LEADER 09944nam 2200529 450 001 996475764403316 005 20221216121758.0 010 $a3-030-97645-9 035 $a(MiAaPQ)EBC7001231 035 $a(Au-PeEL)EBL7001231 035 $a(CKB)22895136700041 035 $a(PPN)269148167 035 $a(EXLCZ)9922895136700041 100 $a20221216d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOCaml scientific computing $efunctional programming in data science and artificial intelligence /$fLiang Wang, Jianxin Zhao, and Richard Mortier 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$dİ2022 215 $a1 online resource (372 pages) 225 1 $aUndergraduate Topics in Computer Science Ser. 311 08$aPrint version: Wang, Liang OCaml Scientific Computing Cham : Springer International Publishing AG,c2022 9783030976446 320 $aIncludes bibliographical references and index. 327 $aIntro -- Foreword -- Preface -- Acknowledgements -- Contents -- Acronyms -- Part I Numerical Techniques -- Chapter 1 Introduction -- 1.1 Scientific Computing -- 1.2 Functional Programming -- 1.3 OCaml Numerical Library -- 1.4 System Architecture -- 1.5 How to Run Code -- 1.6 Summary -- References -- Chapter 2 Numerical Algorithms -- 2.1 Interpolation -- 2.1.1 Polynomial Interpolation -- 2.1.2 Rational Interpolation -- 2.2 Integration -- 2.2.1 Basic Algorithms -- 2.2.2 Gaussian Quadrature -- 2.3 Special Functions -- 2.4 Summary -- References -- Chapter 3 Statistics -- 3.1 Random Variables -- 3.1.1 Descriptive Statistics -- 3.2 Distribution -- 3.2.1 Discrete Distribution -- 3.2.2 Continuous Distribution -- 3.2.3 Special Distributions -- 3.3 Multiple Variables -- 3.3.1 Joint and Conditional Probability -- 3.3.2 Covariance and Correlation -- 3.4 Sampling -- 3.4.1 Unbiased Estimator -- 3.4.2 Inferring Population Parameters -- 3.5 Hypothesis Tests -- 3.5.1 Theory -- 3.5.2 Gaussian Distribution in Hypothesis Testing -- 3.5.3 Two-Sample Inferences -- 3.5.4 More Tests -- 3.6 Summary -- References -- Chapter 4 Linear Algebra -- 4.1 Vectors and Matrices -- 4.2 Gaussian Elimination -- 4.2.1 LU Factorisation -- 4.2.2 Inverse and Transpose -- 4.3 Vector Space -- 4.3.1 Rank and Basis -- 4.3.2 Orthogonality -- 4.3.3 Solving Ax = b -- 4.3.4 Matrix Sensitivity -- 4.4 Determinants -- 4.5 Eigenvalues and Eigenvectors -- 4.5.1 Complex Matrices -- 4.5.2 Similarity Transformation and Diagonalisation -- 4.6 Positive Definite Matrices -- 4.6.1 Positive Definiteness -- 4.6.2 Singular Value Decomposition -- 4.7 Sparse Matrices -- 4.8 Summary -- References -- Chapter 5 N-Dimensional Arrays -- 5.1 Ndarray -- 5.1.1 Types -- 5.1.2 Ndarray Creation and Properties -- 5.1.3 Map, Fold, and Scan -- 5.1.4 Comparison Functions -- 5.1.5 Iteration Functions. 327 $a5.1.6 Manipulation Functions -- 5.1.7 Serialisation -- 5.2 Slicing -- 5.2.1 Slice Definition -- 5.2.2 Conventions and Examples -- 5.2.3 Advanced Usage -- 5.3 Broadcasting -- 5.4 Tensors -- 5.5 Summary -- References -- Chapter 6 Ordinary Differential Equations -- 6.1 Defining an ODE -- 6.1.1 Exact Solutions -- 6.1.2 Linear Systems -- 6.2 Solving ODEs Numerically -- 6.3 ODE Solvers -- 6.3.1 Solving Linear Oscillator System -- 6.3.2 Solver Structure -- 6.3.3 Symplectic Solver -- 6.3.4 Features and Limits -- 6.4 Examples of ODE Solvers -- 6.4.1 Explicit ODE -- 6.4.2 Two-body Problem -- 6.4.3 Lorenz Attractor -- 6.4.4 Damped