Python 3: From None to Machine Learning

Original Title

Python 3: from None to Machine Learning

English Title

Python 3: from None to Machine Learning

Author

Matt Harasymczuk

Language

English

ISBN

978-83-957186-2-5

Year

2019-2020

Online Access

https://python.astrotech.io

License

Creative Commons Attribution-ShareAlike 4.0 International License

Author

author	Matt Harasymczuk
email	book-python@astronaut.center
www	https://www.astronaut.center
github	https://github.com/astromatt
linkedin	https://linkedin.com/in/mattharasymczuk
facebook	https://facebook.com/matt.harasymczuk
slideshare	https://www.slideshare.net/astrotech/presentations

Table 1. Other Books from Author. More information at https://www.astronaut.center/books

ISBN	Online Access	Title
9788395718625	https://python.astrotech.io	Python 3: from None to Machine Learning
9788395718632	https://dev.astrotech.io	The Software Engineering: DevOps, CI/CD, Docker, Provisioning and Git Flow
9788395718601	https://www.astronaut.pl	Astronaut Selection and Training for Long Duration Spaceflight and Extraterrestrial Activity
9788395675201	https://www.astronaut.center/books	Space in Practice: How to Prepare and Conduct Stratospheric Balloon Mission
9788395718649	https://pl.habitatos.space	HabitatOS - Development of operating system prototype for Lunar and Martian habitats
9788395718618	https://alsep.astronaut.center	Geophysics experiments from Apollo Lunar Surface Experiments Package

Note

• For consulting or training course requests please email book-python@astronaut.center

• Before training course please setup your environment as described here Install.

About

• License
• Book Writing Progress
• References in the Book

Python Basics

Python Basics

• 1. About
  • 1.1. Basic Survey
  • 1.2. Agenda
  • 1.3. Development Environment
  • 1.4. Installing Python
  • 1.5. Python Language
  • 1.6. Python Syntax
  • 1.7. Operators
• 2. Types
  • 2.1. Type Int
  • 2.2. Type Float
  • 2.3. Type Bool
  • 2.4. Type None
  • 2.5. Type Str
  • 2.6. Type Str Methods
  • 2.7. Type Annotation
• 3. Sequences
  • 3.1. Sequence List
  • 3.2. Sequence Tuple
  • 3.3. Sequence Set
  • 3.4. Sequence Frozenset
  • 3.5. Sequence Nested
  • 3.6. Sequence Get Item
  • 3.7. Sequence Slice
  • 3.8. Sequence Unpack
  • 3.9. Sequence Type Annotation
• 4. Mappings
  • 4.1. Mapping Dict
  • 4.2. Mapping Generate
  • 4.3. Mapping Nested
  • 4.4. Mapping Type Annotation
• 5. Control Flow
  • 5.1. Operators
  • 5.2. Membership
  • 5.3. Conditional Statements
  • 5.4. Conditional Expressions
  • 5.5. Exceptions
• 6. Loops
  • 6.1. Loop While
  • 6.2. Loop For
  • 6.3. Loop Nested Sequences
  • 6.4. Loop Unpacking Sequences
  • 6.5. Loop over Dict
  • 6.6. Comprehensions
• 7. Files
  • 7.1. File Path
  • 7.2. File Access Modes
  • 7.3. File Write
  • 7.4. File Append
  • 7.5. File Read
  • 7.6. File Encoding
  • 7.7. Files Binary
• 8. Functions
  • 8.1. Function Definition
  • 8.2. Function Return
  • 8.3. Function Parameters
  • 8.4. Function Arguments
  • 8.5. Function Scope
  • 8.6. Function Recurrence
  • 8.7. Function Doctest
  • 8.8. Function Generators
  • 8.9. Function Lambda
  • 8.10. Function Type Annotation
• 9. Object Oriented Programming
  • 9.1. Classes and Instances
  • 9.2. Attributes
  • 9.3. Initializer Method
  • 9.4. Methods
  • 9.5. Stringify Objects
  • 9.6. Inheritance
  • 9.7. OOP Type Annotation
• 10. Recap
  • 10.1. Summary
  • 10.2. Certificate
• 11. References
  • 11.1. Bibliography

