C1W1_Assignment.ipynb : price_in_hundreds_of_thousands

[ai_curious]

In the future, you might give the forum search a try. Here’s an example…

https://community.deeplearning.ai/search?q=Price%20scaling

One of the threads returned by that is…

Assignment 1: How to normalize input arrays to feed the network

TF1 W1 the first exercise is a super simple model to predict house price from number of rooms. The ys values are initially provided in units of dollars, so the house price is order of magnitude 10^6. Meanwhile, the number of rooms is order of magnitude 10^0. I humbly suggest that Spectral Normalization is not the optimal approach or appropriate technology for this level of problem (nor for a learner on the first week of the first TF class). The simple solution is to divide the ys training i…

which seems relevant to your question. There are others, for example…

Week 1 Exercise 1 " value in the `hundreds of thousands`"

Hello @Ann_Maria. There are two ideas in my responses that might be helpful to you. One is that the grader is asking for the output to be expressed in specific units of measure - hundreds of thousands of dollars. In the real world, $400,000 (four hundred thousand dollars) 400 * $1,000 (four hundred thousand dollars) and 4 * $100,000 (four hundred thousand dollars) all buy the same house. Only the units of measure differ - just as an object’s weight is the same regardless if you express it i…

Hope it helps

EDIT: after reviewing the updated exercise notebook on github I see that the narrative markup is much more explicit than it used to be about the units for housing price (hundreds of thousands) than it once was. In the old days, the problem was almost always that models were predicting near 400 instead of near 4. That is what the threads I linked above discuss. My understanding now is that is not the issue raised by the OP in this thread. Rather, it seems to be an implicit requirement that the number of rooms training data vector is ordered and starts with 1.