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What is AI, really? Lots of new tech startups have “AI” in their pitches these days. The media and press also use the words “AI” very broadly, and I’ve been guilty of it, too. “AI” is catch-all term for lots of technologies and techniques, and it isn’t always
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Yesterday I learnt about ROC curves. There’s an extension to this concept: the Area Under each Curve (AUC). If you have two different ROC curves from different models, finding the area under each curve will reveal which model performs better. Here’s an extract from the StatQuest book that
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Today, I went over ROC graphs. The acronym stands for receiver operating characteristic. This concept originates from signal detection theory during World War II. Radar operators wanted to ascertain how good they were at detecting enemy aircraft (“True Positives”) rather than confusing them for birds or some other noise (“False
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Logistic regression thresholds. A few weeks back I built a number of logistic regression models without quite appreciating the impact of the thresholds you set. A few pages into a chapter on assessing ML model performance helped me eliminate this gap today. It turns out that if you lower the
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Naive Bayes. This is a supervised machine learning classification technique that uses Bayes’ theorem to make classification predictions. Today I worked through a simple example of the technique and also found this explainer of Bayes’ theorem especially useful, along with an explanation by ChatGPT4. In machine learning Navie Bayes comes
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One of the great things about continuous learning in this age is the variety of educational material you can find. I’ve found that consuming YouTube videos, online courses, blogs, and books has helped me learn concepts more dynamically. For example, today I went over logistic regression again, this time
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Gradient descent is one of those machine learning concepts that looked intimidating to me. But as is the case with learning anything new, starting with the simplest version of a concept can provide the building blocks required for more complex understanding. Today I took such a step (pun intended for
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Today I reviewed linear regressions and also flicked through this fascinating paper that demonstrated how you can fool machine vision models with a pair of special glasses. In the example below, the man in the top left picture wore glasses that could fool neural networks into thinking he was actress
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Today I went through some concepts I’m already familiar with: sum of the squared residuals, mean squared error, and R-squared. These are important in assessing how good a machine learning model is. Tomorrow I’ll continue revising some key mathematical concepts before getting back into other applied ML basics.
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I went through a primer on histograms today, and reviewed Binomial and Poisson distributions. To my surprise, even these simple school maths concepts underly some of the most valuable machine learning classification algorithms we use today. Tomorrow I’ll continue working through key statistics methods used in machine learning.
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I’m travelling for the next 6 days without my laptop so I’ll keep the posts very short given I’m on mobile. No laptop means no code. However, I’ll work through this wonderful visual introduction to ML from the YouTube channel, StatQuest. Key takeaways from the first
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Precision & Recall (aka Sensitivity) On classification models for ML, I still confuse precision for recall and vice versa. So it was good today that I came across this handy chart that illustrates the differences. A good precision score is really important in areas where getting a false positive is