Python Standard Library

Python Standard Library

• 1. Entry Test
  • 1.1. Pre-Intermediate
• 2. Modules and Packages
  • 2.1. Module venv
  • 2.2. Module Install
  • 2.3. Module Import
  • 2.4. Tools
  • 2.5. Standalone Applications
  • 2.6. Distributing Packages
• 3. Builtins
  • 3.1. Builtin Functions
  • 3.2. Builtin Keywords
  • 3.3. Operator Library
  • 3.4. Builtin Printing
• 4. Looping
  • 4.1. Comprehensions Nesting
  • 4.2. Loop else
• 5. Type Annotations
  • 5.1. Type Annotations
  • 5.2. Variables
  • 5.3. Sequences
  • 5.4. Mapping
  • 5.5. Special
  • 5.6. Classes
  • 5.7. Functions and Methods
  • 5.8. Type Aliases
  • 5.9. Introspection
  • 5.10. Type Checking
  • 5.11. Annotating Existing Code
• 6. Object Oriented Programming
  • 6.1. OOP Enum
  • 6.2. OOP Dataclass
• 7. Mathematics
  • 7.1. Math Stdlib
  • 7.2. Math Statistics
  • 7.3. Math Random
  • 7.4. Math Fractions
  • 7.5. Math Precision
  • 7.6. Math Plotting
  • 7.7. Math Complex Numbers
• 8. Datetime and Timezones
  • 8.1. Datetime Dates and Time
  • 8.2. Datetime Parsing and Formatting
  • 8.3. Datetime Time Deltas
  • 8.4. Datetime Timestamp
  • 8.5. Datetime Time Utils
  • 8.6. Datetime Timezones
• 9. Serialization
  • 9.1. Serialization CSV
  • 9.2. Serialization JSON
  • 9.3. Serialization Pickle
  • 9.4. Serialization XML, XSLT, XPath
• 10. Database
  • 10.1. Database API
  • 10.2. Database SQLite
  • 10.3. Database MySQL
  • 10.4. Database PostgreSQL
  • 10.5. Database MongoDB
  • 10.6. Database ORM
  • 10.7. Database Schema Migration
  • 10.8. Database Exploration
  • 10.9. Database SQL Injection
  • 10.10. Database SQL
• 11. Regular Expressions
  • 11.1. Regexp Syntax
  • 11.2. Regexp Match
  • 11.3. Regexp Search
  • 11.4. Regexp Findall, Finditer
  • 11.5. Regexp Compare
  • 11.6. Regexp Compile
  • 11.7. Regexp Groups
  • 11.8. Regexp Multiline
  • 11.9. Regexp Substitute
  • 11.10. Regexp Split
  • 11.11. Regexp Non-Greedy
  • 11.12. Regexp Standards
  • 11.13. References
• 12. Operating System
  • 12.1. Interpreter declaration
  • 12.2. Commandline Arguments
  • 12.3. Operating System
  • 12.4. pathlib
• 13. GUI
  • 13.1. GUI
• 14. Exit Test
  • 14.1. Stdlib Exam

Python Advanced

Python Advanced

• 1. Entry Test
  • 1.1. Survey
  • 1.2. Entry Exam
  • 1.3. Agenda
• 2. Functions
  • 2.1. Arbitrary Number of Parameters
  • 2.2. Arbitrary Number of Arguments
  • 2.3. Parameter Syntax
  • 2.4. Generators
  • 2.5. Callable
  • 2.6. Closures
• 3. Decorators
  • 3.1. About Decorators
  • 3.2. Function Decorator with Functions
  • 3.3. Function Decorator with Classes
  • 3.4. Function Decorator with Methods
  • 3.5. Class Decorator with Functions
  • 3.6. Class Decorator with Classes
  • 3.7. Class Decorator with Methods
  • 3.8. Decorator with Arguments
  • 3.9. Functools
• 4. Object Oriented Programming
  • 4.1. Attribute Access
  • 4.2. Operator Overload
  • 4.3. Static and Dynamic Fields
  • 4.4. Inheritance vs. Composition
  • 4.5. Inner Classes
  • 4.6. Method Resolution Order
  • 4.7. Relations
  • 4.8. Polymorphism
  • 4.9. Dynamic Typing
  • 4.10. Monkey Patching
  • 4.11. New and Old Style Classes
  • 4.12. Interfaces
  • 4.13. Abstract Classes
  • 4.14. Object Identity
  • 4.15. Object Initialization
  • 4.16. Slots
  • 4.17. Metaclass
  • 4.18. Good Engineering Practises
• 5. OOP Protocols
  • 5.1. Iterators
  • 5.2. Context Managers
  • 5.3. staticmethod
  • 5.4. classmethod
  • 5.5. Properties
  • 5.6. Reflection
  • 5.7. Descriptor
• 6. Performance
  • 6.1. Performance Optimization
  • 6.2. Micro-benchmarking
  • 6.3. Profiling
  • 6.4. Compilers and Interpreters
  • 6.5. Collections
  • 6.6. C Types
  • 6.7. C Modules
• 7. Concurrency
  • 7.1. Concurrency Models
  • 7.2. Queues
  • 7.3. Threading
  • 7.4. Multiprocessing
  • 7.5. Async Programming
• 8. Recap
  • 8.1. Recap
  • 8.2. Certificate
• 9. References
  • 9.1. Bibliography

