#210: Analyzing Kickstarter Campaigns with Python

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

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

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: imagekit.io

Extras

Michael:

Jay:

Elastic Community YouTube Channel
- Just posted my lightning talk on looking at open data from the government.
- Upcoming interview on one of our newest clients - Eland which is python client to create pandas-like dataframes with elasticsearch datastores.
My Podcast The PIT Show weekly insights from me on my developer journey and interviews with amazing folks in the tech space.
Elastic Blog - Just posted my first Elastic Blog post Elastic Contributor Program: How to create a video tutorial

Joke:

via twitter.com/Spirix3/status/1330611989891207168

Full transcript