Python for Scientific Computing and Artificial Intelligence

Length: 314 pages
Edition: 1
Language: English
Publisher: Chapman and Hall/CRC
Publication Date: 2023-06-15
ISBN-10: 1032258713
ISBN-13: 9781032258713
Sales Rank: #0 (See Top 100 Books)

Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).

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.

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.
Support Material

GitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/

Solutions to All Exercises:

Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/Solutions_Section_1.html

Section 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/Solutions_Section_2.html

Section 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/Solutions_Section_3.html

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
SECTION I: An Introduction to Python
	CHAPTER 1: The IDLE Integrated Development Learning Environment
		1.1. INTRODUCTION
			1.1.1. Tutorial One: Using Python as a Powerful Calculator (30 Minutes)
			1.1.2. Tutorial Two: Lists (20 Minutes)
		1.2. SIMPLE PROGRAMMING IN PYTHON
			1.2.1. Tutorial Three: Defining Functions (30 Minutes)
			1.2.2. Tutorial Four: For and While Loops (20 Minutes)
			1.2.3. Tutorial Five: If, elif, else constructs (10 Minutes)
		1.3. THE TURTLE MODULE AND FRACTALS
	CHAPTER 2: Anaconda, Spyder and the Libraries NumPy, Matplotlib and SymPy
		2.1. A TUTORIAL INTRODUCTION TO NUMPY
			2.1.1. Tutorial One: An Introduction to NumPy and Arrays (30 Minutes)
		2.2. A TUTORIAL INTRODUCTION TO MATPLOTLIB
			2.2.1. Tutorial Two: Simple Plots using the Spyder Editor Window (30 minutes)
		2.3. A TUTORIAL INTRODUCTION TO SYMPY
			2.3.1. Tutorial Three: An Introduction to SymPy (30 Minutes)
	CHAPTER 3: Jupyter Notebooks and Google Colab
		3.1. JUPYTER NOTEBOOKS, CELLS, CODE AND MARKDOWN
		3.2. ANIMATIONS AND INTERACTIVE PLOTS
		3.3. GOOGLE COLAB AND GITHUB
	CHAPTER 4: Python for AS-Level (High School) Mathematics
		4.1. AS-LEVEL MATHEMATICS (PART 1)
		4.2. AS-LEVEL MATHEMATICS (PART 2)
	CHAPTER 5: Python for A-Level (High School) Mathematics
		5.1. A-LEVEL MATHEMATICS (PART 1)
		5.2. A-LEVEL MATHEMATICS (PART 2)
SECTION II: Python for Scientific Computing
	CHAPTER 6: Biology
		6.1. A SIMPLE POPULATION MODEL
		6.2. A PREDATOR-PREY MODEL
		6.3. A SIMPLE EPIDEMIC MODEL
		6.4. HYSTERESIS IN SINGLE FIBER MUSCLE
	CHAPTER 7: Chemistry
		7.1. BALANCING CHEMICAL-REACTION EQUATIONS
		7.2. CHEMICAL KINETICS
		7.3. THE BELOUSOV-ZHABOTINSKI REACTION
		7.4. COMMON-ION EFFECT IN SOLUBILITY
	CHAPTER 8: Data Science
		8.1. INTRODUCTION TO PANDAS
		8.2. LINEAR PROGRAMMING
		8.3. K-MEANS CLUSTERING
		8.4. DECISION TREES
	CHAPTER 9: Economics
		9.1. THE COBB-DOUGLAS QUANTITY OF PRODUCTION MODEL
		9.2. THE SOLOW-SWAN MODEL OF ECONOMIC GROWTH
		9.3. MODERN PORTFOLIO THEORY (MPT)
		9.4. THE BLACK-SCHOLES MODEL
	CHAPTER 10: Engineering
		10.1. LINEAR ELECTRICAL CIRCUITS AND THE MEMRISTOR
		10.2. CHUA'S NONLINEAR ELECTRICAL CIRCUIT
		10.3. COUPLED OSCILLATORS: MASS-SPRING MECHANICAL SYSTEMS
		10.4. PERIODICALLY FORCED MECHANICAL SYSTEMS
	CHAPTER 11: Fractals and Multifractals
		11.1. PLOTTING FRACTALS WITH MATPLOTLIB
		11.2. BOX-COUNTING BINARY IMAGES
		11.3. THE MULTIFRACTAL CANTOR SET
		11.4. THE MANDELBROT SET
	CHAPTER 12: Image Processing
		12.1. IMAGE PROCESSING, ARRAYS AND MATRICES
		12.2. COLOR IMAGES
		12.3. STATISTICAL ANALYSIS ON AN IMAGE
		12.4. IMAGE PROCESSING ON MEDICAL IMAGES
	CHAPTER 13: Numerical Methods for Ordinary and Partial Differential Equations
		13.1. EULER'S METHOD TO SOLVE IVPS
		13.2. RUNGE KUTTA METHOD (RK4)
		13.3. FINITE DIFFERENCE METHOD: THE HEAT EQUATION
		13.4. FINITE DIFFERENCE METHOD: THE WAVE EQUATION
	CHAPTER 14: Physics
		14.1. THE FAST FOURIER TRANSFORM
		14.2. THE SIMPLE FIBER RING (SFR) RESONATOR
		14.3. THE JOSEPHSON JUNCTION
		14.4. MOTION OF PLANETARY BODIES
	CHAPTER 15: Statistics
		15.1. LINEAR REGRESSION
		15.2. MARKOV CHAINS
		15.3. THE STUDENT T-TEST
		15.4. MONTE-CARLO SIMULATION
SECTION III: Artificial Intelligence
	CHAPTER 16: Brain Inspired Computing
		16.1. THE HODGKIN-HUXLEY MODEL
		16.2. THE BINARY OSCILLATOR HALF-ADDER
		16.3. THE BINARY OSCILLATOR SET RESET FLIP-FLOP
		16.4. REAL-WORLD APPLICATIONS AND FUTURE WORK
	CHAPTER 17: Neural Networks and Neurodynamics
		17.1. HISTORY AND THEORY OF NEURAL NETWORKS
		17.2. THE BACKPROPAGATION ALGORITHM
		17.3. MACHINE LEARNING ON BOSTON HOUSING DATA
		17.4. NEURODYNAMICS
	CHAPTER 18: TensorFlow and Keras
		18.1. ARTIFICIAL INTELLIGENCE
		18.2. LINEAR REGRESSION IN TENSORFLOW
		18.3. XOR LOGIC GATE IN TENSORFLOW
		18.4. BOSTON HOUSING DATA IN TENSORFLOW AND KERAS
	CHAPTER 19: Recurrent Neural Networks
		19.1. THE DISCRETE HOPFIELD RNN
		19.2. THE CONTINUOUS HOPFIELD RNN
		19.3. LSTM RNN TO PREDICT CHAOTIC TIME SERIES
		19.4. LSTM RNN TO PREDICT FINANCIAL TIME SERIES
	CHAPTER 20: Convolutional Neural Networks, TensorBoard and Further Reading
		20.1. CONVOLVING AND POOLING
		20.2. CNN ON THE MNIST DATASET
		20.3. TENSORBOARD
		20.4. FURTHER READING
	CHAPTER 21: Answers and Hints to Exercises
		21.1. SECTION 1 SOLUTIONS
		21.2. SECTION 2 SOLUTIONS
		21.3. SECTION 3 SOLUTIONS
Index