Dragon

Dragon

• 1. Dragon (version alpha)
• 2. Dragon (version beta)
• 3. Dragon (version release candidate)

Design Patterns

Design Patterns

• 1. Entry Test
  • 1.1. Entry Exam
  • 1.2. Classification
• 2. Creational
  • 2.1. Singleton
  • 2.2. Factory
• 3. Structural
  • 3.1. Dependency injection
• 4. Behavioral
  • 4.1. Gateway
  • 4.2. State Machine
• 5. Paradigms
  • 5.1. Callback
  • 5.2. Functional Programming
  • 5.3. Event Programming
• 6. Exit Test
  • 6.1. Exit Exam

CI/CD and Quality

CI/CD and Quality

• 1. Software Engineering Conventions
  • 1.1. PEP 8 - Style Guide for Python Code
  • 1.2. Static Code Analysis
  • 1.3. Assignments
• 2. The Zen of Python
  • 2.1. Most important rules
  • 2.2. Full Text
• 3. Code Smells
  • 3.1. Bad practice
• 4. ReST and Sphinx documentation
  • 4.1. reStructuredText vs. Markdown
  • 4.2. Format reStructuredText
  • 4.3. Sphinx
  • 4.4. Read the docs
  • 4.5. Assignments
• 5. Unit Testing
  • 5.1. Glossary
  • 5.2. Running tests
  • 5.3. Examples
  • 5.4. Mock
  • 5.5. Stub
  • 5.6. Assignments
• 6. Pytest
  • 6.1. Rationale
  • 6.2. Running
  • 6.3. Skip
  • 6.4. pytest.raises
  • 6.5. Fixtures
• 7. Python WAT?!
• 8. Logging
  • 8.1. Logging levels
  • 8.2. Use Case
  • 8.3. Konfiguracja formatowania logów
  • 8.4. Rotate
  • 8.5. Further Reading
• 9. Warnings
  • 9.1. Usage
  • 9.2. Running
  • 9.3. Examples
• 10. Basic Debugging
  • 10.1. Run in the console
  • 10.2. print
  • 10.3. pprint
  • 10.4. locals()
  • 10.5. Using debugger in IDE
• 11. Advanced Debugging
  • 11.1. json.tool
  • 11.2. Using pdb
  • 11.3. breakpoint()
  • 11.4. code.interact()
  • 11.5. Using debugger in IDE
  • 11.6. Debugging i Wątki
  • 11.7. Debugging i Procesy
  • 11.8. Debugging aplikacji sieciowych
  • 11.9. Assignments
• 12. Introspection
  • 12.1. Introspecting Types
  • 12.2. Introspecting Objects
  • 12.3. Introspecting Docstrings
  • 12.4. Examples
• 13. Type Checking
  • 13.1. MyPy
  • 13.2. PyType
  • 13.3. pyre-check
• 14. Annotating existing code
  • 14.1. PyAnnotate
  • 14.2. monkeytype
• 15. Introspection
• 16. CI/CD
  • 16.1. CI/CD Tools
  • 16.2. CI/CD Pipelines
  • 16.3. Linters
  • 16.4. Behavioral Testing
  • 16.5. Code Style
  • 16.6. Code Coverage
  • 16.7. Internationalization
  • 16.8. DB Schema Migration
  • 16.9. Distributing
  • 16.10. Security
  • 16.11. Smoke Tests
  • 16.12. Static Code Analysis
  • 16.13. Test Automation
  • 16.14. Type Checking
  • 16.15. UI testing
  • 16.16. Mutation Testing

