Wiki: Lesson 5

(Quan Tran) #46

If anyone is interested, I redid this lesson from scratch here but added a few tweak, such as fitting a model with a learner (so we can use learning rate decay/cosine annealing and reach good score with fewer epochs), clipping output range, using full movielens dataset …


what is the intuition behind the above statement, i am having trouble understanding why we would decrease weight decay if there is a lot of variation in the gradient

(Jeremy K) #48

So in the part of the lecture where we went to excel and did the x,y slope intercept equation learning project, was that a cnn?

(Jeremy K) #49

Also when we make the crosstab we do:

pd.crosstab(crosstab.userId, crosstab.movieId, crosstab.rating, aggfunc=np.sum)

Why do we need the np.sum? I understand it adds values but what values are there to add? All we are doing is showing what rating a user gave a movie?


I don’t really get the part about adding bias in EmbeddingDotBias class.

My understanding is that in the line:

res = um + self.ub(users).squeeze() + self.mb(movies).squeeze()

um is not a matrix but a vector and squeeze() does not do broadcasting, but removes a dimension of size 1 (bias was a matrix n x 1 so it has to be converted to a vector so it can be added to um).

But in the video it’s said that squeeze() is for broadcasting, which doesn’t make sense to me.

(Massimo) #51

An in-depth but nice explanation of momentum is here:

(Dave Smith) #52

Hi everyone,

To help me better understand the optimization math (without using Solver or macros), I re-created the movie recommender spreadsheets (Excel + Google Sheets) and wrote a blog post about it. Hopefully this helps some of you a bit if you’re feeling stuck (like I was).

Blog link: Netflix and Chill: Building a Recommendation System in Excel
Link to spreadsheets on Google Drive

Frankly, I still feel like a little kid watching a magic trick each time I see the model learn haha! :mage::100::exploding_head:

Key differences vs. lesson spreadsheets:

1. Gradient descent done using formulas (not Solver, no macros) - Used step-by-step formulas (full derivations…) in batch gradient descent so you can see the math.

2. Added hyperparameter inputs as drop-downs - You can play around with the learning rate, L2 regularization penalty, initial weights, etc…to understand the impact on your errors.

3. Split data into training vs. test sets - This allows you to see the importance of regularization vs. overfitting

4. Added L2 regularization penalty - Helps the model generalize better on the test data

5. Added latent factor visualization graph in Excel


(Karl) #53

Trying to understand what’s going on here

from sklearn.decomposition import PCA
pca = PCA(n_components=3)
movie_pca =

Why do we choose to fit the transpose of the movie embedding, rather than the embedding itself? Why do we take the components from the fit PCA rather than transforming the matrix?

I’ve been playing around with different variants and looking at the dimensionality

test1 =
test1.shape = (3, 3000)

test2 =
test2.shape = (3, 50)

test3 = pca.fit_transform(movie_emb)
test3.shape = (3000, 3)

If someone can illuminate what’s going on here and why we use pca the way we do, I would appreciate it.