Machine Learning with Python
For PC, Raspberry Pi, and MaixDuino
By Dr. Gunter Spanner
Table of Contents
- Chapter 1: Introduction
- Chapter 2: A Brief History of ML and AI
- Chapter 3: Learning from "Big Data"
- Chapter 4: The Hardware Base
- Chapter 5: The PC as Universal AI Machine
- Chapter 6: The Raspberry Pi
- Chapter 7: Sipeed Maix, aka "MaixDuino"
- Chapter 8: Programming and Development Environments
- Chapter 9: Python in a Nutshell
- Chapter 10: Useful Assistants: Libraries!
- Chapter 11: Practical Machine Learning Applications
- Chapter 12: Recognition of Handwritten Numbers
- Chapter 13: How Machines Learn to See: Object Recognition
- Chapter 14: Machines Learn to Listen and Speak
- Chapter 15: Facial Recognition and Identification
- Chapter 16: Train Your Own Models
- Chapter 17: Dreams of the Future: from KPU to Neuromorphic Chips
- Chapter 18: Electronic Components
- Chapter 19: Troubleshooting
- Chapter 20: Buyers Guide
- Chapter 21: References; Bibliography
Chapter 1: Introduction
This chapter introduces the fundamental concepts of machine learning and artificial intelligence, exploring what "super intelligence" might entail and how machines learn.
Chapter 2: A Brief History of ML and AI
A historical overview of the development of machine learning and artificial intelligence.
Chapter 3: Learning from "Big Data"
Explores the role of "Big Data" in machine learning and artificial intelligence.
Chapter 4: The Hardware Base
Discusses the essential hardware components required for machine learning projects.
Chapter 5: The PC as Universal AI Machine
Details how a personal computer can serve as a central hub for AI development and execution.
5.1 The computer as a programming center
Chapter 6: The Raspberry Pi
Focuses on using the Raspberry Pi for machine learning tasks.
- 6.1 The Remote Desktop
- 6.2 Using smartphones and tablets as displays
- 6.3 FileZilla
- 6.4 Pimp my Pi
Chapter 7: Sipeed Maix, aka "MaixDuino"
An in-depth look at the MaixDuino board for machine learning applications.
- 7.1 Small but mighty: the performance figures of the MaixDuino
- 7.2 A wealth of applications
- 7.3 Initial start-up and functional test
- 7.4 Power supply and stand-alone operation
Chapter 8: Programming and Development Environments
Covers various programming and development environments suitable for machine learning.
- 8.1 Thonny -- a Python IDE for beginners and intermediates
- 8.2 Thonny as a universal IDE for RPi and MaixDuino
- 8.3 Working with files
- 8.4 Thonny on the Raspberry Pi
- 8.5 Tips for troubleshooting the Thonny IDE
- 8.6 The MaixPy IDE
- 8.7 A MicroPython interpreter for MaixDuino
- 8.8 The Flash tool in action
- 8.9 Machine Learning and interactive Python
- 8.10 Anaconda
- 8.11 Jupyter
- 8.12 Installation and Start-Up
- 8.13 Using MicroPython Kernels in Jupyter
- 8.14 Communication setup to the MaixDuino
- 8.15 Kernels
- 8.16 Working with Notebooks
- 8.17 All libraries available?
- 8.18 Using Spyder for Python Programming
- 8.19 Who's programming who?
Chapter 9: Python in a Nutshell
A concise guide to Python programming relevant to machine learning.
- 9.1 Comments make your life easier
- 9.2 The print() statement
- 9.3 Output to the display
- 9.4 Indentations and Blocks
- 9.5 Time Control and Sleep
- 9.6 Hardware under control: digital inputs and outputs
- 9.7 For vital values: variables and constants
- 9.8 Numbers and variable types
- 9.9 Converting number types
- 9.10 Arrays as a basis for neural networks
- 9.11 Operators
- 9.12 Conditions, branches and loops
- 9.13 Trial and error -- try and except
Chapter 10: Useful Assistants: Libraries!
An overview of essential Python libraries for machine learning.
