Stephen Lynch
Python for Scientific Computing and Artificial Intelligence
70,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in über 4 Wochen
Melden Sie sich
hier
hier
für den Produktalarm an, um über die Verfügbarkeit des Produkts informiert zu werden.
Stephen Lynch
Python for Scientific Computing and Artificial Intelligence
- Broschiertes Buch
This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.
Andere Kunden interessierten sich auch für
- Marco Scutari (Istituto Dalle Molle)The Pragmatic Programmer for Machine Learning110,99 €
- Marcus HutterAn Introduction to Universal Artificial Intelligence82,99 €
- Jeffery J. LeaderNumerical Analysis and Scientific Computation85,99 €
- John Atkinson-AbutridyText Analytics65,99 €
- Mathematics in Cyber Research151,99 €
- Nello CristianiniThe Shortcut23,99 €
- Victor Grigor'e GanzhaNumerical Solutions for Partial Differential Equations85,99 €
-
-
-
This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.
Produktdetails
- Produktdetails
- Chapman & Hall/CRC The Python Series
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 314
- Erscheinungstermin: 15. Juni 2023
- Englisch
- Abmessung: 177mm x 254mm x 20mm
- Gewicht: 710g
- ISBN-13: 9781032258713
- ISBN-10: 1032258713
- Artikelnr.: 67400005
- Chapman & Hall/CRC The Python Series
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 314
- Erscheinungstermin: 15. Juni 2023
- Englisch
- Abmessung: 177mm x 254mm x 20mm
- Gewicht: 710g
- ISBN-13: 9781032258713
- ISBN-10: 1032258713
- Artikelnr.: 67400005
In 2022, Stephen Lynch was named a National Teaching Fellow, which celebrates and recognises individuals who have made an outstanding impact on student outcomes and teaching in higher education. He won the award for his work in programming in the STEM subjects, research feeding into teaching, and widening participation (using experiential and object-based learning). Although educated as a pure mathematician, Stephen's many interests now include applied mathematics, cell biology, electrical engineering, computing, neural networks, nonlinear optics and binary oscillator computing, which he co-invented with a colleague. He has authored 2 international patents for inventions, 8 books, 4 book chapters, over 40 journal articles, and a few conference proceedings. Stephen is a Fellow of the Institute of Mathematics and Its Applications (FIMA) and a Senior Fellow of the Higher Education Academy (SFHEA). He is currently a Reader with MMU and was an Associate Lecturer with the Open University from 2008-2012. In 2010, Stephen volunteered as a STEM Ambassador, in 2012, he was awarded MMU Public Engagement Champion status, and in 2014 he became a Speaker for Schools. He runs national workshops on "Python for A-Level Mathematics and Beyond," and international workshops on "Python for Scientific Computing and TensorFlow for Artificial Intelligence." He has run workshops in China, Malaysia, Singapore, and the USA.
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.
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.
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.
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.