Imagine you are building a model that can identify cats in photographs. You collect thousands of images, choose a neural network, train it, and check whether it predicts correctly. Everything seems straightforward, until someone asks, “But what exactly is the model doing?” This is where Marr’s three levels of analysis become useful. Instead of looking…
Category: Data Diaries
Short and insightful entries that bring data analysis to life. Discover trends, patterns, and meaning hidden in everyday data.
ML Models Only Speak Numbers. So How Do We Make Non-Numerics Predictive?
These “non-numerics” are often referred to as Categorical Features in machine learning. Almost every feature set we use for our ML model contains some categorical features that have the potential of becoming great predictors, but, because the model can only work with numerics, they often go underused. Encoding in machine learning give us a way…
How Information Travels Through Networks
A network is more than just a collection of connected objects. It is a system through which something can move, spread, and interact. So, understanding how information travels through networks is really about understanding how these connections shape its journey. You know how a signal fires from one neuron and, a few synapses later, it…
Why Linear Algebra Runs Machine Learning
Machine Learning Begins with Numbers We have always perceived machine learning as some mysterious black box. It takes in our heterogeneous data, and somehow it is able to see a pattern and give us the prediction based on that pattern. But, in reality, it is just a bunch of numbers playing around. And the best…
Why Your ML Model Performs Great on Training Data but Fails in Real Life
You trained a model, Accuracy- 98.1%. You test it on real data, the accuracy drops to 62%. And then you dare to blame it on the already known random nature of the real world data. While you don’t want to believe that the culprit is not the data, rather your own model. Let me introduce…
Bayes’ Theorem: The Way Evidence Reshapes Your Reality
Most of us like to think we’re rational creatures, calmly weighing facts before making decisions. Spoiler: we’re not. Our brains love to jump to conclusions, “My pizza’s late, must be the rain!” or “This test is positive, I definitely have the disease!” Enter Bayes’ theorem, a 250-year-old formula that acts like a truth serum for…
Conditional Probability: Revealing the Power of Context
A Slice of Pizza and a Slice of Probability Conditional probability is the math of shifting odds once new information enters the scene. How? Imagine this: You walk into a party.You see a person holding a slice of pizza. You wonder: “What’s the probability they’ll share it with me?” Same pizza, same person. The only…
Variance vs. Standard Deviation: The Drama Queens of Data
Picture This: You and your group of friends go out for coffee every weekend. Everyone usually orders a cappuccino for ₹150. But one week, someone shows up with a ₹500 matcha-latte-sprinkled-with-moon-dust. The bill soars, your wallet cries, and suddenly… your group’s “vibe” feels off. That, dear reader, is variance creeping in. Let’s break it down…
You’ve Been Living in a Bell Curve—and Here’s the Proof
Ever noticed how so many things in life seem to cluster around an average? Exam scores, human height, even measurement errors—they all seem to cuddle up into a pleasing, symmetrical hump. That’s the normal distribution, or what most of us lovingly call the bell curve. It’s not just a statistical quirk; it’s the poster child…