Network and HTTP

Network and HTTP

• 1. HTTP and Web
  • 1.1. HTTP Protocol
  • 1.2. HTTP using stdlib
  • 1.3. HTTP using requests
  • 1.4. HTML Scrapping
  • 1.5. atlassian-python-api
  • 1.6. Standard WSGI
  • 1.7. Standard ASGI
  • 1.8. django
  • 1.9. FastAPI
  • 1.10. flask
  • 1.11. webapp2
  • 1.12. tornado
  • 1.13. Templates with Jinja2
  • 1.14. PaaS - Platform as a Service
  • 1.15. SOAP and WSDL
  • 1.16. Asynchronous processing
• 2. Protocols
  • 2.1. FTP
  • 2.2. IMAP
  • 2.3. SMTP
  • 2.4. POP3
  • 2.5. LDAP
  • 2.6. SSH
  • 2.7. DNS
• 3. Transport
  • 3.1. OSI Model
  • 3.2. Interfaces
  • 3.3. Routing
  • 3.4. Socket
  • 3.5. SSL
  • 3.6. IPv4/IPv6
  • 3.7. TCP/UDP
  • 3.8. scapy
  • 3.9. 3rd Party Libraries
  • 3.10. Mini Botnet

Django

Django

• 1. HTTP
  • 1.1. Narzędzia
  • 1.2. DNS
  • 1.3. HTTP Protocol
  • 1.4. HTTP Methods
  • 1.5. HTML + JS + CSS
• 2. Introduction to Django
  • 2.1. Co to jest Django?
  • 2.2. Dokumentacja
  • 2.3. Wersjonowanie
  • 2.4. setup.cfg
  • 2.5. IDE Support
• 3. Django Framework Architecture
  • 3.1. Framework Architecture
  • 3.2. Request-Response flow
• 4. Installation and Running
  • 4.1. Instalacja
  • 4.2. Starting new project
  • 4.3. IDE Run configuration
• 5. Settings
• 6. Apps
  • 6.1. Reusability
  • 6.2. Location
  • 6.3. Create
  • 6.4. Structure
  • 6.5. Configuration
• 7. Models
  • 7.1. Model Fields
  • 7.2. Model field arguments
  • 7.3. Abstract Models
  • 7.4. Architecture
  • 7.5. Single File vs. Models per file
  • 7.6. Reverse engineering database
  • 7.7. Database schema migration
  • 7.8. Example Model
  • 7.9. Assignments
• 8. URL Router
  • 8.1. Global URLs
  • 8.2. In app URLs
  • 8.3. urlpatterns
  • 8.4. path
  • 8.5. url
  • 8.6. include
  • 8.7. URLs reversing
  • 8.8. URLs in templates
• 9. ORM
  • 9.1. QuerySet
  • 9.2. Field lookups
  • 9.3. Filters can reference fields on the model
  • 9.4. Complex lookups with Q objects
  • 9.5. Comparing objects
  • 9.6. Q() expressions
  • 9.7. F() expressions
  • 9.8. Aggregations
• 10. Views
  • 10.1. Class Based Views
  • 10.2. Generic Views
  • 10.3. Responses
  • 10.4. Permissions in Views
• 11. Forms
  • 11.1. Forms
  • 11.2. Model Forms
  • 11.3. Widgets
• 12. REST
  • 12.1. RESTful Views
  • 12.2. python -m json.tool
  • 12.3. django-rest-framework
  • 12.4. django-rest-swagger
• 13. Staticfiles
  • 13.1. Settings
  • 13.2. Serving staticfiles
• 14. Templates
• 15. Templatetags
  • 15.1. Filters
  • 15.2. Simple Tags
  • 15.3. Assignment tags
  • 15.4. Block tags
• 16. Admin panel
  • 16.1. Customizong
  • 16.2. Permissions
  • 16.3. Model Admin
  • 16.4. Model Inlines
  • 16.5. Extending Admin
  • 16.6. ModelAdmin Example
  • 16.7. django-import-export
  • 16.8. Grapelli
• 17. Management Commands
  • 17.1. Built-in
  • 17.2. Writing own management commands
• 18. i18n and l10n
  • 18.1. Timezone
  • 18.2. i18n - internationalization
• 19. Database
  • 19.1. Database schema migration
  • 19.2. Management commands
  • 19.3. Database settings
  • 19.4. Fixtures
  • 19.5. Multiple DB and db routing
• 20. Cache
• 21. Signals
  • 21.1. Available signals
  • 21.2. Examples
• 22. Auth and Permissions
  • 22.1. Basic Auth
  • 22.2. Require Permissions
• 23. API CORS
• 24. Standalone scripts
• 25. Tests and quality
  • 25.1. Tests
  • 25.2. Test URLs
  • 25.3. SonarQube
  • 25.4. Debug Toolbar
• 26. Tests
• 27. CI/CD - Continuous Integration and Deployment
  • 27.1. Bitbucket Pipelines
  • 27.2. Travis
• 28. Deployment
  • 28.1. Deployment platforms
  • 28.2. Packaging
  • 28.3. Staticfils serving

