A Lisp Programmer Living in Python-Land: The Hy Programming Language
A Lisp Programmer Living in Python-Land: The Hy Programming Language
All examples are provided in both Hy and Python
About the Book
Github repository update: December 2020: all examples are also provided in Python (the book discusses the Hy language versions of the examples).
Updated July 31, 2020: I corrected several typos, added more descriptive text, fixed a possible installation problem for the NLP coreference example, and did some rework on the semantic web material (knowledge Graphs).
Updated June 7, 2020: I added two chapters on using the Bing Search APIs and on my Knowledge Graph Navigator project. Several other minor changes.
While this is a book on the Hy Lisp language, all example programs are also provided in Python.
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
- Using the Bing search APIs
- 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
- Creating Knowledge Graphs from text data
- Hy language implementation of the author's Knowledge Graph Navigator project
All of my Leanpub books, including this book, can be read for FREE on my web site: https://markwatson.com/
About the Author
Table of Contents
- Cover Material, Copyright, and License
-
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 Often 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
- Formatted Output
- Importing Libraries from Different Directories on Your Laptop
- 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
-
Running Hy in Jupyter Notebooks
- Install Requirements
-
Writing Web Applications
- Getting Started With Flask: Using Python Decorators in Hy
- Using Jinja2 Templates To Generate HTML
- Handling HTTP Sessions and Cookies
- Deploying Hy Language Flask Apps to Google Cloud Platform AppEngine
- Deploying Hy Language Flask Apps to the Heroku Platform
- 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
-
Using the Microsoft Bing Search APIs
- Getting an Access Key for Microsoft Bing Search APIs
- Example Search Script
- Wrap-up
-
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
- Coreference (Anaphora Resolution)
- Wrap-up
-
Datastores
- Sqlite
- PostgreSQL
- RDF Data Using the “rdflib” Library
- Wrap-up
-
Linked Data, the Semantic Web, and Knowledge Graphs
- Understanding the Resource Description Framework (RDF)
- Resource Namespaces Provided in rdflib
- Understanding the SPARQL Query Language
- Wrapping the Python rdflib Library
-
Knowledge Graph Creator
- 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
-
Knowledge Graph Navigator
- Review of NLP Utilities Used in Application
- Developing Low-Level Caching SPARQL Utilities
- Utilities to Colorize SPARQL and Generated Output
- Text Utilities for Queries and Results
- Finishing the Main Function for KGN
- Wrap-up
- Book Wrap-up
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