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Clojure for machine learning : successfully leverage advanced machine learning techniques using the Clojure ecosystem /
A book that brings out the strengths of Clojure programming that have to facilitate machine learning. Each topic is described in substantial detail, and examples and libraries in Clojure are also demonstrated. This book is intended for Clojure developers who want to explore the area of machine learn...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing,
2014.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Working with Matrices; Introducing Leiningen; Representing matrices; Generating matrices; Adding matrices; Multiplying matrices; Transposing and inverting matrices; Interpolating using matrices; Summary; Chapter 2: Understanding Linear Regression; Understanding single-variable linear regression; Understanding gradient descent; Understanding multivariable linear regression; Gradient descent with multiple variables; Understanding ordinary least squares
- Using linear regression for predictionUnderstanding regularization; Summary; Chapter 3: Categorizing Data; Understanding binary and multiclass classification; Understanding the Bayesian classification; Using the k-nearest neighbors algorithm; Using decision trees; Summary; Chapter 4: Building Neural Networks; Understanding nonlinear regression; Representing neural networks; Understanding multilayer perceptron ANNs; Understanding the backpropagation algorithm; Understanding recurrent neural networks; Building SOMs; Summary; Chapter 5: Selecting and Evaluating Data
- Understanding underfitting and overfittingEvaluating a model; Understanding feature selection; Varying the regularization parameter; Understanding learning curves; Improving a model; Using cross-validation; Building a spam classifier; Summary; Chapter 6: Building Support Vector Machines; Understanding large margin classification; Alternative forms of SVMs; Linear classification using SVMs; Using kernel SVMs; Sequential minimal optimization; Using kernel functions; Summary; Chapter 7: Clustering Data; Using K-means clustering; Clustering data using clj-ml; Using hierarchical clustering
- Using Expectation-MaximizationUsing SOMs; Reducing dimensions in the data; Summary; Chapter 8: Anomaly Detection and Recommendation; Detecting anomalies; Building recommendation systems; Content-based filtering; Collaborative filtering; Using the Slope One algorithm; Summary; Chapter 9: Large-scale Machine Learning; Using MapReduce; Querying and storing datasets; Machine learning in the cloud; Summary; Appendix: References; Index