Numerical Analysis, Data Science

Numerical Analysis, Data Science

• 1. Introduction
  • 1.1. Description
  • 1.2. Program
  • 1.3. Agenda
  • 1.4. Survey
• 2. Jupyter
  • 2.1. Jupyter
• 3. Python
  • 3.1. Tools and libs
  • 3.2. Python Syntax
  • 3.3. Types
  • 3.4. Sequences
  • 3.5. Mappings
  • 3.6. Conditionals
  • 3.7. Looping
  • 3.8. Control Flow
  • 3.9. Functions
  • 3.10. Object Oriented Programming
  • 3.11. Packages
  • 3.12. Math
  • 3.13. Serialization
  • 3.14. Performance
• 4. Numpy
  • 4.1. Numpy
  • 4.2. Built-ins
  • 4.3. Data Import and Export
  • 4.4. Array Create
  • 4.5. Array Random
  • 4.6. Array Data Types
  • 4.7. Array Attributes
  • 4.8. Array Shape
  • 4.9. Array Indexing
  • 4.10. Array Slicing
  • 4.11. Array Iteration
  • 4.12. Array Rounding
  • 4.13. Array Axis
  • 4.14. Array Sort
  • 4.15. Array Methods
  • 4.16. Array Arithmetic
  • 4.17. Array Concatenation
  • 4.18. Array Serialize
  • 4.19. Array Statistics
  • 4.20. Array Logic
  • 4.21. Array Inf and NaN
  • 4.22. Array Select
  • 4.23. Statistics
  • 4.24. Trigonometry
  • 4.25. Polynomials
  • 4.26. Linear Algebra
• 5. Pandas
  • 5.1. Pandas
  • 5.2. Types Basics
  • 5.3. Types Advanced
  • 5.4. Series Create
  • 5.5. Series Attributes
  • 5.6. Series Index
  • 5.7. Series Indexing
  • 5.8. Series Slicing
  • 5.9. Series Update
  • 5.10. Series Alter
  • 5.11. Series Arithmetic
  • 5.12. Series Statistics
  • 5.13. Series Mapping
  • 5.14. Series Cleaning
  • 5.15. DataFrame Create
  • 5.16. DataFrame Import
  • 5.17. DataFrame Index
  • 5.18. DataFrame Attributes
  • 5.19. DataFrame Select
  • 5.20. DataFrame Indexing
  • 5.21. DataFrame At
  • 5.22. DataFrame Slicing
  • 5.23. DataFrame Loc
  • 5.24. DataFrame Alter
  • 5.25. DataFrame NaN
  • 5.26. DataFrame Sample
  • 5.27. DataFrame Plotting
  • 5.28. DataFrame Sort
  • 5.29. DataFrame Statistics
  • 5.30. DataFrame Mapping
  • 5.31. DataFrame Cleaning
  • 5.32. DataFrame Group By
  • 5.33. DataFrame Aggregations
  • 5.34. DataFrame Join
  • 5.35. DataFrame Export Data
  • 5.36. Date and Time Types
  • 5.37. Date and Time Timezones
  • 5.38. Date and Time Shifts
  • 5.39. Date and Time Frequency
  • 5.40. Date and Time Calendar
  • 5.41. Configuration
  • 5.42. Example Workflow
  • 5.43. References
• 6. Matplotlib
  • 6.1. Matplotlib
  • 6.2. Import/Export
  • 6.3. How to understand charts?
  • 6.4. Plot Styles
  • 6.5. Line Chart
  • 6.6. Bar Chart
  • 6.7. Box Chart
  • 6.8. Histogram
  • 6.9. Examples
  • 6.10. Scatter Plot
  • 6.11. Stacked Plot
  • 6.12. Pie Chart
  • 6.13. 3D Charts
  • 6.14. Advanced Styling
  • 6.15. Basic customizations
  • 6.16. Multiple figures and plots
  • 6.17. Logarithmic and other nonlinear axes
  • 6.18. Matplotlib Examples
  • 6.19. Matplotlib Plot Lifecycle
  • 6.20. Example COVID-19 plot
  • 6.21. Assignments
• 7. Data Visualization
  • 7.1. Plotly
  • 7.2. Bokeh
  • 7.3. Seaborn
  • 7.4. Generowanie grafów
• 8. Scipy
  • 8.1. Zastosowanie naukowe

