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…
Tag: machine learning
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 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…