| ISBN13: | 9781032117676 |
| ISBN10: | 1032117672 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 452 oldal |
| Méret: | 234x156 mm |
| Nyelv: | angol |
| Illusztrációk: | 116 Illustrations, black & white; 5 Illustrations, color; 116 Line drawings, black & white; 5 Line drawings, color; 41 Tables, black & white |
| 700 |
A matematika általános kérdései
Valószínűségelmélet és matematikai statisztika
Optimalizáció, lineáris programozás, játékelmélet
Alkalmazott matematika
Villamosmérnöki tudományok, híradástechnika, műszeripar
Energetika, energiaipar
A számítástudomány elmélete, a számítástechnika általában
Számítógép architektúrák, logikai tervezés
Szoftverfejlesztés
Magasszintű programnyelvek
Mesterséges intelligencia
Digitális jel-, hang- és képfeldolgozás
Programnyelvek általában
Környezetmérnöki tudományok
A matematika általános kérdései (karitatív célú kampány)
Valószínűségelmélet és matematikai statisztika (karitatív célú kampány)
Optimalizáció, lineáris programozás, játékelmélet (karitatív célú kampány)
Alkalmazott matematika (karitatív célú kampány)
Villamosmérnöki tudományok, híradástechnika, műszeripar (karitatív célú kampány)
Energetika, energiaipar (karitatív célú kampány)
A számítástudomány elmélete, a számítástechnika általában (karitatív célú kampány)
Számítógép architektúrák, logikai tervezés (karitatív célú kampány)
Szoftverfejlesztés (karitatív célú kampány)
Magasszintű programnyelvek (karitatív célú kampány)
Mesterséges intelligencia (karitatív célú kampány)
Digitális jel-, hang- és képfeldolgozás (karitatív célú kampány)
Programnyelvek általában (karitatív célú kampány)
Környezetmérnöki tudományok (karitatív célú kampány)
Introduction to Python
GBP 170.00
Kattintson ide a feliratkozáshoz
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. This book is written in such a way that it can also serve as a self-contained handbook for professionals working in various fields.
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
1. Introduction to Python. 1.1. What is the Python programming language. 1.2. The Python programming language. 1.3. Book organization. 1.4. Algorithms. 1.5. Variables. 1.6. Input and output in Python. 1.7. Programs in Python. 1.8. Comments in a program. 1.9. Functions in Python. 1.10. Modules and libraries. 1.11. 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. 2.9. Nested loops. 2.10. The instruction break. 2.11. The instruction continues. 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. 3.3. Functions on strings. 3.4. Immutability of strings. 3.5. Lists. 3.6. Tuples. 3.7. Dictionaries. 3.8. Sets. 3.9. Python Instructions for Chapter 3. 3.10. Conclusions. 3.11. Exercises . 4 Arrays. 4.1. Introduction. 4.2. Introduction to array. 4.3. Vectors. 4.4. Examples with vectors in Python. 4.5. Matrices. 4.6. Arrays in Python. 4.7. Matrix operations using linear algebra with numpy. 4.8. Special Matrices. 4.9. Examples. 4.10. Arrays in Pandas. 4.11. Python instructions for Chapter 4. 4.12. Conclusions. 4.13. 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. 6.4. 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. 7.3. Writing numerical data to a file. 7.4. Data reading from a file. 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. 8.3. The package seaborn. 8.4. Other two-dimensional plots. 8.5. Pie charts. 8.6. Multiple figures. 8.7. Three-Dimensional Plots. 8.8. Python instructions for Chapter 8. 8.9. Conclusions. 8.10. Exercises. 8.11. References. 9. Optimization. 9.1. Introduction. 9.2. Optimization Concepts. 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. 10.3. Video capture and display. 10.4. Binary images. 10.5. Histogram. 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. 10.10. Morphological image processing. 10.11. Python Instructions in Chapter 10. 10.12. Conclusions. 10.13. Selected bibliography. 11. Machine Learning. 11.1. Types of machine learning systems. 11.2. Gradient descent algorithm. 11.3. Multivariate regression. 11.4. The normal equation. 11.5. The package scikit-learn. 11.6. Polynomial regression. 11.7. Classification with logistic regression. 11.8. Unsupervised Learning. 11.9. Clustering using k-means. 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. TensorFlow. 12.6. Convolutional neural networks. 12.7. A layer of a convolutional filter. 12.8. Python instructions in Chapter 12. 12.9. Conclusions.
