Introduction to Python: With Applications in Optimization, Image and Video Processing, and Machine Learning

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Preface
Author Biographies
1. Introduction to Python
	1.1. What is the Python Programming Language
	1.2. The Python Programming Language
		1.2.1. Downloading Python
		1.2.2. The Python’s Integrated Development and Learning Environment
	1.3. Book Organization
	1.4. Algorithms
	1.5. Variables
		1.5.1. Types of Variables
		1.5.2. Variable Assignment
		1.5.3. Basic Operations
	1.6. Input and Output in Python
		1.6.1. Escape Sequences
	1.7. Programs in Python
	1.8. Comments in a Program
		1.8.1. Operations with Integer and Floating-point Numbers
	1.9. Functions in Python
	1.10. Modules and Libraries
	1.11. Operators
		1.11.1. Arithmetic Operators
		1.11.2. Relational Operators
		1.11.3. Logical Operators
		1.11.4. Assignment Operators
	1.12. Alphanumeric Variables
	1.13. Lists
	1.14. Dictionaries
	1.15. Tuples
	1.16. Examples
	1.17. Python Instructions for Chapter 1
	1.18. Conclusions
	1.19. Exercises
2. Conditionals and Loops
	2.1. Introduction
	2.2. Conditionals
	2.3. The Conditional if-else
	2.4. Nested Conditionals
	2.5. Exceptions and Errors
	2.6. Loops
	2.7. The while Loop
	2.8. The for Loop
		2.8.1. The Function range
	2.9. Nested Loops
	2.10. The Instruction break
	2.11. The Instruction continue
	2.12. Additional Examples
	2.13. Python Instructions for Chapter 2
	2.14. Conclusions
	2.15. Exercises
	2.16. Bibliography
3. Data Structures: Strings, Lists, Tuples, and Dictionaries
	3.1. Introduction
	3.2. Strings
		3.2.1. Concatenation or Sum and Multiplication of Strings
	3.3. Functions on Strings
		3.3.1. Length of a String
		3.3.2. Split of a String
	3.4. Immutability of Strings
		3.4.1. Functions on Strings
		3.4.2. Conditions and Loops
	3.5. Lists
		3.5.1. Operations on Lists
		3.5.2. Mutability
		3.5.3. Conversion from a List of Strings to a Single String
	3.6. Tuples
		3.6.1. Tuple Assignment
		3.6.2. Functions on Tuples
	3.7. Dictionaries
		3.7.1. Functions for Dictionaries
	3.8. Sets
		3.8.1. Sets by Comprehension
		3.8.2. Frozen Sets
	3.9. Python Instructions for Chapter 3
	3.10. Conclusions
	3.11. Exercises
4. Arrays
	4.1. Introduction
	4.2. Introduction to Arrays
	4.3. Vectors
		4.3.1. Access to Vectors
		4.3.2. Vectors by Comprehension
		4.3.3. The Instruction append
	4.4. Examples with Vectors in Python
		4.4.1. Vector Sorting
	4.5. Matrices
	4.6. Arrays in Python
		4.6.1. Array Generation by Indexing
		4.6.2. Array Generation by Comprehension
	4.7. Matrix Operations using Linear Algebra with numpy
		4.7.1. Sum, Difference, and Multiplication of Matrices
		4.7.2. Sum of Matrices
		4.7.3. Product of a Matrix by a Matrix
		4.7.4. Product of Matrices in Python
			4.7.4.1. Matrix Multiplication Using np.dot
			4.7.4.2. Matrix Multiplication Using np.matrix
	4.8. Special Matrices
		4.8.1. The Identity Matrix
		4.8.2. The Transpose Matrix
		4.8.3. Transpose by Comprehension
	4.9. Examples
	4.10. Arrays in Pandas
		4.10.1. Data Frames
		4.10.2. Generation of Data Frames
		4.10.3. Functions for Series and Data Frames
	4.11. Python Instructions for Chapter 4
	4.12. Conclusions
	4.13. Selected Bibliography
	4.14. Exercises
5. Functions
	5.1. Introduction
	5.2. Subprograms
	5.3. Functions in Python
	5.4. Recursion
	5.5. Anonymous Functions or lambda Functions
	5.6. Pass by Reference
	5.7. Local and Global Variables
	5.8. Keyword and Default Arguments
	5.9. Variable-length Arguments
	5.10. Additional Examples
	5.11. Python Instructions in Chapter 5
	5.12. Conclusions
	5.13. Exercises
6. Object-Oriented Programming
