Foreword xiii
Preface xv
Section I AnIntroductiontoPython
Chapter 1 TheIDLEIntegratedDevelopmentLearningEnvironment3
1.1INTRODUCTION 4
1.1.1TutorialOne:UsingPythonasaPowerfulCalculator(30
Minutes) 5
1.1.2 TutorialTwo:Lists(20Minutes) 7
1.2SIMPLEPROGRAMMINGINPYTHON 8
1.2.1TutorialThree:DefiningFunctions(30Minutes) 9
1.2.2TutorialFour:ForandWhileLoops(20Minutes) 11
1.2.3TutorialFive:If,elif,elseconstructs(10Minutes) 11
1.3THETURTLEMODULEANDFRACTALS 11
Chapter 2 Anaconda,SpyderandtheLibrariesNumPy,Matplotliband
SymPy 21
2.1ATUTORIALINTRODUCTIONTONUMPY 23
2.1.1TutorialOne:AnIntroductiontoNumPyandArrays(30
Minutes) 23
2.2A TUTORIALINTRODUCTIONTOMATPLOTLIB 25
2.2.1TutorialTwo:SimplePlotsusingtheSpyderEditorWindow
(30minutes) 25
2.3ATUTORIALINTRODUCTIONTOSYMPY 28
2.3.1TutorialThree:AnIntroductiontoSymPy(30Minutes)28
Chapter 3 JupyterNotebooksandGoogleColab 33
3.1JUPYTERNOTEBOOKS,CELLS,CODEANDMARKDOWN 33
3.2ANIMATIONSANDINTERACTIVEPLOTS 37
3.3GOOGLECOLABANDGITHUB 41
Chapter 4 PythonforAS-Level(HighSchool)Mathematics 45
4.1AS-LEVELMATHEMATICS(PART1) 46
4.2AS-LEVELMATHEMATICS(PART2) 50
Chapter 5 PythonforA-Level(HighSchool)Mathematics 61
5.1A-LEVELMATHEMATICS(PART1) 62
5.2A-LEVELMATHEMATICS(PART2) 68
Section II PythonforScientificComputing
Chapter 6 Biology 81
6.1ASIMPLEPOPULATIONMODEL 81
6.2APREDATOR-PREYMODEL 84
6.3ASIMPLEEPIDEMICMODEL 87
6.4HYSTERESISINSINGLEFIBERMUSCLE 89
Chapter 7 Chemistry 95
7.1BALANCINGCHEMICAL-REACTIONEQUATIONS 95
7.2CHEMICALKINETICS 97
7.3THEBELOUSOV-ZHABOTINSKIREACTION 99
7.4COMMON-IONEFFECTINSOLUBILITY 101
Chapter 8 DataScience 107
8.1INTRODUCTIONTOPANDAS 107
8.2LINEARPROGRAMMING 110
8.3K-MEANSCLUSTERING 115
8.4DECISIONTREES 119
Chapter 9 Economics 125
9.1THECOBB-DOUGLASQUANTITYOFPRODUCTIONMODEL126
9.2THESOLOW-SWANMODELOFECONOMICGROWTH 128
9.3MODERNPORTFOLIOTHEORY(MPT) 130
9.4THEBLACK-SCHOLESMODEL 133
Chapter 10 Engineering 139
10.1LINEARELECTRICALCIRCUITSANDTHEMEMRISTOR 139
10.2CHUA’SNONLINEARELECTRICALCIRCUIT 142
10.3COUPLEDOSCILLATORS:MASS-SPRINGMECHANICAL
SYSTEMS 144
10.4 PERIODICALLYFORCEDMECHANICALSYSTEMS 146
Chapter 11 Fractalsand Multifractals 153
11.1PLOTTINGFRACTALSWITHMATPLOTLIB 153
11.2BOX-COUNTINGBINARYIMAGES 158
11.3THEMULTIFRACTALCANTORSET 160
11.4THEMANDELBROTSET 162
Chapter 12 ImageProcessing 167
12.1IMAGEPROCESSING,ARRAYSANDMATRICES 168
12.2COLORIMAGES 169
12.3STATISTICALANALYSISONANIMAGE 170
12.4IMAGEPROCESSINGONMEDICALIMAGES 172
Chapter 13 NumericalMethodsforOrdinaryandPartialDifferential
Equations 177
13.1EULER’SMETHODTOSOLVEIVPS 178
13.2RUNGEKUTTAMETHOD(RK4) 179
13.3FINITEDIFFERENCEMETHOD:THEHEATEQUATION 181
13.4FINITEDIFFERENCEMETHOD:THEWAVEEQUATION 184
Chapter 14 Physics 191
14.1THEFASTFOURIERTRANSFORM 192
14.2THESIMPLEFIBERRING(SFR)RESONATOR 194
14.3THEJOSEPHSONJUNCTION 196
14.4MOTIONOFPLANETARYBODIES 198
Chapter 15 Statistics 203
15.1LINEARREGRESSION 203
15.2MARKOVCHAINS 207
15.3THESTUDENTT-TEST 210
15.4MONTE-CARLOSIMULATION 214
Section III Artificial Intelligence
Chapter 16 BrainInspiredComputing 223
16.1THEHODGKIN-HUXLEYMODEL 224
16.2THEBINARYOSCILLATORHALF-ADDER 227
16.3THEBINARYOSCILLATORSETRESETFLIP-FLOP 231
16.4REAL-WORLDAPPLICATIONSANDFUTUREWORK 234
Chapter 17 NeuralNetworksandNeurodynamics 241
17.1HISTORYANDTHEORYOFNEURALNETWORKS 241
17.2THEBACKPROPAGATIONALGORITHM 245
17.3MACHINELEARNINGONBOSTONHOUSINGDATA 247
17.4NEURODYNAMICS 250
Chapter 18 TensorFlow andKeras 255
18.1ARTIFICIALINTELLIGENCE 256
18.2LINEARREGRESSIONINTENSORFLOW 257
18.3XORLOGICGATEINTENSORFLOW 259
18.4BOSTONHOUSINGDATAINTENSORFLOWANDKERAS 261
Chapter 19 RecurrentNeuralNetworks 267
19.1THEDISCRETEHOPFIELDRNN 267
19.2THECONTINUOUSHOPFIELDRNN 270
19.3LSTMRNNTOPREDICTCHAOTICTIMESERIES 273
19.4LSTMRNNTOPREDICTFINANCIALTIMESERIES 278
Chapter 20 Convolutional NeuralNetworks,TensorBoardandFurther
Reading 285
20.1CONVOLVINGANDPOOLING 285
20.2CNNONTHEMNISTDATASET 288
20.3TENSORBOARD 290
20.4FURTHERREADING 292
Chapter 21 Answersand HintstoExercises 299
21.1SECTION1SOLUTIONS 299
21.2SECTION2SOLUTIONS 303
21.3SECTION3SOLUTIONS 306
Index 309

 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