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Deep Learning with R [[electronic resource] /] / by Abhijit Ghatak
| Deep Learning with R [[electronic resource] /] / by Abhijit Ghatak |
| Autore | Ghatak Abhijit |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (259 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Artificial intelligence
Computer science—Mathematics Computer programming Statistics R (Computer program language) Artificial Intelligence Mathematics of Computing Programming Techniques Statistics and Computing/Statistics Programs |
| ISBN | 981-13-5850-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Machine Learning -- Introduction to Neural Networks -- Deep Neural Networks – I -- Initialization of Network Parameters -- Optimization -- Deep Neural Networks - II -- Convolutional Neural Networks (ConvNets) -- Recurrent Neural Networks (RNN) or Sequence Models -- Epilogue. |
| Record Nr. | UNINA-9910350219603321 |
Ghatak Abhijit
|
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning with R [[electronic resource] /] / by Abhijit Ghatak
| Machine Learning with R [[electronic resource] /] / by Abhijit Ghatak |
| Autore | Ghatak Abhijit |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIX, 210 p. 56 illus.) |
| Disciplina | 519.502855133 |
| Soggetto topico |
Artificial intelligence
Computer programming Programming languages (Electronic computers) Database management R (Computer program language) Artificial Intelligence Programming Techniques Programming Languages, Compilers, Interpreters Database Management |
| ISBN | 981-10-6808-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- The Data-Driven Universe -- Causality-The Cornerstone of Accountability -- The Growth of Machines -- What is Machine Learning? -- Intended Audience -- Acknowledgements -- Contents -- About the Author -- 1 Linear Algebra, Numerical Optimization, and Its Applications in Machine Learning -- 1.1 Scalars, Vectors, and Linear Functions -- 1.1.1 Scalars -- 1.1.2 Vectors -- 1.2 Linear Functions -- 1.3 Matrices -- 1.3.1 Transpose of a Matrix -- 1.3.2 Identity Matrix -- 1.3.3 Inverse of a Matrix -- 1.3.4 Representing Linear Equations in Matrix Form -- 1.4 Matrix Transformations -- 1.5 Norms -- 1.5.1 ell2 Optimization -- 1.5.2 ell1 Optimization -- 1.6 Rewriting the Regression Model in Matrix Notation -- 1.7 Cost of a n-Dimensional Function -- 1.8 Computing the Gradient of the Cost -- 1.8.1 Closed-Form Solution -- 1.8.2 Gradient Descent -- 1.9 An Example of Gradient Descent Optimization -- 1.10 Eigendecomposition -- 1.11 Singular Value Decomposition (SVD) -- 1.12 Principal Component Analysis (PCA) -- 1.12.1 PCA and SVD -- 1.13 Computational Errors -- 1.13.1 Rounding---Overflow and Underflow -- 1.13.2 Conditioning -- 1.14 Numerical Optimization -- 2 Probability and Distributions -- 2.1 Sources of Uncertainty -- 2.2 Random Experiment -- 2.3 Probability -- 2.3.1 Marginal Probability -- 2.3.2 Conditional Probability -- 2.3.3 The Chain Rule -- 2.4 Bayes' Rule -- 2.5 Probability Distribution -- 2.5.1 Discrete Probability Distribution -- 2.5.2 Continuous Probability Distribution -- 2.5.3 Cumulative Probability Distribution -- 2.5.4 Joint Probability Distribution -- 2.6 Measures of Central Tendency -- 2.7 Dispersion -- 2.8 Covariance and Correlation -- 2.9 Shape of a Distribution -- 2.10 Chebyshev's Inequality -- 2.11 Common Probability Distributions -- 2.11.1 Discrete Distributions -- 2.11.2 Continuous Distributions.
