Machine Learning with the Raspberry Pi - Experiments with Data and Computer Vision
| Verlag | Springer |
| Auflage | 2019 |
| Seiten | 568 |
| Format | 15,5 x 4 x 23,6 cm |
| Gewicht | 867 g |
| Artikeltyp | Englisches Buch |
| ISBN-10 | 1484251733 |
| EAN | 9781484251737 |
| Bestell-Nr | 48425173UA |
Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most complex topic in the hobbyist world of computing understandable and approachable.
Machine learning, also commonly referred to as deep learning (DL), is currently being integrated into a multitude of commercial products as well as widely being used in industrial, medical, and military applications. It is hard to find any modern human activity, which has not been "touched" by artificial intelligence (AI) applications. Building on the concepts first presented in Beginning Artificial Intelligence with the Raspberry Pi, you'll go beyond simply understanding the concepts of AI into working with real machine learning experiments and applying practical deep learning concepts to experiments with the Pi board and computer vision.
What you learn with Machine Learning with the Raspberry Pi can then be moved on to other platforms to go even further in the world of AI and ML to better your hobbyist or commercial projects.
What You'll Learn
Acquire a working knowledge of current ML
Use the Raspberry Pi to implement ML techniques and algorithms
Apply AI and ML tools and techniques to your own work projects and studies
Who This Book Is For
Engineers and scientists but also experienced makers and hobbyists. Motivated high school students who desire to learn about ML can benefit from this material with determination.
Inhaltsverzeichnis:
Chapter 1: Introduction to Machine Learning (ML) with the Raspberry Pi (RasPi)
Chapter 2: Exploration of ML data models: Part 1
Chapter 3: Exploration of ML data models: Part 2
Chapter 4: Preparation for Deep Learning
Chapter 5: Practical deep learning ANN demonstrations
Chapter 6: CNN demonstrations
Chapter 7: Predictions using ANNs and CNNs
Chapter 8: Predictions using CNNs and MLPs for medical research
Chapter 9: Reinforcement Learning.