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TinyML:TensorFlow Lite边缘计算(影印版)

TinyML:TensorFlow Lite边缘计算(影印版)

出版社:东南大学出版社出版时间:2020-06-01
开本: 其他 页数: 484
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TinyML:TensorFlow Lite边缘计算(影印版) 版权信息

  • ISBN:9787564188948
  • 条形码:9787564188948 ; 978-7-5641-8894-8
  • 装帧:一般胶版纸
  • 册数:暂无
  • 重量:暂无
  • 所属分类:>

TinyML:TensorFlow Lite边缘计算(影印版) 内容简介

深度学习网络正变得越来越小。谷歌助理(Google Assistant)团队可以在微控制器上运行只有14KB大小的模型来检测单词。这本实用的书将带你进入TinyML的世界,让深度学习和嵌入式系统结合在一起,用微小的设备创造出惊奇的事业。

TinyML:TensorFlow Lite边缘计算(影印版) 目录

Preface 1. Introduction Embedded Devices Changing Landscape 2. Getting Started Who Is This Book Aimed At? What Hardware Do You Need? What Software Do You Need? What Do We Hope You'll Learn? 3. Getting Up to Speed on Machine Learning What Machine Learning Actually Is The Deep Learning Workflow Decide on a Goal Collect a Dataset Design a Model Architecture Train the Model Convert the Model Run Inference Evaluate and Troubleshoot Wrapping Up 4. The "Hello World" of TinyML: Building and Training a Model What We're Building Our Machine Learning Toolchain Python and Jupyter Notebooks Google Colaboratory TensorFlow and Keras Building Our Model Importing Dependencies Generating Data Splitting the Data Defining a Basic Model Training Our Model Training Metrics Graphing the History Improving Our Model Testing Converting the Model for TensorFlow Lite Converting to a C File Wrapping Up 5. The "Hello World" of TinyMt: Builfling an Application Walking Through the Tests Including Dependencies Setting Up the Test Getting Ready to Log Data Mapping Our Model Creating an AllOpsResolver Defining a Tensor Arena Creating an Interpreter Inspecting the Input Tensor Running Inference on an Input Reading the Output Running the Tests Project File Structure Walking Through the Source Starting with main_functions.cc Handling Output with output_handler.cc Wrapping Up main_functions.cc Understanding main.cc Running Our Application Wrapping Up 6. The "Hello World" of TinyML: Deploying to Microcontrollers, What Exactly Is a Microcontroller? Arduino Handling Output on Arduino …… 7. Wake-Word Detection: Training a Model 9. Person Detection: Building an Application 10. Person Detection: Training a Model 11. Magic Wand: Building an Application 12. Magic Wand: Training a Model 13. TensorFlow Lite for Microcontrollers 14. Designing Your Own TinyML Applications 15. Optimizing Latency 16. Optimizing Energy Usage 17. Optimizing Model and Binary Size 18. Debugging 19. Porting Models from TensorFlow to TensorFlow Lite 20. Privacy, Security, and Deployment 21. Learning More A. Using and Generating an Arduino Library Zip B. Capturing Audio on Arduino Index
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