Oscillation -- 6.5 Stiffness -- 6.5.1 Solving Non-Stiff ODEs -- 6.5.2 Solve Stiff ODEs -- 6.6 Summary -- References -- Chapter 7 Signal Processing -- 7.1 Discrete Fourier Transform -- 7.2 Fast Fourier Transform -- 7.2.1 Example: 1-D Discrete Fourier Transforms -- 7.3 Applications of FFTs -- 7.3.1 Finding the Period of Sunspots -- 7.3.2 Determine the Tone -- 7.3.3 Image Processing -- 7.4 Filtering -- 7.4.1 Example: Smoothing -- 7.4.2 Gaussian Filter -- 7.4.3 Signal Convolution -- 7.4.4 FFT and Image Convolution -- 7.5 Summary -- References -- Part II Advanced Data Analysis Techniques -- Chapter 8 Algorithmic Differentiation -- 8.1 Chain Rule -- 8.2 Differentiation Methods -- 8.2.1 Numerical Differentiation -- 8.2.2 Symbolic Differentiation -- 8.2.3 Algorithmic Differentiation -- 8.3 How Algorithmic Differentiation Works -- 8.3.1 Forward Mode -- 8.3.2 Reverse Mode -- 8.3.3 Forward Mode or Reverse Mode? -- 8.4 A Strawman AD Engine -- 8.4.1 Implementation of Forward Mode -- 8.4.2 Implementation of Reverse Mode -- 8.4.3 A Unified Implementation -- 8.5 Forward and Reverse Propagation API -- 8.5.1 Expressing Computation -- 8.5.2 Example: Forward Mode -- 8.5.3 Example: Reverse Mode -- 8.6 High-Level Functions -- 8.6.1 Derivative and Gradient. 327 $a8.6.2 Jacobian -- 8.6.3 Hessian and Laplacian -- 8.6.4 Other APIs -- 8.7 Internals of Algorithmic Differentiation -- 8.7.1 Architecture and Components -- 8.7.2 Extending AD -- 8.7.3 Lazy Evaluation -- 8.8 Summary -- References -- Chapter 9 Optimisation -- 9.1 Objective Functions -- 9.2 Root Finding -- 9.3 Univariate Function Optimisation -- 9.3.1 Use Derivatives -- 9.3.2 Golden Section Search -- 9.4 Multivariate Function Optimisation -- 9.4.1 Nelder-Mead Simplex Method -- 9.4.2 Gradient Descent Methods -- 9.4.3 Conjugate Gradient Method -- 9.4.4 Newton and Quasi-Newton Methods -- 9.5 Global Optimisation and Constrained Optimisation -- 9.6 Summary -- References -- Chapter 10 Regression -- 10.1 Linear Regression -- 10.1.1 Problem: Where to open a new McDonald's restaurant? -- 10.1.2 Cost Function -- 10.1.3 Solving Problem with Gradient Descent -- 10.2 Multiple Regression -- 10.2.1 Feature Normalisation -- 10.2.2 Analytical Solution -- 10.3 Non-Linear Regressions -- 10.4 Regularisation -- 10.4.1 Ols, Ridge, Lasso, and Elastic_net -- 10.5 Logistic Regression -- 10.5.1 Sigmoid Function -- 10.5.2 Cost Function -- 10.5.3 Example -- 10.5.4 Multi-class Classification -- 10.6 Support Vector Machines, SVMs -- 10.6.1 Kernel and Non-Linear Boundary -- 10.6.2 Example -- 10.7 Model Error and Selection -- 10.7.1 Error Metrics -- 10.7.2 Model Selection -- 10.8 Summary -- References -- Chapter 11 Neural Network -- 11.1 The Perceptron -- 11.2 Yet Another Regression -- 11.2.1 Model Representation -- 11.2.2 Forward Propagation -- 11.2.3 Back Propagation -- 11.2.4 Feedforward Network -- 11.2.5 Layers -- 11.2.6 Activation Functions -- 11.2.7 Initialisation -- 11.2.8 Training -- 11.2.9 Test -- 11.3 Neural Network Module -- 11.3.1 Neurons -- 11.3.1.1 Neural Graph -- 11.3.2 Training Parameters -- 11.4 Convolutional Neural Network -- 11.5 Recurrent Neural Network. 