A Lisp Programmer Living in Python-Land: The Hy Programming Language
This book is 100% complete
Completed on 2020-05-20
About the Book
While this is a book on the Hy Lisp language, we have a wider theme here. In an age where artificial intelligence (AI) is a driver of the largest corporations and government agencies, the question is how do individuals and small organizations take advantage of AI technologies given disadvantages of small scale. The material I choose to write about here is selected to help you, dear reader, survive as a healthy small fish in a big bond.
In addition to being a tutorial and cookbook on using the Hy language, longer examples explore a variety of different applications and tools. The current structure of the book is:
- Introduction to the Hy Language
- Why use Lisp? Advantages of bottom up development using macros and closures
- Relational databases
- Web app development
- Web scraping
- Accessing semantic web and linked data sources like Wikipedia, DBpedia, and Wikidata
- Automatically constructing Knowledge Graphs from text documents, semantic web and linked data
- Deep Learning (predictive model from spreadsheet data and a LSTM-based English text language model)
- Natural Language Processing (NLP) using Deep Learning
About the Author
Table of Contents
-
Preface
- Setting Up Your Development Environment
- What is Lisp Programming Style?
- Hy is Python, But With a Lisp Syntax
- How This Book Reflects My Views on Artificial Intelligence and the Future of Society and Technology
- About the Book Cover
- A Request from the Author
- Acknowledgements
-
Introduction to the Hy Language
- We Will Use the Contributed let Macro in Book Example Code
- Using Python Libraries
- Global vs. Local Variables
- Using Python Code in Hy Programs
- Using Hy Libraries in Python Programs
- Replacing the Python slice (cut) Notation with the Hy Functional Form
- Iterating Through a List With Index of Each Element
- Importing Libraries from Different Directories on Your Laptop
- Using Closures
- Hy Looks Like Clojure: How Similar Are They?
- Plotting Data Using the Numpy and the Matplotlib Libraries
- Bonus Points: Configuration for macOS and ITerm2 for Generating Plots Inline in a Hy REPL and Shell
-
Why Lisp?
- I Hated the Waterfall Method in the 1970s but Learned to Love a Bottom-Up Programming Style
- First Introduction to Lisp
- Commercial Product Development and Deployment Using Lisp
- Hy Macros Let You Extend the Hy Language in Your Programs
- Performing Bottom Up Development Inside a REPL is a Lifestyle Choice
-
Writing Web Applications
- Getting Started With Flask: Using Python Decorators in Hy
- Using Jinja2 Templates To Generate HTML
- Handling HTTP Sessions and Cookies
- Wrap Up
-
Responsible Web Scraping
- Using the Python BeautifulSoup Library in the Hy Language
- Getting HTML Links from the DemocracyNow.org News Web Site
- Getting Summaries of Front Page from the NPR.org News Web Site
-
Deep Learning
- Simple Multi-layer Perceptron Neural Networks
- Deep Learning
- Using Keras and TensorFlow to Model The Wisconsin Cancer Data Set
- Using a LSTM Recurrent Neural Network to Generate English Text Similar to the Philosopher Nietzsche’s writing
-
Natural Language Processing
- Exploring the spaCy Library
- Implementing a HyNLP Wrapper for the Python spaCy Library
- Wrap Up
-
Datastores
- Sqlite
- PostgreSQL
- RDF Data Using the “rdflib” Library
- Wrap Up
- Understanding the Resource Description Framework (RDF)
- Resource Namespaces Provided in rdflib
- Understanding the SPARQL Query Language
- Wrapping the Python rdflib Library
-
Knowledge Graphs - Implementing the KGCreator In Hy
- Recommended Industrial Use of Knowledge Graphs
- Design of KGCreator Application
- Problems with using Literal Values in RDF
- Revisiting This Example Using URIs Instead of Literal Values
- Wrap Up
- Book Wrap Up
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