AI Logbook

Why do we go through all the trouble of building matrices? Parallelization.

Why is linear dependence a problem for Artificial Intelligence? In data science, this concept is called Multicollinearity, and it destroys models.

Why is transposition so critical in machine learning? It is the ultimate "adapter cable" for matrix multiplication.

Why do we need to multiply two matrices together? This is the exact mathematical operation that unlocks Deep Learning.

In The Capstone: Object-Oriented ML Architecture, to make predictions for 3 houses, we had to run our predict method 3 separate times. In Python, this requires a for loop, which is notoriously slow for large datasets.

Why do we need these operations? In machine learning, we rarely use raw data exactly as it comes to us. We need to preprocess it so our neural networks can digest it effectively.

In Week 1, we built an engine that could look at a single house (a vector) and predict a price. But in the real world, we don't train AI on one house at a time; we train it on thousands or millions of houses simultaneously.

In standard software engineering, you separate your data from your business logic. In Machine Learning, we separate our Data from our Model.

We now have all the individual pieces of an Artificial Intelligence engine:

1. The Data: Features translated into numbers.
2. The Model: Weights and Bias combined to make predictions.
3. The Evaluation: The Loss Function from Measuring Error: How Wrong is Our Model? to measure how wrong we are.

In machine learning, the formula we use to measure error is called the Loss Function (or Cost Function).