Doing It Wrong

It’s been a long week so I only have the basic WASD controller set up with a proper state machine. Now I can run around the featureless checkerboard plain with a few amenities like walking around a featureless checkerboard plain autorunning around a featureless checkerboard plain autowalking around a featureless checkerboard plain I’ll add jumping[…]

I spend a lot of time tinkering with various software projects, going at problems the wrong way, and generally making stupid decisions that a professional or more formally trained person would cringe to look at. Herein begins my cautionary tale as I learn painful lessons the hard way so you, dear reader, don’t have to.[…]

Coming away from the first class on the Bodhisattva Precepts at Buddha Eye Temple I have the following to roll around in my head for a while. The Bodhisattva Precepts used in the Soto Zen tradition encompass and expand on the ethics / sila encompassed in the Noble Eightfold Path. The sixteen Bodhisattva Precepts include[…]

I don’t know why I continue to use Windows. Every single god damned thing feels like pushing a giant rock up a hill. I’ll admit that the fact that I’ve been doing most of my work on Linux for the past twenty years inclines me to use the linux idiom for getting things done and[…]

HAL8999 7/100 I was sick yesterday but did spend some time looking over some “cheat sheets” that people had put together for various machine learning topics. Some were good, some were just stupid (I’m looking at you Machine Learning in Emoji). Also went through a very simply classifier based on the iris data set.

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HAL8999 6/100 Watched the “Learn how to Learn” Google talk on youtube Updated the jupyter notebooks for handson-ml from github and read through the Ch2 notebook to address the CategoricalEncoder issue from yesterday Looked at a basic transformer Part of building a data pipeline is likely to include the creation of custom transformer classes to[…]

HAL8999 – 5/100 Today while going back through the Hands On Machine Learning book Ch2 I learned that the CategoricalEncoder referenced in the section on handling categorical attributes still isn’t in scikit-learn. I checked the reqirements.txt which shows scikit-learn=0.19.1. Checking my virtualenv, I should be good.

Turns out that the CategoricalEncoder isn’t going to[…]

HAL8999 – [3,4]/100 Chapter 2 of Hands on ML continues Creation of test sets Stratified sampling sklearn’s StratifiedShuffleSplit Visualizing data with matplotlib Coorelation coefficients Getting a good train-test split Since you can’t train a model and just expect it to work well right out of the box it’s standard practice to split off about 20%[…]

HAL8999 – 3/100 Chapter 2 of Hands on ML Cost functions virtualenv setup code to get the dataset The chapter follows a rudimentary machine learning project from business case to final product. California census data is analyzed to build a model which will predict media housing price in a district based on other factors using[…]

Achievement: HAL 8999 – 2/100 Completed chapter 1 of Hands On ML and worked through the exercises Modified yesterdays example to also do both k-nearest-neighbors with both three and four neighbors. Four neighbors was further from the linear regression than three demonstrating that more is not always better. Short list but Ch1 is something of[…]