Reference books on machine learning in Clojure

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About Clojure and Functional Programming

a language called Clojure, a relatively new language created by Rich Hickey and introduced in 2007. Clojure is a relatively new language, created by Rich Hickey and introduced in 2007, but it is a dialect of the LISP language, which was introduced in 1958, and it runs on top of the JVM and can use code from the legacy JAVA programming language.

One of the features of Clojure is that it is a functional language. This is one of the latest trends in the history of programming languages, where all programs are composed of functional blocks called functions, as opposed to the usual languages such as python and javascript, where procedures are laid out.

One of the perspectives in the development of programming languages is to improve reusability. The object-oriented languages that dominated the world before functional languages were also developed from this perspective, but the idea of constructing programs in blocks of functions has further improved reusability.

In addition, a mechanism called REPL, in which functions are evaluated as they are written, reduces bugs at the time of writing and improves the efficiency of code generation. Furthermore, the “data = code” feature of LISP has the potential to realize artificial intelligence technologies, including automatic program generation.

Reference books on machine learning in Clojure are as follows. Unfortunately, they are not published in Japanese.Reference books on Clojure in general will be discussed separately.

Reference books on machine learning in Clojure

This will be the one that describes the most practical examples among the reference books introduced here.

「Mastering Clojure Data Analysis」

Chapter 1 : Network Analysis - The Six Degrees of Kevin Bacon
Chapter 2 : GIS Analysis - Mapping Climate Change
Chapter 3 : Topic Modeling - Changing Concerns in the State of the Union Addresses
Chapter 4 : Classifying UFO - Sightings
Chapter 5 : Benford's Law - Detecting Natural Progressions of Numbers
Chapter 6 : Sentiment Analysis - Categorizing Hotel Reviews
Chapter 7 : Null Hypothesis Test - Analyzing Crime Data
Chapter 8 : A/B Testing - Statical Experiments for Web
Chapter 9 : Analyzing Social Data Participation
Chapter 10 : Modeling Stock Data

「Clojure Data Structures and Algorithm Cookbook」

Chapter 1 : Revisiting Arrays
Chapter 2 : Alternative Linked Lists
Chapter 3 : Walking Down Forests of Data
Chapter 4 : Making Decisions with the Help of Science
Chapter 5 : Programming with Logic
Chapter 6 : Sharing by Communicating
Chapter 7 : Transformations as First-class Citizens

「Clojure for Machine Learning」

Chapter 1 : Working with Matrices
Chapter 2 : Understanding Linear Regression
Chapter 3 : Categorizing Data
Chapter 4 : Building Neural Networks
Chapter 5 : Selecting and Evaluating Data
Chapter 6 : Building Support Vector Machines
Chapter 7 : Clustering Data
Chapter 8 : Anomaly Detection and Recommendation
Chapter 9 : Large-scale Machine Learning

「Clojure Data Science」

Chapter 1 : Statistics
Chapter 2 : Inference
Chapter 3 : Correlation
Chapter 4 :  Classification
Chapter 5 : Big Data
Chapter 6 : Clustering
Chapter 7 :  Recommender  Systems
Chapter 8 :  Network  Analysis
Chapter 9 :  Time Series
Chapter 10 :  Visualization

「Clojure Data Analysis Cookbook 2nd」

Chapter 1 : Importing Data for Analysis
Chapter 2 : Cleaning and Validating Data
Chapter 3 : Managing Complexity with Concurrent
Chapter 4 : Improving Performance with Parallel Programming
Chapter 5 : Distributed Data Processing with Cascalog
Chapter 6 : Working with Incanter Datasets
Chapter 7 : Statical Data Analysis with Incanter
Chapter 8 : Working with Mathematica and R
Chapter 9 : Clustering, Classifying, and Working with Weka
Chapter 10 : Working with Unstructured and Texual Data
Chapter 11 : Graphing in Incanter
Chapter 12 : Creating Charts for the Web

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