This is a good question. Setting the random seed to the same value guarantees that every time you run your model it will generate and consume exactly the same stream of random numbers, and therefore will get the same results. This is useful because when you are modifying or debugging the code, you can always compare your results against a baseline (the results with this random number seed) to check that you havenāt inadvertently changed anything.
Thanks very much! I think my problem was naivetĆ©: I was too willing to believe in the true randomness of the numbers chosen, which isnāt possible.
Iām still not completely clear on why weāre seeding with 42 in particular, but Iām just going to assume itās because itās the answer to life, the universe, and everything unless told otherwise.
Of course thatās why itās 42
! Trust your intuition on that one.
@radek are there going to be lectures 3 and 4 sections? seeing you list out the bullet points really helped me focus
I intended this to be just something for the first lecture, to get people started. I am preparing something that will help with reviewing some of the material for each lecture but realistically it is at least a couple of weeks from completion.
But can share an early version if there would be interest.
Yes, please!
Thank you for the suggestions!
I will add one that works for me. I am doing this course not the first time, so I try to accumulate knowledge from several lectures and than practice training models from scratch (I mean from a blank notebook, but for sure use imagenet pre-trained model, Transfer learning is the greatest tool!)
So now Iām watching lesson 6 and working on Kaggle competition https://www.kaggle.com/c/plant-pathology-2020-fgvc7 - it is pretty small dataset with several things that I have to change. It is classification, but augmentations that were described in 1 and 3 lessons may be enhanced with bigger crop and bigger rotation. It is not straight forward multiclass or multilabel task, so I want to train one network to classify āTrue falseā and another to classify the diseases (one, another or multiple). Another thing to work on is TTA, we have lots of computational time to get best results, so this is an opportunity to do some extended homework and learn about models ensemble.
For sure, it is always a lot of peeping into lessons notebooks, but after several notebooks from scratch it is a great feeling that you know exactly what to do to solve minimal tasks.
Happy learning everyone
Yes, interested
Hey Radek,
the stuff thatās been put together above above is fantastic.
Did you manage to put together a ābreakdownā per lecture? itād be cool to see if so
Not sure we ever had a separate topic for this, but an idea I had was to convert numerical or alphanumerical data into a quick response code and use that to train a model. I donāt have the complete process in my head yet, like how to separate train and validate data and perhaps the idea is not a feasible one, I would appreciate any comments, note I donāt have a specific application in mind just general thought here.
Thanks Radek for this thread,
I was really confused regarding Homework for chapter 1. turns out I have already done it.