Book Image

TinyML Cookbook

By : Gian Marco Iodice
Book Image

TinyML Cookbook

By: Gian Marco Iodice

Overview of this book

This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you’ll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you’ll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you’ll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you’ll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.
Table of Contents (10 chapters)

Training the ML model with TF

The model designed for forecasting the snow is a binary classifier, and it is illustrated in the following diagram:

Figure 3.5 – Neural network model for forecasting the snow

The network consists of the following layers:

  • 1 x fully connected layers with 12 neurons and followed by a ReLU activation function
  • 1 x dropout layer with a 20% rate (0.2) to prevent overfitting
  • 1 x fully connected layer with one output neuron and followed by a sigmoid activation function

In this recipe, we will train the preceding model with TF.

The following Colab file (see the Training the ML model with TF section in the following repository) contains the code referred to in this recipe:

  • preparing_model.ipynb:

https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter03/ColabNotebooks/preparing_model.ipynb

Getting ready

The model designed in this recipe has one input and output node. The input...