327 $a11.6 Generative Adversarial Network -- 11.7 Summary -- References -- Chapter 12 Vector Space Modelling -- 12.1 Introduction -- 12.2 Text Corpus Analysis -- 12.2.1 Building a Text Corpus -- 12.2.2 Using Corpus Module -- 12.3 Vector Space Models -- 12.3.1 Word Embedding and BERT -- 12.3.2 Bag of Words (BOW) -- 12.4 Term Frequency-Inverse Document Frequency (TF-IDF) -- 12.5 Latent Dirichlet Allocation (LDA) -- 12.5.1 Models -- 12.5.2 Dirichlet Distribution -- 12.5.3 Gibbs Sampling -- 12.5.4 Topic Modelling Example -- 12.6 Latent Semantic Analysis -- 12.7 Search Relevant Documents -- 12.7.1 Euclidean and Cosine Similarity -- 12.7.2 Linear Searching -- 12.8 Summary -- References -- Part III Use Cases -- Chapter 13 Case Study: Image Recognition -- 13.1 Types of Networks -- 13.1.1 LeNet -- 13.1.2 AlexNet -- 13.1.3 VGG -- 13.1.4 ResNet -- 13.1.5 SqueezeNet -- 13.1.6 Capsule Network -- 13.2 Building the InceptionV3 Network -- 13.2.1 InceptionV1 and InceptionV2 -- 13.2.2 Factorisation -- 13.2.3 Grid Size Reduction -- 13.2.4 InceptionV3 Architecture -- 13.3 PreparingWeights -- 13.4 Processing Image -- 13.5 Running Inference -- 13.6 Applications -- 13.7 Summary -- References -- Chapter 14 Case Study: Instance Segmentation -- 14.1 Introduction -- 14.2 Object Detection Architectures -- 14.2.1 R-CNN -- 14.2.2 Fast R-CNN -- 14.2.3 Faster R-CNN -- 14.2.4 Mask R-CNN -- 14.3 Mask R-CNN Network -- 14.3.1 Building Mask R-CNN -- 14.3.2 Feature Extractor -- 14.3.3 Proposal Generation -- 14.3.4 Classification -- 14.4 Run the Code -- 14.5 Summary -- References -- Chapter 15 Case Study: Neural Style Transfer -- 15.1 Content and Style -- 15.1.1 Content Reconstruction -- 15.1.2 Style Recreation -- 15.1.3 Combining Content and Style -- 15.1.4 Running NST -- 15.2 Extending NST -- 15.3 Fast Style Transfer -- 15.3.1 Building FST Network -- 15.3.2 Running FST -- 15.4 Summary. 327 $aReferences -- Chapter 16 Case Study: Recommender System -- 16.1 Introduction -- 16.2 Key Components And Pipeline -- 16.3 Reducing Dimensionality -- 16.4 Random Projection -- 16.5 Vector Storage Optimisation -- 16.6 Combining Multiple Trees -- 16.7 Critical Boundary Elimination -- 16.8 Search Operations Parallelisation -- 16.9 Code Implementation -- 16.10 Summary -- References -- Appendix A Conventions of Owl -- A.1 Pure vs Impure -- A.2 Ndarray vs Scalar -- A.3 Infix Operators -- A.4 Module Structures -- A.5 Operator Extension -- Appendix B Visualisation -- B.1 Plotting in Owl -- B.1.1 Create Plots -- B.1.2 Specification -- B.1.3 Subplots -- B.1.4 Multiple Lines -- B.1.5 Legend -- B.1.6 Drawing Patterns -- B.2 Plot Types -- Index. 410 0$aUndergraduate Topics in Computer Science Ser. 606 $aOCaml (Computer program language) 606 $aArtificial intelligence$xData processing 615 0$aOCaml (Computer program language) 615 0$aArtificial intelligence$xData processing. 676 $a005.114 700 $aWang$b Liang$f1975-$01270968 702 $aMortier$b Richard 702 $aZhao$b Jianxin$f1948- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996475764403316 996 $aOCaml scientific computing$92994034 997 $aUNISA LEADER 03543nam 22007092 450 001 9910783111003321 005 20160210135936.0 010 $a1-107-13508-7 010 $a1-280-43445-7 010 $a1-139-14852-4 010 $a0-511-17796-8 010 $a0-511-06124-2 010 $a0-511-05491-2 010 $a0-511-33035-9 010 $a0-511-61383-0 010 $a0-511-06970-7 035 $a(CKB)1000000000018122 035 $a(EBL)218127 