Donald J. Norris
Machine Learning with the Raspberry PiExperiments with Data and Computer Vision
Donald J. Norris
Barrington, NH, USA
Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the book’s product page, located at www.​apress.​com/​978-1-4842-5173-7 . For more detailed information, please visit http://​www.​apress.​com/​source-code .
ISBN 978-1-4842-5173-7e-ISBN 978-1-4842-5174-4
https://doi.org/10.1007/978-1-4842-5174-4
© Donald J. Norris 2020
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Table of Contents
Chapter 1:​ Introduction to machine learning (ML) with the Raspberry Pi (RasPi) 1
RasPi introduction 1
Writing the Raspbian Image to a micro SD card 4
Updating and upgrading the Raspbian distribution 15
ML facts 24
Chapter 2:​ Exploration of ML data models:​ Part 1 49
Installing OpenCV 4 49
Download OpenCV 4 source code 51
Building the OpenCV software 52
Seaborn data visualization library 59
Underlying big principle 77
Naive Bayes 91
k-nearest neighbor (k-NN) model 101
KNN demonstration 102
Decision tree classifier 106
Decision tree algorithm 107
Decision tree classifier demonstration with scikit-learn 122
Chapter 3:​ Exploration of ML data models:​ Part 2 135
Principal component analysis 136
PCA script discussion 137
Linear discriminant analysis 150
LDA script discussion 152
Support vector machines 162
SVM demonstration – Part 1 166
SVM demonstration – Part 2 169
Learning vector quantization 177
LVQ basic concepts 178
LVQ demonstration 180
Bagging and random forests 190
Introduction to bagging and random forest 190
Bootstrap resampling demonstration 193
Bagging demonstration 195
Random forest demonstration 204
Chapter 4:​ Preparation for deep learning 213
DL basics 213
Machine learning from data patterns 214
Loss functions 221
Optimizer algorithm 225
Artificial neural network 235
How ANNs are trained and function 238
Practical ANN weight modification example 258
ANN Python demonstration – Part 1 262
ANN Python demonstration – Part 2 267
Chapter 5:​ Practical deep learning ANN demonstrations 279
Parts list 280
Recognizing handwritten number demonstration 280
Project history and preparatory details 284
Adjusting the input datasets 293
Interpreting ANN output data values 295
Creating an ANN that does handwritten number recognition 297
Initial ANN training script demonstration 299
ANN test script demonstration 301
ANN test script demonstration using the full training dataset 309
Recognizing your own handwritten numbers 313
Handwritten number recognition using Keras 324
Introduction to Keras 324
Installing Keras 325
Downloading the dataset and creating a model 326
Chapter 6:​ CNN demonstrations 335
Parts list 335
Introduction to the CNN model 336
History and evolution of the CNN 342
Fashion MNIST demonstration 358
More complex Fashion MNIST demonstration 369
VGG Fashion MNIST demonstration 373
Jason’s Fashion MNIST demonstration 378
Chapter 7:​ Predictions using ANNs and CNNs 387
Pima Indian Diabetes demonstration 388
Background for the Pima Indian Diabetes study 388
Preparing the data 389
Using the scikit-learn library with Keras 408
Grid search with Keras and scikit-learn 411
Housing price regression predictor demonstration 416
Preprocessing the data 417
The baseline model 421
Improved baseline model 425
Another improved baseline model 428
Predictions using CNNs 431
Univariate time series CNN model 433
Chapter 8:​ Predictions using CNNs and MLPs for medical research 453
Parts list 454
Downloading the breast cancer histology Image dataset 455
Preparing the project environment 459
Using a MLP model for breast cancer prediction 491
Chapter 9:​ Reinforcement learning 501
Markov decision process 503
Discounted future reward 505
Q-learning 506
Q-learning and neural networks 547
Index 555
About the Author and About the Technical Reviewer
About the Author
Donald J. Norris
is an avid electronics hobbyist and maker. He is also an electronics engineer with an advanced degree in Production Management. Don is retired from civilian government service with the US Navy, where he specialized in acoustics and digital signal processing. He also has more than a dozen years’ experience as a professional software developer using C, C#, C++, Python, and Java, and 5 years’ experience as a certified IT security consultant.
 
About the Technical Reviewer
Ahmed Fawzy Gad
A482214_1_En_BookFrontmatter_Figb_HTML.png
is a machine learning engineer who holds B.Sc. and M.Sc. in Information Technology. Ahmed is a teaching assistant and a researcher who is interested in machine/deep learning, computer vision, and Python. He is a machine learning technical reviewer and consultant helping others do their projects. Ahmed contributes written tutorials and articles to a number of blogs including Paperspace , Real Python , KDnuggets , Heartbeat , and Towards Data Science .
He welcomes you to connect with him through LinkedIn (linkedin.com/in/AhmedFGad ), Facebook (fb.com/AhmedFGadd ), and e-mail ([email protected] ).
 
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