Stochastic Gradient Descent and Deploying Your Python Scripts on the Web | Real Python Podcast #48
Skills:
ML Pipelines70%
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.
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
👉 Links from the show: https://realpython.com/podcasts/rpp
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