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

✍ Scribed by David Báez-López, David Alfredo Báez Villegas

• Publisher: CRC Press
• Year: 2024
• Tongue: English
• Leaves: 453
• Series: The Python Series
• Category: Library

✦ Synopsis

Introduction to Python: with Applications in Optimization, Image and Video Processing, and Machine Learning is intended primarily for advanced undergraduate and graduate students in quantitative sciences such as mathematics, computer science, and engineering. In addition to this, the book is written in such a way that it can also serve as a self-contained handbook for professionals working in quantitative fields including finance, IT, and many other industries where programming is a useful or essential tool.

The book is written to be accessible and useful to those with no prior experience of Python, but those who are somewhat more adept will also benefit from the more advanced material that comes later in the book.

Features

Covers introductory and advanced material. Advanced material includes lists, dictionaries, tuples, arrays, plotting using Matplotlib, object-oriented programming
Suitable as a textbook for advanced undergraduates or postgraduates, or as a reference for researchers and professionals
Solutions manual, code, and additional examples are available for download

✦ Table of Contents

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