和會議上演講。

目錄大綱

  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

目錄大綱(中文翻譯)

- 解密人工智慧和機器學習基礎
- 使用Python入門深度學習
- 建立你的第一個深度學習網頁應用程式
- 使用TensorFlow.js入門深度學習
- 透過API進行深度學習
- 使用Python在Google Cloud Platform上進行深度學習
- 使用Python在AWS上進行深度學習：物件偵測和家庭自動化
- 使用Python在Microsoft Azure上進行深度學習
- 一個用於深度學習網站的通用生產框架
- 使用深度學習保護網頁應用程式
- 自製 - 一個網頁深度學習生產環境
- 使用深度學習API和客戶支援聊天機器人創建端到端網頁應用程式
- 附錄：深度學習在網頁上的成功案例和新興領域

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