Master C++ with practicals in Arduino/CNN

Description

Embark on a journey into the world of Machine Learning, Deep Learning, C++, and Arduino with this comprehensive guide. This book is meticulously crafted to provide a robust understanding of the fundamental concepts and hands-on experience with practical implementation using LibTorch (the PyTorch C++ API) and C++.

The book begins with an introductory course on C++ and Arduino, designed for beginners and those looking to refresh their knowledge. This course covers everything from the basics of programming in C++ to the intricacies of working with Arduino, all taught from scratch. It provides a solid foundation for the subsequent modules.

What you will learn

The book is structured into nine distinct modules:

1. Introduction to C++ and Arduino - This module serves as an introductory course for beginners. It covers the basics of programming in C++, the use of Arduino IDE, and the fundamentals of Arduino programming.

2. Introduction to Machine Learning and Deep Learning - Acquire the basics of Machine Learning, Deep Learning, and Neural Networks.

3. Convolutional Neural Networks - Comprehend Convolutional Layers, Pooling, and Fully Connected Layers. Construct a CNN using PyTorch.

4. Practical Implementation with LibTorch - Gain knowledge about Data Loading, Preprocessing, Training a CNN Model, and Model Evaluation and Optimization.

5. Integration with Arduino - Delve into Arduino, On-device AI, Edge Computing, and the process of deploying a LibTorch Model on Arduino. Understand the potential of Arduino in facilitating real-time machine learning applications and how it can be used to implement and test machine learning models in a hardware environment.

6. Training and Testing the CNN - Understand the procedure of training and testing a Convolutional Neural Network (CNN) on a dataset.

7. Exporting the Trained Model in LibTorch and ONNX - Learn the method to export a trained LibTorch model and convert it into the Open Neural Network Exchange (ONNX) format.

8. Loading and Using the Model in C++ - Learn the technique to load the exported ONNX model in a C++ environment and use it for inference.

9. Optimizing C++ Code - Discover various strategies to optimize the C++ code for enhanced performance.

10. Advanced Topics - Learn about advanced CNN architectures and their implementation using LibTorch.

    
    

Table of Contents

1. Introduction to C++ and Arduino

2. Introduction to Machine Learning and Deep Learning

3. Convolutional Neural Networks

4. Practical Implementation with LibTorch

5. Integration with Arduino

6. Training and Testing the CNN

7. Exporting the Trained Model in LibTorch and ONNX

8. Loading and Using the Model in C++

9. Optimizing C++ Code

10. Advanced Topics

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