Loving Common Lisp, or the Savvy Programmer's Secret Weapon
Loving Common Lisp, or the Savvy Programmer's Secret Weapon
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Loving Common Lisp, or the Savvy Programmer's Secret Weapon

This book is 100% complete

Completed on 2017-05-02

About the Book

This is the fourth edition of my book "Loving Common Lisp, or the Savvy Programmer's Secret Weapon." I removed some of the older material from the earlier editions and added application examples for deep learning, back-propagation and Hopfield neural networks, using the CLML machine learning library, heuristic search, and using Common Lisp clients for: MongoDB, Solr, CouchDB, and relational databases. For the older 3rd edition I added a chapter using my Natural Language Processing (NLP) library and a short chapter on information gathering.

The purpose of this book is to provide a quick introduction to Common Lisp and then provide the user with many fun and useful examples for using Common Lisp.

Before you buy this book you can first look at the programming examples at https://github.com/mark-watson/loving-common-lisp

The author has used Lisp programming languages professionally since the 1980s.

About the Author

Mark Watson
Mark Watson

Interested in the author? Please visit Mark's web site at http://markwatson.com and read his blog at http://blog.markwatson.com

Mark is the author of 16 books published for Springer-Verlag, J. Wiley, McGraw-Hill, and Morgan Kaufman publishers.

Mark's client list includes Google, Disney, SAIC, CompassLabs, Embed.ly, Nintendo, Lutris Technologies, PacBell, and the Webmond Corporation.

Table of Contents

  • Cover Material, Copyright, and License
  • Preface for the Fourth Edition of “Loving Common Lisp, or the Savvy Programmer’s Secret Weapon”
    • A Request from the Author
    • Book editions
    • Acknowledgments
  • Introduction
    • Why Did I Write this Book?
    • Free Software Tools for Common Lisp Programming
    • How is Lisp Different from Languages like Java and C++?
    • Advantages of Working in a Lisp Environment
  • Common Lisp Basics
    • Getting Started with SBCL
    • Making the repl Nicer using rlwrap
    • The Basics of Lisp Programming
    • Symbols
    • Operations on Lists
    • Using Arrays and Vectors
    • Using Strings
    • Using Hash Tables
    • Using Eval to Evaluate Lisp Forms
    • Using a Text Editor to Edit Lisp Source Files
    • Recovering from Errors
    • Garbage Collection
    • Loading your Working Environment Quickly
    • Functional Programming Concepts
  • Defining Lisp Functions
    • Using Lambda Forms
    • Using Recursion
    • Closures
  • Quicklisp
    • Using Quicklisp to Find Packages
    • Using Quicklisp to Configure Emacs and Slime
  • Defining Common Lisp Macros
    • Example Macro
    • Using the Splicing Operator
    • Using macroexpand-1
  • Using Common Lisp Loop Macros
    • dolist
    • dotimes
    • do
    • Using the loop Special Form to Iterate Over Vectors or Arrays’’
  • Common Lisp Package System
  • Input and Output
    • The Lisp read and read-line Functions
    • Lisp Printing Functions
  • Plotting Data
  • Common Lisp Object System - CLOS
    • Example of Using a CLOS Class
    • Implementation of the HTMLstream Class
  • Heuristically Guided Search
  • Network Programming
    • An introduction to Drakma
    • An introduction to Hunchentoot
    • Complete REST Client Server Example Using JSON for Data Serialization
  • Accessing Relational Databases
  • Using MongoDB, CouchDB, and Solr NoSQL Data Stores
    • MongoDB
    • CouchDB
    • A Common Lisp Solr Client
    • NoSQL Wrapup
  • Natural Language Processing
    • Loading and Running the NLP Library
    • Part of Speech Tagging
    • Categorizing Text
    • Stemming Text
    • Detecting People’s Names and Place Names
    • Summarizing Text
    • Text Mining
  • Information Gathering
    • DBPedia Lookup Service
    • Web Spiders
    • Using Apache Nutch
    • Wrap Up
  • Using The CL Machine-Learning Library
    • Using the CLML Data Loading and Access APIs
    • K-Means Clustering of Cancer Data Set
    • SVM Classification of Cancer Data Set
    • CLML Wrap Up
  • Backpropagation Neural Networks
  • Hopfield Neural Networks
  • Using Armed Bear Common Lisp With DeepLearning4j
    • Tutorial on passing data between Common Lisp and Java
    • Using DeepLearning4j Java Code
  • Book Wrapup

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