So difficult to manage some time on this Day 4 was a long day for me, just got 15mins before bed to quickly skim through the video "multiple features" and "vectorization part 1". Day 5, a longer day than yesterday... went to urgent care in morning... then back-to-back meeting after come back.... lunch... back-to-back meeting again... need to step out again... Anyway, that's life. Multiple features (variables) and Vectorization In "multiple features", Andrew uses crispy language explained how to simplify the multiple features formula by using vector and dot product. In "Part 1", Andrew introduced how to use NumPy to do dot product and said GPU is good at this type of calculation. Numpy function can use parallel hardware (like GPU) to make dot product fast. In "Part 2", Andrew further introduced why computer can do dot product fast. He used gradient descent as an example. The lab was informative, I walked through all of them though I've known most of them before. More linkes about Vectorization can be find here. Questions for helping myself learning I created the following questions to test my knowledge later. What is x(4)1 in above graph?   Ps. feel free to check out the series of my Supervised Machine Learning journey.