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#210: Analyzing Kickstarter Campaigns with Python

Published Thu, Dec 3, 2020, recorded Mon, Nov 23, 2020

The live stream recording on YouTube.

Special guest: Jay Miller

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Brian #1: Analyzing Kickstarter Campaigns with Python Data Science Tools

  • Article title: “Kickstarter Projects — Do They Succeed?”
  • Aditya Patkar
  • Using a Kaggle dataset of 378,661-ish projects up to 2018.
  • Looks at using pandas data frames to explore the data.
  • Using .describe() data frame method to learn a lot.
  • Uses matplotlib and seaborn to analyze the data further.
  • Odd statement that I’m not sure is straight faced or a really dry joke: “The data from 1970 seems to be bad or insignificant data.”
  • Examples of using heat maps, line graphs, bar charts, to look at different aspects.
  • Some results:
    • 35.64% of projects are successful (meaning goal hit)
    • tech asks for the most for goals, and has the highest average per backer.
    • Comics has the lowest pledged amount per backer average.
  • Nice that you can use the techniques to ask your own questions of the data.

Michael #2: GPU Accelerated Python for Machine Learning on Cross-Vendor Graphics Cards

  • Building machine learning algorithms using the Vulkan Kompute Python Framework
  • When you hear “CUDA”, that means Nvidia 🙂
  • Uses Vulkan Kompute framework
  • A large number of high profile (and new) machine learning frameworks such as Google’s Tensorflow, Facebook’s Pytorch, Tencent’s NCNN, Alibaba’s MNN —between others — have been adopting Vulkan as their core cross-vendor GPU computing SDK.
  • As you can imagine, the Vulkan SDK provides very low-level C / C++ access to GPUs, which allows for very specialized optimizations.
  • The main disadvantage is the verbosity involved, requiring 500–2000+ lines of C++ code to only get the base boilerplate required to even start writing the application logic.
  • The Kompute Python package is built on top of the Vulkan SDK through optimized C++ bindings, which exposes Vulkan’s core computing capabilities. Kompute is the Python GPGPU framework.
  • The main article talks through a couple of numerical computation examples.

Jay #3: Adafruit PyPortal - CircuitPython Powered Internet Display

  • Gift for the tinkering pythonista
  • CircuitPy
  • Use it to make plenty of cool things
  • Screen/speaker/Light Sensor Built-In

Brian #4: Introduction to Linear Algebra for Applied Machine Learning with Python

  • Pablo Caceres
  • Intended as a reference and not a comprehensive review.
  • Still, I very much appreciate it.
  • Includes links to both free and paid resources to thoroughly learn linear algebra
  • Covers
    • sets, ordered pairs, relations, functions,
    • vectors
    • matrices
    • linear and affine mappings
    • matrix decomposition
  • Uses numpy, pandas, and altair for examples
  • Quick (but useful) explanations of concepts, along with how to represent and do it with numpy
  • I’m really just getting into it, but I’m enjoying it and this is the right level of handholding I needed.

Michael #5: How many notebook frameworks? Many, and now +1 with Deepnote

  • Deepnote is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud.
  • Free for individuals, paid for teams and companies
  • Real time collaboration is a key feature
  • Built in versioning coming
  • Code review in the notebook coming
  • “View” your variables as a whole environment
  • Better — real — autocomplete
  • Dashboards coming too

Jay #6:



  • The Apple M1 mac mini wait continues. :)
  • Talk Python To Me, pro edition
  • PSF Fundraiser for the month of December:




  • Q: why can't SQL and NoSQL Developers date one other?
  • A: because they don't agree on relationships.

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