Hands-on TinyML: Harness the power of Machine Learning on the edge devices

Book title
Inner title
Copyright
Dedicated
About the Author
About the Reviewers
Acknowledgements
Preface
Code Bundle and Coloured Images
Piracy
Table of Contents
Chapter 1: Introduction to TinyML and its Applications
	Introduction
	Structure
	Objectives
	Brief overview of Machine Learning
		Supervised Machine Learning
		Unsupervised Machine Learning
	Machine Learning and Deep Learning
	Edge computing and TinyML
	Applications of TinyML
	Hardware for deploying TinyML
	Software for TinyML
	Process flow of creating TinyML applications
	Prerequisites—hardware and software
	Conclusion
	Key facts
Chapter 2: Crash Course on Python and TensorFlow Basics
	Introduction
	Structure
	Objectives
	Colab Notebook
	Python variables
		Python strings
		Lists
		Tuple
		Dictionary
	Conditional and logical operations
	Loops in Python
	Functions in Python
	Python libraries
		NumPy library
		Matplotlib library
		Pandas library
	Introduction to TensorFlow
		Tensors and datatypes
		Differentiation in TensorFlow
		Graphs and functions in TensorFlow
		End-to-end Machine Learning algorithm using TensorFlow
	Conclusion
	Key facts
	Further reading
Chapter 3: 
Gearing with Deep Learning
	Introduction
	Structure
	Objectives
	Theory of artificial neural networks
		Binary cross entropy loss function
		Neural network activation functions
		Learning the neural network weights—the backpropagation algorithm
	Introduction to Convolutional Neural Network
		Architecture of a CNN
		Putting them all together
	Neural network hyperparameters
		Number of layers
		Learning rate
		Dropout
		Regularization
		Choice of optimization algorithm
		Mini-batch size
	Conclusion
	Key facts
	Further reading
Chapter 4: Experiencing TensorFlow
	Introduction
	Structure
	Objectives
	Keras and TensorFlow
	Classification of handwritten digits using a feedforward neural network
		Data processing
		Model implementation
	Implementation of a Convolutional Neural Network
	Evaluation metrics in classification models
	Conclusion
	Key facts
Chapter 5: Model Optimization Using TensorFlow
	Introduction
	Structure
	Objectives
	Experiencing TensorFlow Lite
	TensorFlow Model Optimization Toolkit
		Quantization
		Weight pruning
		Weight clustering
	Collaborative optimization
	Conclusion
	Key facts
Chapter 6: Deploying My First TinyML Application
	Introduction
	Structure
	Objectives
	The MobileNet architecture
		Depthwise separable convolution
	Image classification using MobileNet
		Brief introduction to transfer learning
		Implementing MobileNet using transfer learning
		Creating an optimized model for a smaller target device
		Evaluation of the model on the test set
	Introduction to Raspberry Pi
	Getting started with the Pi
		Installing the operating system
		Setting up the Pi
		Remotely accessing the Pi
	Deploying the model on Raspberry Pi to make inference
	Conclusion
	Key facts
Chapter 7: Deep Dive into Application Deploymen
t
	Introduction
	Structure
	Objectives
	System requirement
	The face recognition pipeline
	Setting up the Raspberry Pi for face recognition
		The Raspberry Pi camera module
		Installing the necessary libraries
	Implementation of the project
		Data collection for training
		Model training
		Real-time face recognition
	Conclusion
	Key facts
Chapter 8: TensorFlow Lite for Microcontrollers
	Introduction
	Structure
	Objectives
	Arduino Nano 33 BLE Sense
		Setting up the Arduino Nano
	First TinyML project on the microcontroller—modulating the potentiometer
		Required components
		Connecting the circuit
		Read potentiometer to control the brightness of the LED
		Creating a TensorFlow model to modulate the potentiometer reading
		Inference on Arduino Nano using TensorFlow Lite for Microcontrollers
	Conclusion
	Key facts
Chapter 9: Keyword Spotting on Microcontrollers
	Introduction
	Structure
	Objectives
	Working principles of a voice assistant
	Implementation of a keyword spotting algorithm in Python
		Audio spectrogram
		Designing a Convolutional Neural Network model for keyword spotting
	Introduction to Edge Impulse
	Implementing keyword spotting in Edge Impulse
	Model deployment
	Conclusion
	Key facts
Chapter 10: Conclusion and Further Reading
	Introduction
	Structure
	Objectives
	Brief learning summary
	TinyML best practices
	AutoML and TinyML
	Edge ML on smartphones
	Future of TinyML
	Further reading
Appendix
Index
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