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GenAI Intensive Course Google and Kaggle — My Takeaway – Day 3: Agents
This is my takeaway on the third day of Google’s Intensive GenAI Course. Agents Components of Agents More information I posted a more detailed summary on GitHub. Thank you for reading! Check out my GitHub for other projects. You can also read the story on Medium. Feel free to reach out on LinkedIn. Continue Reading
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GenAI Intensive Course Google and Kaggle — My Takeaway – Day 2: Embedding
This is my takeaway on the second day of Google’s Intensive GenAI Course. Embedding Introduction Why using embedding? From Input to Embedding Types of Embedding More information I posted a more detailed summary on GitHub. Thank you for reading! Check out my GitHub for other projects. You can also read the story on Medium. Feel free to reach… Continue Reading
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GenAI Intensive Course Google and Kaggle — My Takeaway – Day 1: Foundations of LLM and Prompt Engineering
This is my takeaway on the first day of Google’s Intensive GenAI Course. Foundations of LLM Prompt Engineering More information I posted a more detailed summary on GitHub. Thank you for reading! Check out my GitHub for other projects. You can also read the story on Medium. Feel free to reach out on LinkedIn. Continue Reading
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Start of a new series – #BookLog
I have decided to start a new series – Booklog, where I will share the books I have read and what I think about them. Will it be an extensive book review? Certainly not – Some articles will be long, some will be short. Will it be fun? I hope so. Will it always be… Continue Reading
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The Great Regression — with Python: Fixed Effect Regressions (Part I)
In my last post, I discussed the experimental design for comparing the effect of an event on two groups (treatment and control groups). The example showed no significant effect, i.e., no difference between the groups, for the intervention discussed. But is this true? The result could be correct and there is indeed no detectable difference… Continue Reading
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The Great Regression — with Python: Difference-in-Differences Regressions
Motivation In the last article, I discussed how to implement a simple model to judge the (linear) relationship between two or more variables. In this article, I will walk you through a simple yet powerful tool for judging the difference between groups. In an ideal world, we would like to conduct a random experiment in… Continue Reading
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The Great Regression — with Python: Linear and Multilinear Regressions
For many real-life questions, we are interested in the relationship between two (or more) parameters. Typical questions are: This article shows how to approach such questions using the simplest model. A model that assumes a linear relationship between the outcome (the thing that is affected) and the regressor(s) (the thing that affects the outcome). Theoretical… Continue Reading
