I just finished a linear algebra/differential equations class, so I am familiar with how machine learning works under the hood. I need some help with the terminology and how the numbers are dealt with. If I draw comparisons to what I learned in calculus and LA/Diff Eq that are wrong or bad let me know. I am try to learn by association as it’s the most effective way for me.
If I have, lets say a furnace, and it has a pressure sensor, a temperature sensor and an outside skin sensor(easiest for me to picture as all the variables are directly related.) this would produce a vector of 3 variables, let’s say [100, 200, 150].
If that furnace had 3 zones of sensors, it would produce a matrix [100,200,150; 120, 220, 160; 110,240,220] (using semicolon between rows because I took a Matlab class last semester).
How would I enter this in a machine learning model? Would the matrix be considered 1 batch? Are the measurements (matrix elements) what are called features in this forum? If I lost the temp sensor, that would become my y variable and the other 2 would be x_1 and x_2, so how do I solve for a single y given 2 x as input? I saw were the input dimensions are batch, sequence length and feature. Am I wrong or is batch the whole matrix at time t or is it a group of matrices, say from t-5 to t, a 3d matrix with the third dimension as time? ? Sequence length is the second input. Is that the maximum length of any 1 row of the matrix or is it the whole matrix? The second dimension or number of columns? From what I read, the 3rd input is number of features, I believe this is the number of elements in the matrix, is that right? Any help would be very appreciated. Also, I have used some example code and tried to plug multiple inputs(x’s) into the model and tried to get one y out and it complains about the input and output being different sizes. I have seen some example of multi input to single output, but I don’t know what’s going on and am trying to understand it.
Thank you