Machine Learning

Machine Learning

• 1. Introduction
  • 1.1. What is Data?
  • 1.2. Basic Statistics
  • 1.3. Machine Learning Introduction
  • 1.4. Glossary PL
  • 1.5. Glossary
  • 1.6. Datasets
  • 1.7. Machine Learning Algorithms
  • 1.8. Features
  • 1.9. Classifiers
• 2. Sklearn
  • 2.1. scikit-learn
• 3. Model Quality
  • 3.1. Model Quality
  • 3.2. Principal Component Analysis
• 4. Decision Trees
  • 4.1. Decision Tree
• 5. Regressions
  • 5.1. Linear Regression
  • 5.2. Logistic Regression
• 6. K-Nearest Neighbors
  • 6.1. K Nearest Neighbors
• 7. Bayes
  • 7.1. Naive Bayes
• 8. Support Vector Machines
  • 8.1. Support Vector Machines
• 9. Clustering
  • 9.1. K-Means Clustering
• 10. Neural Networks
  • 10.1. Deep Neural Network
  • 10.2. Convolutional Neural Network
• 11. References
  • 11.1. References
  • 11.2. Notes

Blogposts

Blogposts

• 1. Wprowadzenie do Machine Learning w Pythonie

Appendixes

Appendixes

• 1. History
  • 1.1. BDFL - Guido van Rossum
  • 1.2. Name
  • 1.3. PSF - Python Software Foundation
• 2. Further reading
  • 2.1. My favourite sources
  • 2.2. Documentation
  • 2.3. Working with Excel
  • 2.4. IDE - Integrated Development Environment
  • 2.5. Others
  • 2.6. IT Fun
  • 2.7. Wyzwania i potyczki
• 3. Books
  • 3.1. Algorithms
  • 3.2. Databases
  • 3.3. Software Engineering Practises
  • 3.4. Design pattern
  • 3.5. Refactoring
  • 3.6. Python
• 4. Video
  • 4.1. Collections of videos
  • 4.2. Python
  • 4.3. Machine Learning and Data Science
  • 4.4. Filmy z ostatnich konferencji
• 5. Python 2 vs. 3
  • 5.1. six
  • 5.2. Lista kompatybilności Python 2 i Python 3
  • 5.3. Unicode i kodowanie znaków
  • 5.4. Biblioteki standardowe w 2 i 3
• 6. Python Intermediate
  • 6.1. Description
  • 6.2. Agenda
  • 6.3. Survey before workshop
  • 6.4. Certificate
• 7. Python in Networking
  • 7.1. Description
  • 7.2. Agenda
  • 7.3. Survey before workshop
  • 7.4. Certificate
• 8. Django Framework
  • 8.1. Description
  • 8.2. Agenda
  • 8.3. Survey before workshop
  • 8.4. Certificate
• 9. Python in Testing
  • 9.1. Description
  • 9.2. Agenda
  • 9.3. Survey before workshop
  • 9.4. Certificate
• 10. Python in Science and Engineering
  • 10.1. Description
  • 10.2. Agenda
  • 10.3. Survey before workshop
  • 10.4. Certificate
• 11. Python in Machine Learning
  • 11.1. Description
  • 11.2. Agenda
  • 11.3. Survey before workshop
  • 11.4. Certificate
• 12. References
  • 12.1. Bibliography
  • 12.2. Images
  • 12.3. Video