Hands-On Deep Learning for IoT
暫譯: 物聯網深度學習實戰

Karim, MD Rezaul, Razzaque, Mohammad Abdur

商品描述

earn
  • Get acquainted with different neural network architectures and their suitability in IoT
  • Understand how deep learning can improve the predictive power in your IoT solutions
  • Capture and process streaming data for predictive maintenance
  • Select optimal frameworks for image recognition and indoor localization
  • Analyze voice data for speech recognition in IoT applications
  • Develop deep learning-based IoT solutions for healthcare
  • Enhance security in your IoT solutions
  • Visualize analyzed data to uncover insights and perform accurate predictions
About

Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale.

Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT.

You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN).

You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced.

By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making.

Features
  • Understand how deep learning facilitates fast and accurate analytics in IoT
  • Build intelligent voice and speech recognition apps in TensorFlow and Chainer
  • Analyze IoT data for making automated decisions and efficient predictions

商品描述(中文翻譯)



學習目標


  • 熟悉不同的神經網絡架構及其在物聯網(IoT)中的適用性

  • 了解深度學習如何提升您物聯網解決方案的預測能力

  • 捕捉並處理流式數據以進行預測性維護

  • 選擇最佳框架進行圖像識別和室內定位

  • 分析語音數據以實現物聯網應用中的語音識別

  • 開發基於深度學習的物聯網解決方案以應用於醫療保健

  • 增強您物聯網解決方案的安全性

  • 可視化分析數據以發現洞察並進行準確預測





關於本書

人工智慧正在快速增長,這是由於神經網絡(NN)和深度學習(DL)的進步。隨著對智慧城市、智慧醫療和工業物聯網(IoT)的投資增加,物聯網的商業化將很快達到高峰,屆時需要大規模處理由物聯網設備生成的大量數據。

《物聯網的實作深度學習》將提供對物聯網數據的深入見解,首先介紹深度學習如何融入使物聯網應用更智能的背景。接著涵蓋如何使用 TensorFlow、Keras 和 Chainer 為物聯網構建深度架構。

您將學習如何訓練卷積神經網絡(CNN)來開發基於圖像的道路故障檢測和智慧垃圾分類應用,然後實現由遞歸神經網絡(RNN)驅動的語音啟動智慧燈光控制和家庭進入機制。

您將掌握用於室內定位、預測性維護和在大型醫院中定位設備的物聯網應用,使用自編碼器、DeepFi 和 LSTM 網絡。此外,您還將學習增強物聯網安全的醫療保健應用開發。

在本書結束時,您將擁有足夠的知識,以有效地使用深度學習來驅動您的物聯網應用,以便做出更智能的決策。





特色


  • 了解深度學習如何促進物聯網中的快速和準確分析

  • 在 TensorFlow 和 Chainer 中構建智能語音和語音識別應用

  • 分析物聯網數據以進行自動化決策和高效預測




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