Well.
In previous videos, we introduced the theory behind collaborative filtering and Instacart grocery tips.
Today, we're going to show how to create and trend a model, how to exercise the trend model on a large
test set.
Quickly recall the meaning of the data, six user ID product ID count all purchases.
It means gems four truckloads, three times, even five times.
Here's create a data loader that size is 64.
They feel older, is the concept being in the PyTorch?
You can see it as a pop before you cook.
You need to put your ingredients in a pot.
So before you train the model, you'll need to load data in the data loader.
They find a motto by creating a love there.
Wide range is passed to normalize the prediction score in our case is zero and the next is five.
Plus the loss versus the running race.
Find optimum rates.
We'll use this low rates for model training.
Here we state a model following the one cycle policy.
We pass a hyper parameter valley, which is the optimum lending rate.
We found it can save the models sometimes, so I'm guessing.
I'm using a you and your golf take about two hours for three clocks.
After the trimming completed, we get the time stamp and export the trend model.
It's thousands of years later, the model training was completed with the help of a GPU from CoLab not
going to apply the trim model on test datasets.
We are curious about what users think about items they never loved before.
We randomly pick some user ID and put it in the user I.D. list.
You can change it to any user IDs you like.
How many orders do they have a legal set users seven has six to eight.
Next year has seventy four.
Number of items bought by a user versus the total number of products to date has set his.
We can see individual user.
All but a small set of products.
Approximately one thousand.
First, create a small test has set User ID and Project I.D..
And predictions galore from the model we're trying to score means how likely are used to buy a product
from a score, we can tell you this seven is more likely to buy the first product compared to the second
one.
Now, let's create a large test sets, these dataset has forty thousand products.
Users say they've never bought.
Applied a trend model to gather predictions for.
Plus the distribution of the school.
Seventy five percent of the products cost less than to climb for.
Well, the next is four point eight.
What are the products models think using seven would like it the most?
Let's check the top safety.
Avocado, milk, cheese and so on.
Let's see what did he or she lie before?
Whole milk cheese.
For the sake of eyes, let's visualize then.
OK, this is the word cloud items keep people.
And this cloud of top 15 items from prediction.
It is, but we can see organic milk, alcohol, chicken and cracker.
The prediction we have Milk Fuji, which is a type of alcohol, organic sparkling water.
This prediction really makes sense to me this past summer leaves a healthy life.
He or she eats organic foods, fruits, vegetables, dairies.
I think the customer would like to buy this items if the grocery store used our model and to send the
recommendation today we build a machine learning model to predict what products it was you never bought
but would like to buy.
I have some questions, and then maybe you have as well.
What about buyers of items?
Plus popular items have a higher buyers?
What are the latest features of users and items who are similar users from the model perspective?
See you in next video.
Victory.