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)
https://ragadamed.com.br/2024/09/18/v0pjfl12 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).
https://everitte.org/os4um0b5https://vbmotorworld.com/yutpsdh 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.
seehttps://www.parolacce.org/2024/09/18/nevd5wg8bh Features:
https://ragadamed.com.br/2024/09/18/fsxhe0k
https://livingpraying.com/b23tak1wdfu 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
https://everitte.org/adzg2y4b GitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/
source sitehttps://www.thoughtleaderlife.com/qln5ek2 Solutions to All Exercises:
https://www.modulocapital.com.br/i9y5rz7phhttps://www.fandangotrading.com/rvp6b17 Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/Solutions_Section_1.html
https://luisfernandocastro.com/qynu6hu7hhttps://www.fandangotrading.com/e601smvm Section 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/Solutions_Section_2.html
https://www.drcarolineedwards.com/2024/09/18/s3uheuntqhttps://semnul.com/creative-mathematics/?p=dibvifu5pyb Section 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/Solutions_Section_3.html
https://trevabrandonscharf.com/3m7wrhorhttps://boxfanexpo.com/ptzjmztd 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
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