Topic: Machine Learning

Machine learning is what first attracted me to data science as I finished my PhD. The ability to train a machine to do a better job at certain tasks than a human was awe inspiring. My articles about machine learning can be found here:

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  LLMs Make Python Scripts Free
  I used to spend hours writing and saving little Python scripts. Now, with LLMs, I can get the same code in under a minute. Python scripts, and small programs in general, are effectively free.
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  How I Code With LLMs
  Over the past few years, I've experimented with coding alongside large language models. This post shares how I integrate them into my workflow.
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  How I Write with Large Language Models
  OpenAI's ChatGPT 3.5 transformed my writing process when it came out. After years of experience using it, I've further refined my method of using LLMs. This post explains how.
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  Using Large Language Models To Clean Data
  Manually fixing messy data is tedious and slow. But thankfully, LLMs are pretty good at piecing together mangled text. Read on to find out how!
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  Machine Learning Deployment:
  Return Actions, Not Scores
  A poorly designed machine learning model API will leave you trapped. Properly hiding your implementation will make life much easier!
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  Claude Solves SAT Analogies
  Word2Vec failed to solve SAT analogies, can modern language models do better? A small test of Anthropic's Claude LLM.
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  When Are Large Language Models Useful?
  Large language models (LLMs) are incredibly valuable tools, but they're not for everything. Here's a simple rule to know when to use them and when to avoid them.
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  How I Write with ChatGPT
  OpenAI's ChatGPT is viewed as entertaining but not useful because it makes up facts. But I find it incredibly valuable for writing. Here is how I use it.
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  AI, Artists, and Technology
  AI generated art took off with the open-source release of Stable Diffusion, leaving some artists worried. As an artist and machine learning engineer, here is my take.
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  Using Scikit-learn Pipelines with Pandas Dataframes
  Pandas and scikit-learn are two important libraries for building machine learning models. Here is how to get them to work together.
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  Computing Machine Learning Features in Real-time
  Models often derive great value from real-time features, but computing them is hard because it has to be done quickly. Here is one way I have done it successfully.
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  Machine Learning Deployment: Shadow Mode
  Deploying machine learning models is hard; Shadow Mode is one way to make testing a little easier.
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  Interview Question: What Machine Learning Metric to Use
  One of my favorite questions to ask in an interview is "What metric should you use to decide if your model works?". Read on to find out what a good answer looks like!
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  SAT2Vec: Word2Vec Versus SAT Analogies
  Could Word2Vec pass the SAT analogies section and get accepted to a good college? I take a pre-trained model and find out!