Stochastic Gradient Descent and Deploying Your Python Scripts on the Web

Feb 19, 2021

Ask episode

Chapters

Transcript

Episode notes

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.

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.

Course Spotlight: Simulating Real-World Processes in Python With SimPy

In this step-by-step course, you’ll see how you can use the SimPy package to model real-world processes with a high potential for congestion. You’ll create an algorithm to approximate a complex system, and then you’ll design and run a simulation of that system in Python.

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:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas