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Episode 48: Stochastic Gradient Descent and Deploying Your Python Scripts on the Web

The Real Python Podcast

Feb 19, 2021 1h 1m

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David Amos
David Amos
Host
Christopher Bailey
Christopher Bailey
Host
Martin Breuss
Martin Breuss
Guest

Do you know the initial steps to get your Python script hosted on the web? You may have built something with Flask, but how would you stand it up so that you can share it with others? This week on the show, we have the previous guest Martin Breuss back on the show. Martin shares his recent article titled, “Python Web Applications: Deploy Your Script as a Flask App”. David Amos also returns, and he’s brought another batch of PyCoder’s Weekly articles and projects.

Episode Sponsor:

David shares a recent mathematical Real Python article about the stochastic gradient descent algorithm with Python. Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find ideal model parameters.

We also cover several other articles and projects from the Python community including, property-based testing with hypothesis, Python’s tug of war between beginner-friendly features and support for advanced users, how Python integers work, the steering council accepts PEP 634, a magical full-stack framework for Django named django-unicorn, and a visual programming environment called Math Inspector.

Topics:

  • 00:00:00 – Introduction
  • 00:02:44 – Property-Based Testing With hypothesis, and Associated Use Cases
  • 00:09:55 – Python’s Tug of War Between Beginner-Friendly Features and Support for Advanced Users
  • 00:18:50 – Sponsor: Scout APM
  • 00:19:54 – How Python Integers Work
  • 00:26:53 – Python Steering Council Accepts PEP 634
  • 00:32:48 – Stochastic Gradient Descent Algorithm With Python and NumPy
  • 00:38:36 – Video Course Spotlight
  • 00:39:39 – Martin Breuss - Followup about Stay at Home Mentorship Program
  • 00:42:13 – Python Web Applications: Deploy Your Script as a Flask App
  • 00:52:25 – django-unicorn: A Magical Full-Stack Framework for Django
  • 00:55:15 – Math Inspector: A Visual Programming Environment for Scientific Computing With NumPy and SciPy
  • 01:00:21 – Thanks and goodbye

Show Links:

Property-Based Testing With hypothesis, and Associated Use Cases – Testing software is hard. Property-based testing can help you create more effective tests. Learn how to do property-based testing with the hypothesis framework by looking at some real-world use cases.

Python’s Tug of War Between Beginner-Friendly Features and Support for Advanced Users – Python has made some big improvements to tracebacks in recent versions. See how tracebacks have evolved over the last couple of major releases and where there’s still some work left to be done. Check out the discussion on Hacker News.

How Python Integers Work – Python’s integer datatype is pretty different from most other languages because they allow arbitrary precision. Learn how integers work under the hood in this in-depth article.

Python Steering Council Accepts PEP 634 – Pattern matching, which adds a kind of switch-case statement to Python, has been accepted.

Stochastic Gradient Descent Algorithm With Python and NumPy – Learn what the stochastic gradient descent algorithm is, how it works, and how to implement it with Python and NumPy.

Python Web Applications: Deploy Your Script as a Flask App – In this tutorial, you’ll learn how to go from a local Python script to a fully deployed Flask web application that you can share with the world.

Projects:

Additional Links:

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