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Viser: Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence
Stephen Lynch
(2023)
Sprog: Engelsk
CRC Press LLC
737,00 kr.
Denne bog er endnu ikke udgivet. Den forventes Jun 2023.
Detaljer om varen
- Paperback: 344 sider
- Udgiver: CRC Press LLC (Juni 2023)
- ISBN: 9781032258713
Features:
- No prior experience of programming is required.
- Online GitHub repository available with codes for readers to practice.
- Covers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computing.
- Full solutions to exercises are available as Jupyter notebooks on the Web.
Section I. An Introduction to Python.
1. The IDLE Integrated Development Learning Environment.
2. Anaconda, Spyder and the Libraries NumPy, Matplotlib and SymPy.
3. Jupyter Notebooks and Google Colab.
4. Python for AS-Level (High School) Mathematics.
5. Python for A-Level (High School) Mathematics. Section II. Python for Scientific Computing.
6. Biology.
7. Chemistry.
8. Data Science.
9. Economics.
10. Engineering.
11. Fractals and Multifractals.
12. Image Processing.
13. Numerical Methods for Ordinary and Partial Differential Equations.
14. Physics.
15. Statistics. Section III. Artificial Intelligence.
16. Brain Inspired Computing.
17. Neural Networks and Neurodynamics.
18. TensorFlow and Keras.
19. Recurrent Neural Networks.
20. Convolutional Neural Networks, TensorBoard, and Further Reading.
21. Answers and Hints to Exercises.
1. The IDLE Integrated Development Learning Environment.
2. Anaconda, Spyder and the Libraries NumPy, Matplotlib and SymPy.
3. Jupyter Notebooks and Google Colab.
4. Python for AS-Level (High School) Mathematics.
5. Python for A-Level (High School) Mathematics. Section II. Python for Scientific Computing.
6. Biology.
7. Chemistry.
8. Data Science.
9. Economics.
10. Engineering.
11. Fractals and Multifractals.
12. Image Processing.
13. Numerical Methods for Ordinary and Partial Differential Equations.
14. Physics.
15. Statistics. Section III. Artificial Intelligence.
16. Brain Inspired Computing.
17. Neural Networks and Neurodynamics.
18. TensorFlow and Keras.
19. Recurrent Neural Networks.
20. Convolutional Neural Networks, TensorBoard, and Further Reading.
21. Answers and Hints to Exercises.
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