Clojure for Machine Learning by Wali, Akhil -- Read -- Imperial Library of Trantor

Index

Clojure for Machine Learning

Table of Contents Clojure for Machine Learning Credits About the Author About the Reviewers www.PacktPub.com

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Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Downloading the color images of this book Errata Piracy Questions

1. Working with Matrices

Introducing Leiningen Representing matrices Generating matrices Adding matrices Multiplying matrices Transposing and inverting matrices Interpolating using matrices Summary

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 prediction Understanding regularization Summary

3. Categorizing Data

Understanding the binary and multiclass classification Understanding the Bayesian classification Using the k-nearest neighbors algorithm Using decision trees Summary

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

5. Selecting and Evaluating Data

Understanding underfitting and overfitting

Evaluating a model Understanding feature selection

Varying the regularization parameter Understanding learning curves Improving a model Using cross-validation Building a spam classifier Summary

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

7. Clustering Data

Using K-means clustering

Clustering data using clj-ml

Using hierarchical clustering Using Expectation-Maximization Using SOMs Reducing dimensions in the data Summary

8. Anomaly Detection and Recommendation

Detecting anomalies Building recommendation systems Content-based filtering Collaborative filtering Using the Slope One algorithm Summary

9. Large-scale Machine Learning

Using MapReduce Querying and storing datasets Machine learning in the cloud Summary

A. References

Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9

Index