2.11.3 Summary of Probability Distributions -- 2.12 Tests for Fit -- 2.12.1 Chi-Square Distribution -- 2.12.2 Chi-Square Test -- 2.13 Ratio Distributions -- 2.13.1 Student's t-Distribution -- 2.13.2 F-Distribution -- 3 Introduction to Machine Learning -- 3.1 Scientific Enquiry -- 3.1.1 Empirical Science -- 3.1.2 Theoretical Science -- 3.1.3 Computational Science -- 3.1.4 e-Science -- 3.2 Machine Learning -- 3.2.1 A Learning Task -- 3.2.2 The Performance Measure -- 3.2.3 The Experience -- 3.3 Train and Test Data -- 3.3.1 Training Error, Generalization (True) Error, and Test Error -- 3.4 Irreducible Error, Bias, and Variance -- 3.5 Bias--Variance Trade-off -- 3.6 Deriving the Expected Prediction Error -- 3.7 Underfitting and Overfitting -- 3.8 Regularization -- 3.9 Hyperparameters -- 3.10 Cross-Validation -- 3.11 Maximum Likelihood Estimation -- 3.12 Gradient Descent -- 3.13 Building a Machine Learning Algorithm -- 3.13.1 Challenges in Learning Algorithms -- 3.13.2 Curse of Dimensionality and Feature Engineering -- 3.14 Conclusion -- 4 Regression -- 4.1 Linear Regression -- 4.1.1 Hypothesis Function -- 4.1.2 Cost Function -- 4.2 Linear Regression as Ordinary Least Squares -- 4.3 Linear Regression as Maximum Likelihood -- 4.4 Gradient Descent -- 4.4.1 Gradient of RSS -- 4.4.2 Closed Form Solution -- 4.4.3 Step-by-Step Batch Gradient Descent -- 4.4.4 Writing the Batch Gradient Descent Application -- 4.4.5 Writing the Stochastic Gradient Descent Application -- 4.5 Linear Regression Assumptions -- 4.6 Summary of Regression Outputs -- 4.7 Ridge Regression -- 4.7.1 Computing the Gradient of Ridge Regression -- 4.7.2 Writing the Ridge Regression Gradient Descent Application -- 4.8 Assessing Performance -- 4.8.1 Sources of Error Revisited -- 4.8.2 Bias--Variance Trade-Off in Ridge Regression -- 4.9 Lasso Regression. 4.9.1 Coordinate Descent for Least Squares Regression -- 4.9.2 Coordinate Descent for Lasso -- 4.9.3 Writing the Lasso Coordinate Descent Application -- 4.9.4 Implementing Coordinate Descent -- 4.9.5 Bias Variance Trade-Off in Lasso Regression -- 5 Classification -- 5.1 Linear Classifiers -- 5.1.1 Linear Classifier Model -- 5.1.2 Interpreting the Score -- 5.2 Logistic Regression -- 5.2.1 Likelihood Function -- 5.2.2 Model Selection with Log-Likelihood -- 5.2.3 Gradient Ascent to Find the Best Linear Classifier -- 5.2.4 Deriving the Log-Likelihood Function -- 5.2.5 Deriving the Gradient of Log-Likelihood -- 5.2.6 Gradient Ascent for Logistic Regression -- 5.2.7 Writing the Logistic Regression Application -- 5.2.8 A Comparison Using the BFGS Optimization Method -- 5.2.9 Regularization -- 5.2.10 \ell_2 Regularized Logistic Regression -- 5.2.11 \ell_2 Regularized Logistic Regression with Gradient Ascent -- 5.2.12 Writing the Ridge Logistic Regression with Gradient Ascent Application -- 5.2.13 Writing the Lasso Regularized Logistic Regression With Gradient Ascent Application -- 5.3 Decision Trees -- 5.3.1 Decision Tree Algorithm -- 5.3.2 Overfitting in Decision Trees -- 5.3.3 Control of Tree Parameters -- 5.3.4 Writing the Decision Tree Application -- 5.3.5 Unbalanced Data -- 5.4 Assessing Performance -- 5.4.1 Assessing Performance--Logistic Regression -- 5.5 Boosting -- 5.5.1 AdaBoost Learning Ensemble -- 5.5.2 AdaBoost: Learning from Weighted Data -- 5.5.3 AdaBoost: Updating the Weights -- 5.5.4 AdaBoost Algorithm -- 5.5.5 Writing the Weighted Decision Tree Algorithm -- 5.5.6 Writing the AdaBoost Application -- 5.5.7 Performance of our AdaBoost Algorithm -- 5.6 Other Variants -- 5.6.1 Bagging -- 5.6.2 Gradient Boosting -- 5.6.3 XGBoost -- 6 Clustering -- 6.1 The Clustering Algorithm -- 6.2 Clustering Algorithm as Coordinate Descent optimization. 6.3 An Introduction to Text mining -- 6.3.1 Text Mining Application---Reading Multiple Text Files from Multiple Directories -- 6.3.2 Text Mining Application---Creating a Weighted tf-idf Document-Term Matrix -- 6.3.3 Text Mining Application---Exploratory Analysis -- 6.4 Writing the Clustering Application -- 6.4.1 Smart Initialization of k-means -- 6.4.2 Writing the k-means++ Application -- 6.4.3 Finding the Optimal Number of Centroids -- 6.5 Topic Modeling -- 6.5.1 Clustering and Topic Modeling -- 6.5.2 Latent Dirichlet Allocation for Topic Modeling -- Appendix References and Further Reading. |
| Record Nr. | UNINA-9910254832103321 |
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Ghatak Abhijit
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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