035 $a(OCoLC)475924646 035 $a(SSID)ssj0000243713 035 $a(PQKBManifestationID)11193934 035 $a(PQKBTitleCode)TC0000243713 035 $a(PQKBWorkID)10164429 035 $a(PQKB)10396198 035 $a(UkCbUP)CR9780511613838 035 $a(MiAaPQ)EBC218127 035 $a(Au-PeEL)EBL218127 035 $a(CaPaEBR)ebr10070261 035 $a(CaONFJC)MIL43445 035 $a(EXLCZ)991000000000018122 100 $a20090914d2002|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSelf love and Christian ethics /$fDarlene Fozard Weaver$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2002. 215 $a1 online resource (xiii, 267 pages) $cdigital, PDF file(s) 225 1 $aNew studies in Christian ethics ;$v23 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-52097-5 311 $a0-521-81781-1 320 $aIncludes bibliographical references (p. 251-263) and index. 327 $aCover; Half-title; Series-title; Title; Copyright; Dedication; Contents; General editor's preface; Acknowledgments; CHAPTER 1 The contemporary problem of self love; CHAPTER 2 Self love in Christian ethics; CHAPTER 3 A hermeneutical account of self-relation; CHAPTER 4 Right self love; CHAPTER 5 Self love and moral action; CHAPTER 6 Self love, religion, and morality; Bibliography; Index 330 $aSelf love is an inescapable problem for ethics, yet much of contemporary ethics is reluctant to offer any normative moral anthropologies. Instead, secular ethics and contemporary culture promote a norm of self-realization which is subjective and uncritical. Christian ethics also fails to address this problem directly, because it tends to investigate self love within the context of conflicts between the self's interests and those of her neighbors. Self Love and Christian Ethics argues for right self love as the solution of proper self-relation that intersects with love for God and love for neighbor. Darlene Fozard Weaver explains that right self love entails a true self-understanding that is embodied in the person's concrete acts and relations. In making this argument, she calls upon ethicists to revisit ontological accounts of the self and to devote more attention to particular moral acts. 410 0$aNew studies in Christian ethics. ;$v23. 517 3 $aSelf Love & Christian Ethics 606 $aChristian ethics 606 $aSelf-esteem$xReligious aspects$xChristianity 606 $aLove$xReligious aspects$xChristianity 615 0$aChristian ethics. 615 0$aSelf-esteem$xReligious aspects$xChristianity. 615 0$aLove$xReligious aspects$xChristianity. 676 $a241 700 $aWeaver$b Darlene Fozard$01483915 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910783111003321 996 $aSelf love and Christian ethics$93702266 997 $aUNINA LEADER 02022nam 22006614a 450 001 9910781000603321 005 20171026195700.0 010 $a1-282-44518-9 010 $a9786612445187 010 $a0-472-02423-X 024 7 $a10.3998/mpub.11832 035 $a(CKB)2520000000006848 035 $a(OCoLC)587803840 035 $a(CaPaEBR)ebrary10360131 035 $a(SSID)ssj0000419049 035 $a(PQKBManifestationID)11301693 035 $a(PQKBTitleCode)TC0000419049 035 $a(PQKBWorkID)10382100 035 $a(PQKB)11320028 035 $a(MiU)10.3998/mpub.11832 035 $a(MiAaPQ)EBC3414617 035 $a(EXLCZ)992520000000006848 100 $a20030620d2004 ub 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGrowth, trade & systemic leadership /$fRafael Reuveny and William R. 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