	6.1. Introduction
	6.2. The Object-Oriented Programming Paradigm
	6.3. Classes in Python
		6.3.1. Creation and Use of a Class
		6.3.2. Declaration and Use of Setters and Getters
		6.3.3. Static Methods
		6.3.4. Encapsulation
		6.3.5. Inheritance
		6.3.6. Overloading
	6.4. Example
	6.5. Python Instructions for Chapter 6
	6.6. Conclusions
	6.7. Exercises
	6.8. Selected Bibliography
7. Reading and Writing to Files
	7.1. Introduction
	7.2. Writing Data to a File
		7.2.1. Writing Alphanumeric Data to a File
		7.2.2. The Instruction with
	7.3. Writing Numerical Data to a File
	7.4. Data Reading from a File
		7.4.1. Reading Data from a File
		7.4.2. The Instruction readline
		7.4.3. The Instruction readlines
	7.5. Reading and Writing Data from and To Excel
	7.6. Reading and Writing Binary Files
	7.7. Python Instructions in Chapter 7
	7.8. Conclusions
	7.9. Exercises
8. Plotting in Python
	8.1. Introduction
	8.2. Plots in Two Dimensions
		8.2.1. Color and Marker Options
	8.3. The Package seaborn
	8.4. Other Two-dimensional Plots
		8.4.1. Polar Plots
	8.5. Pie Charts
		8.5.1. Histogram Plots
		8.5.2. Stem Plots
		8.5.3. Scatter Plots
	8.6. Multiple Figures
		8.6.1. Plots in Separate Figures
		8.6.2. Subplots
	8.7. Three-Dimensional Plots
	8.8. Python Instructions for Chapter 8
	8.9. Conclusions
	8.10. Exercises
	8.11. Selected Bibliography
9. Optimization
	9.1. Introduction
	9.2. Optimization Concepts
		9.2.1. Parameters, Variables, and Functions
	9.3. General Format of the Optimization Process
	9.4. Optimization with Python
	9.5. The minimize Function
	9.6. Linear Programming
	9.7. Quadratic Programming
	9.8. Python Instructions for Chapter 9
	9.9. Conclusions
	9.10. Selected Bibliography
10. Image Processing with OpenCV
	10.1. Introduction
	10.2. Reading and Writing Images and Videos
		10.2.1. Image Representation in Python
	10.3. Video Capture and Display
		10.3.1. Saving a Video File
	10.4. Binary Images
	10.5. Histogram
		10.5.1. Histogram Equalization
	10.6. Draw Geometric Shapes and Text on an Image
	10.7. Contour Detection
	10.8. Frequency Domain Processing
	10.9. Noise Addition to Images
		10.9.1. Denoising
	10.10. Morphological Image Processing
		10.10.1. Erosion and Dilation
		10.10.2. Dilation and Erosion
	10.11. Python Instructions in Chapter 10
	10.12. Conclusions
	10.13. Selected Bibliography
11. Machine Learning
	11.1. Types of Machine Learning Systems
		11.1.1. Supervised Machine Learning
		11.1.2. Multiple Features
		11.1.3. Feature Scaling
	11.2. Gradient Descent Algorithm
	11.3. Multivariate Regression
		11.3.1. Feature Scaling for the Multivariate Case
	11.4. The Normal Equation
	11.5. The Package scikit-learn
	11.6. Polynomial Regression
	11.7. Classification with Logistic Regression
		11.7.1. Logistic regression
	11.8. Unsupervised Learning
	11.9. Clustering Using k-means
		11.9.1. Cost Function
	11.10. Python Instructions in Chapter 11
	11.11. Conclusions
12. Neural Networks
	12.1. Introduction
	12.2. A Model for a Neuron
	12.3. Activation Functions
	12.4. Cost Function
	12.5. Tensor Flow
	12.6. Convolutional Neural Networks
		12.6.1. Padding
		12.6.2. Strided convolution
		12.6.3. Pooling
	12.7. A Layer of a Convolutional Filter
	12.8. Python Instructions in Chapter 12
	12.9. Conclusions
A. Installation of Libraries and Packages and Running the Scripts
	A.1. Introduction
	A.2. Running a Script using Google Colab
	A.3. Installation of Python
	A.4. Modules and Libraries Installation with pip
	A.5. Installation of Modules or Libraries
	A.6. Installation of OpenCV
		A.6.1. Installation in Windows for the IDLE
		A.6.2. Installation in the Mac for the IDLE
Index