First of all, thank you for taking the time to write this comment, I really appreciate the effort you put in!
- The problem you desribed is not really a supervised learning problem.
The exact assignment was to build a wine classifier which recognizes the wine label based on 13 features. From what I have learned so far is that we have 3 main types of Machine learning: supervised, unsupervised and reinforcement learning. Given that we know the labels of the wine and we have 13 attributes, doesn’t that make this a supervised classification problem? I will talk about training in point 3.
2. The input layer should have 13 layers instead of 178.
This is correct, thank you for pointing this out. I must have confused it when I was watching 3Blue1Brown’s video on Neural Networks. His input data consisted of 784 nodes which is 28x28 pixels.
I am aware of the problem of overfitting and the need to have a train, dev and test set. At first I considered mentioning the problem of overfitting and the fact that the accuracy is not reliable because not using a test set in the last paragraph. But in the end I decided not to mention it because I was afraid it could be overwhelming for people reading about Neural Nets for the first time and seeing all these new concepts.
Regarding the actual problem and overfitting; because this was the first week of the bootcamp, for the sake of keeping things simple, we did not have a training, dev and test set yet. These were introduced in week 2 and implemented this in our models from then on.
Thank you for wishing me luck! It is a lot of fun & learning a lot. :)