Clojure for Machine Learning

In this compelling introduction to machine learning techniques and algorithms, you’ll learn how to use your knowledge of Clojure. From building systems to using machine learning techniques in cloud architecture, it’s the complete guide.
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Clojure for Machine Learning

Akhil Wali

1 customer reviews
In this compelling introduction to machine learning techniques and algorithms, you’ll learn how to use your knowledge of Clojure. From building systems to using machine learning techniques in cloud architecture, it’s the complete guide.
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Book Details

ISBN 139781783284351
Paperback292 pages

Book Description

Clojure for Machine Learning is an introduction to machine learning techniques and algorithms. This book demonstrates how you can apply these techniques to real-world problems using the Clojure programming language.

It explores many machine learning techniques and also describes how to use Clojure to build machine learning systems. This book starts off by introducing the simple machine learning problems of regression and classification. It also describes how you can implement these machine learning techniques in Clojure. The book also demonstrates several Clojure libraries, which can be useful in solving machine learning problems.

Clojure for Machine Learning familiarizes you with several pragmatic machine learning techniques. By the end of this book, you will be fully aware of the Clojure libraries that can be used to solve a given machine learning problem.

Table of Contents

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
Understanding Ordinary Least Squares
Using linear regression for prediction
Understanding regularization
Summary
Chapter 3: Categorizing Data
Understanding the 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 overfitting
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
Linear classification using SVMs
Using kernel SVMs
Summary
Chapter 7: Clustering Data
Using K-means clustering
Using hierarchical clustering
Using Expectation-Maximization
Using 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

What You Will Learn

  • Build systems that use machine learning techniques in Clojure
  • Understand machine learning problems such as regression, classifi cation, and clustering
  • Discover the data structures used in machine learning techniques such as artifi cial neural networks and support vector machines
  • Implement machine learning algorithms in Clojure
  • Learn more about Clojure libraries to build machine learning systems
  • Discover techniques to improve and debug solutions built on machine learning techniques
  • Use machine learning techniques in a cloud architecture for the modern Web

Authors

Table of Contents

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
Understanding Ordinary Least Squares
Using linear regression for prediction
Understanding regularization
Summary
Chapter 3: Categorizing Data
Understanding the 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 overfitting
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
Linear classification using SVMs
Using kernel SVMs
Summary
Chapter 7: Clustering Data
Using K-means clustering
Using hierarchical clustering
Using Expectation-Maximization
Using 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

Book Details

ISBN 139781783284351
Paperback292 pages
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