Month: September 2020

# Time Series Model Template Using Exponential Smoothing Version 1

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

The purpose of this modeling exercise is to construct an end-to-end template for solving machine learning problems. This Python script will adapt Dr. Jason Brownlee’s blog post on this topic and build a robust template for solving similar problems.

Version 1 of the exponential smoothing template contains structures and features that are like the ARIMA template. I pull together this template to take a machine learning exercise from beginning to end.

You will find the Python templates on the Machine Learning Project Templates page.

# Regression Modeling Template Using Python and AutoKeras Version 1

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

The purpose of this modeling exercise is to construct an end-to-end template for solving machine learning problems. This Python script will adapt Dr. Jason Brownlee’s blog post on this topic and build a robust template for solving similar problems.

Version 1 of the AutoKeras regression template contains structures and features that are like the Scikit-Learn templates. I pull together this template to take a machine learning exercise from beginning to end.

You will find the Python templates on the Machine Learning Project Templates page.

# Multi-Class Modeling Template Using Python and AutoKeras Version 1

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

The purpose of this modeling exercise is to construct an end-to-end template for solving machine learning problems. This Python script will adapt Dr. Jason Brownlee’s blog post on this topic and build a robust template for solving similar problems.

Version 1 of the AutoKeras multi-class template contains structures and features that are like the Scikit-Learn templates. I pull together this template to take a machine learning exercise from beginning to end.

You will find the Python templates on the Machine Learning Project Templates page.

# Scott Adams on Loserthink, Part 9

In the book, Loserthink: How Untrained Brains Are Ruining America, Scott Adams analyzed and discussed ways to teach us how to eliminate our biases and to sharpen our ability to think critically.

These are some of my favorite quotes and takeaways from reading the book.

Here are tips on how we can help others break out of their mental prison.

What to watch out for: The Magic Question

“State ONE thing you believe on this topic that you think I do NOT believe.”

“Don’t play Whack-A-Mole with people who have laundry lists of reasons supporting their hallucinations. Ask for their strongest point only, and debunk it if you can. Target their undue confidence, not their entire laundry list.”

What to watch out for: Pacing

“Agree with people as much as you can without lying, and you will be in a better position to persuade.”

What to watch out for: Define the Weeds

“Don’t argue in the weeds of a debate. Dismiss the trivial stuff and concentrate on the variables that matter. That gives you the high ground.”

What to watch out for: Describe the Long Term

“Ask people with opposing opinions to describe what the future would look like if their view of the world were to play out. Does it sound reasonable?”

What to watch out for: Calling Out the Mind Reading

“The best way to avoid the mind reading illusion is to look for it in others. That will prime you to better catch yourself when you do your own mind reading.”

What to watch out for: Framing Issues

“You can’t get the right answer until you frame the question correctly. And partisans rarely do.”

（從我一個尊敬的作家，賽斯·高汀