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TinyML Cookbook

TinyML Cookbook

By : Gian Marco Iodice
4.9 (11)
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TinyML Cookbook

TinyML Cookbook

4.9 (11)
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)
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Designing and training the ML model

With the dataset in our hands, we can start designing the model.

In this recipe, we will develop the following architecture with Edge Impulse:

Figure 6.17 – Fully connected neural network to train

As you can see, the spectral features are the input for the model, which consists of just two fully connected layers.

Getting ready

In this recipe, we want to explain why the tiny network shown in the preceding diagram recognizes gestures from accelerometer data.

When developing deep neural network architectures, we commonly feed the model with raw data to leave the network to learn how to extract the features automatically.

This approach proved to be effective and incredibly accurate in various applications, such as image classification. However, there are some applications where hand-crafted engineering features offer similar accuracy results to deep learning and help reduce the architecture's complexity...

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