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

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 函式庫如 Flask 和 Django 創建下一代智能網頁應用程式
- 實現深度學習算法和技術以進行智能網頁自動化
- 整合神經網絡架構以創建強大的全棧網頁應用程式

#### 書籍描述

當有效使用時，深度學習技術可以幫助您開發智能網頁應用程式。在本書中，您將涵蓋最新的工具和技術實踐，這些工具和實踐正在用於使用 Python 在網頁開發中實現深度學習。

從機器學習的基本原理開始，您將專注於深度學習（DL）和神經網絡的基本概念，包括常見的變體，如卷積神經網絡（CNN）。您將學習如何將它們整合到不同標準網頁技術堆疊的前端網站中。接著，本書將幫助您獲得使用 Python 函式庫如 Django 和 Flask 開發深度學習啟用的網頁應用程式的實踐經驗，通過為自定義模型創建 RESTful API。之後，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頁部署的雲環境。接下來，您將學習如何使用微軟的智能情感 API，該 API 可以通過人臉圖片檢測一個人的情感。您還將學習如何部署實際的網站，並學習如何使用 reCAPTCHA 和 Cloudflare 來保護網站。最後，您將使用自然語言處理（NLP）通過 Dialogflow 在您的網頁上整合語音用戶體驗（UX）。

到本書結束時，您將學會如何利用有效的工具和實踐來部署智能網頁應用程式和網站。

#### 您將學到的內容

- 探索深度學習模型並在瀏覽器中實現它們
- 使用 Django 和 Flask 設計智能基於網頁的客戶端
- 使用不同的基於 Python 的 API 來執行深度學習任務
- 使用 TensorFlow.js 實現流行的神經網絡模型
- 使用深度學習在雲端設計和構建深度網路服務
- 熟悉將深度學習模型投入生產的標準工作流程

#### 本書適合誰

這本深度學習書籍適合數據科學家、機器學習從業者和深度學習工程師，他們希望在網頁上執行深度學習技術和方法。如果您是一名希望在瀏覽器中實現智能技術以使其更具互動性的網頁開發人員，您也會發現這本書很有用。具備 Python 程式語言的工作知識和基本的機器學習技術將是有益的。

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 是一位自 Bootstrap 發佈之前就開始從事網頁開發的開發者，他是一位技術探索者，經常將不常見的技術進行瘋狂的組合。他是國際網路奧林匹克競賽的排名持有者，15 歲時便開始開發自己的社交網路和搜尋引擎，這些項目在運營期間曾位列印度前 500 大網站之中。他持續為社群開發軟體，專注於那些較少人涉足的領域。你常常可以看到他指導學生如何接觸機器學習（ML）或網頁開發，甚至兩者結合。他也是以人工智慧為重點的初創公司 The Code Foundation 的創辦人。Anubhav 是 Venkat Panchapakesan 紀念獎學金的得主，也是 Intel 軟體創新者。

Sayak Paul 目前在 PyImageSearch 工作，專注於將深度學習應用於解決計算機視覺中的現實問題，並為邊緣設備提供解決方案。他負責為 PyImageSearch 的讀者提供問答支持。他的興趣領域包括計算機視覺、生成建模等。之前在 DataCamp 工作時，Sayak 開發了多個專案和練習池。在加入 DataCamp 之前，Sayak 在 TCS 研究與創新中心（TRDDC）從事數據隱私的工作。在那裡，他參與了 TCS 獲得廣泛好評的 GDPR 解決方案 Crystal Ball。工作之餘，Sayak 喜歡撰寫技術文章，並在開發者聚會和會議上發表演講。

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)


  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

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