- 10.1 MatPlotLib as a graphics artist
- 10.2 The math genius: Numpy
- 10.3 Data-mining using Pandas
- 10.4 Learning and visualization using Scikit, Scipy, SkImage & Co.
- 10.5 Machine Vision using OpenCV
- 10.6 Brainiacs: KERAS and TensorFlow
- 10.7 Knowledge transfer: sharing the learning achievements
- 10.8 Graphical representation of network structures
- 10.9 Solution of the XOR problem using KERAS
- 10.10 Virtual environments
Chapter 11: Practical Machine Learning Applications
Demonstrates practical applications of machine learning.
- 11.1 Transfer functions and multilayer networks
- 11.2 Flowers and data
- 11.3 Graphical representations of data sets
- 11.4 A net for iris flowers
- 11.5 Training and testing
- 11.6 What's blossoming here?
- 11.7 Test and learning behavior
Chapter 12: Recognition of Handwritten Numbers
Focuses on the recognition of handwritten numbers using machine learning.
- 12.1 "Hello ML" -- the MNIST data set
- 12.2 A neural network reads digits
- 12.3 Training, tests and predictions
- 12.4 Live recognition of digits
- 12.5 KERAS can do even better!
- 12.6 Convolutional networks
- 12.7 Power training
- 12.8 Quality control -- an absolute must!
- 12.9 Recognizing live images
- 12.10 Batch sizes and epochs
- 12.11 MaixDuino also reads digits
Chapter 13: How Machines Learn to See: Object Recognition
Explores how machines can learn to recognize objects.
- 13.1 TensorFlow for Raspberry Pi
- 13.2 Virtual environments in action
- 13.3 Using a Universal TFlite Model
- 13.4 Ideal for sloths: clothes-sorting
- 13.5 Construction and training of the model
- 13.6 MaixDuino recognizes 20 objects
- 13.7 Recognizing, counting and sorting objects
Chapter 14: Machines Learn to Listen and Speak
Covers machine learning applications in speech recognition and synthesis.
- 14.1 Talk to me!
- 14.2 RPi Learns to talk
- 14.3 Talking instruments
- 14.4 Sorry, didn't get you
- 14.5 RPi as a ChatBot
- 14.6 From ELIZA to ChatterBots
- 14.7 The Talking Eye
- 14.8 An AI Bat
Chapter 15: Facial Recognition and Identification
Details the process and implications of facial recognition and identification.
- 15.1 The right to your own image
- 15.2 Machines recognize people and faces
- 15.3 MaixDuino as a Door Viewer
- 15.4 How many guest were at the party?
- 15.5 Person-detection alarm
- 15.6 Social minefields? -- face identification
- 15.7 Big Brother RPi: face identification in practice
- 15.8 Smile, please ;-)
- 15.9 Photo Training
- 15.10 "Know thyself!" -- and others
- 15.11 A Biometric scanner as a door opener
- 15.12 Recognizing gender and age
Chapter 16: Train Your Own Models
Guides users on how to train their own machine learning models.
- 16.1 Creation of a model for the MaixDuino
- 16.2 Electronic parts recognition with the MaixDuino
- 16.3 Performance of the trained network
- 16.4 Field test
- 16.5 Outlook: Multi-object detectors
Chapter 17: Dreams of the Future: from KPU to Neuromorphic Chips
Explores future trends in machine learning, including KPU and neuromorphic chips.
Chapter 18: Electronic Components
A breakdown of essential electronic components used in machine learning projects.
- 18.1 Breadboards
- 18.2 Wires and jumpers
- 18.3 Resistors
- 18.4 Light-emitting diodes (LEDs)
- 18.5 Transistors
- 18.6 Sensors
- 18.7 Ultrasound range finder
Chapter 19: Troubleshooting
Provides guidance on troubleshooting common issues in machine learning projects.
Chapter 20: Buyers Guide
A guide to purchasing necessary hardware and software.
Chapter 21: References; Bibliography
Lists references and bibliography for further reading.
Index
An index for quick reference to topics covered in the book.
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