Python Notes: Introduction

  1. The information presented here is intended for educational use.
  2. The information presented here is provided free of charge, as-is, with no warranty of any kind.
  3. Edit: 2024-02-03
  1. Introduction
  2. my example Python programs
  3. Shifting over to Python
  4. Machine Learning + Deep Learning
  5. External Links

Introduction

Hacking - Noodling with BASIC

Anyone who played with BASIC on personal computers in the 1970s and 1980s will recognize the importance of the BASIC language for noodling around. The creators of BASIC intended it for use in teaching computer programming concepts to FORTRAN students at Dartmouth College (photo), but noodling around made BASIC ideal for teaching other concepts in science, engineering, and math all over the world.

BASIC on 8-bit personal computers in the 1970s was usually implemented in ROM, and every implementation was different (Apple2, Radio Shack TRS-80, HeathKit-H8). Starting with the IBM-PC in 1981, Microsoft, began publishing 16-bit software products like GW-BASIC (1983),  QuickBASIC (1985) and QBASIC (1991) which worked well on 16-bit operating systems like MS-DOS (1981) up through Windows-3.11 (1992). These 16-bit language interpreters were also supported on 32-bit operating systems, starting with Windows-95 up through Windows-7.

The big problem today is that 64-bit computers run 64-bit operating systems, like Windows-10, where 32-bit programs are supported via THUNKING, but native support for 16-bit programs has been dropped. Technical work-arounds exist including "setting up a virtual machine" on your 64-bit OS, but why go to all that bother when all you want to do is noodle around? Perhaps it is time for you to ditch BASIC for Python.

Hacking - Noodling with Python

Many people reading this will not know that Python was first created in 1991 to produce an internet-friendly BASIC-like scripting tool for use under CGI (common gateway interface) in web servers. Python quickly morphed into a language which could easily support both internet servers and clients, as well a standalone applications. Here is a short list:

Python does not have these BASIC imitations:
Python3 has no difficulty with these statements:

Imagine using numbers this size to index your data arrays

(a few of my) Example Programs (alphabetical order)

link description Notes
calendar a very simple calendar generator BASIC-to-Python conversion examples
compiling caching Python compiling - Python file caching article for nerds and speed demons
dft-fft Discrete Fourier Transform -
Fast Fourier Transform		 BASIC-to-Python conversion examples
dh standalone Diffie-Hellman key exchange demo (interactive) Python interactive standalone application
dh web Diffie-Hellman key exchange demo (web) Python web application (just to show you how)
Easter compute the date of Easter for any given year it all starts with determining the date of the first full moon on, or after. the Spring equinox.
fun with floats fun with floats and decimals be careful how you initialize float and decimal data; this program also shows how to properly use the exec() function in python3
http website tester shows how to fetch a web page this technique works with both http as well as https
https harder demo shows how to try harder shows how to connect to an older https site
logical_name_demo logical name demo caveat: this will only work on OpenVMS systems
money rounding money rounding demo required for GAAP (generally accepted accounting principles)
pix-of-day picture of the day generator extracts picture-of-the-day info from a relational database which is then passed to JavaScript.
Click this link (https://neilrieck.net) to see it in action:
1) the top right corner will show a NASA chart for last month's CO2 readings
2) eight seconds later, the image and text will switch to something extracted from my database
3) the pictures will cycle back for ten iterations
4) punch PF12 on your browser then click CONSOLE to observe log messages

Shifting over to Python

Machine Learning - Deep Learning

Executive Summary
  1. Just as it is nearly impossible to develop a working knowledge of Calculus without a working knowledge of Algebra, I am now convinced that you cannot develop a working knowledge Deep Machine Learning without a working knowledge of Probability, Statistics and Python
  2. Unlike other programming projects, you cannot develop a deep machine learning application, then publish it, then move onto something else. These systems are only as good as their training data which means they should constantly be updated with new information. (so the programmer becomes a teacher?)
  3. Artificial Intelligence does not mean Artificial Consciousness. While some modern A.I. systems might pass a Turing Test by Joe citizen, a specialist will always be able to do a better job. (think about the Voight-Kampf interview between Rick Deckard and Rachel in the movie Bladerunner. Rachel knew that eating raw oysters was okay but didn't know a serving 'boiled dog' was wrong)

Anyone considering a course on machine learning or deep learning should learn Python immediately where most examples will include one (or more) of these free Python libraries

NumPy Library to add array support to Python2
Also adds support for matrix mathematics		 https://en.wikipedia.org/wiki/NumPy Community - 2006
scikit-learn first generation learning library https://en.wikipedia.org/wiki/Scikit-learn Google Summer of Code - 2007
TensorFlow second generation learning library https://en.wikipedia.org/wiki/TensorFlow Google - 2015
Keras second generation learning library https://en.wikipedia.org/wiki/Keras Google - 2015
PyTorch second generation learning library https://en.wikipedia.org/wiki/PyTorch Facebook - 2017

The roots of artificial intelligence (and expert systems, and machine learning) go back to the 1940s and 1950s so contain a lot of non-computer terminology. Modern computer technologists wishing to learn more might wish to start here:

External Links

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 Neil Rieck
 Waterloo, Ontario, Canada.