## Time Series for scikit-learn People (Part II): Autoregressive Forecasting Pipelines

In this post, I will walk through how to use my new library skits for building `scikit-learn`

pipelines to fit, predict, and forecast time series data.

In this post, I will walk through how to use my new library skits for building `scikit-learn`

pipelines to fit, predict, and forecast time series data.

When I first started to learn about machine learning, specifically supervised learning, I eventually felt comfortable with taking some input $\mathbf{X}$, and determining a function $f(\mathbf{X})$ that best maps $\mathbf{X}$ to some known output value $y$. Separately, I dove a little into time series analysis and thought of this as a completely different paradigm. In time series, we don't think of things in terms of features or inputs; rather, we have the time series $y$, and $y$ alone, and we look at previous values of $y$ to predict future values of $y$.

Hey, remember when I wrote those ungodly long posts about matrix factorization chock-full of gory math? Good news! You can forget it all. We have now entered the Era of Deep Learning, and automatic differentiation shall be our guiding light.

I've been making my way through the recently released Deep Learning textbook (which is absolutely excellent), and I came upon the section on Universal Approximation Properties. The Universal Approximation Theorem (UAT) essentially proves that neural networks are capable of approximating any continuous function (subject to some constraints and with upper …

After the long series of previous posts describing various recommendation algorithms using Sketchfab data, I decided to build a website called Rec-a-Sketch which visualizes the different algorithms' recommendations. In this post, I'll describe the process of getting this website up and running on AWS with nginx and gunicorn.

To close out our series on building recommendation models using Sketchfab data, I will venture far from the previous posts' factorization-based methods and instead explore an unsupervised, deep learning-based model. You'll find that the implementation is fairly simple with remarkably promising results which is almost a smack in the face to all of that effort put in earlier.

In this post we're going to do a bunch of cool things following up on the last post introducing implicit matrix factorization. We're going to explore Learning to Rank, a different method for implicit matrix factorization, and then use the library LightFM to incorporate side information into our recommender. Next, we'll use scikit-optimize to be smarter than grid search for cross validating hyperparameters. Lastly, we'll see that we can move beyond simple user-to-item and item-to-item recommendations now that we have side information embedded in the same space as our users and items. Let's go!

Last post I described how I collected implicit feedback data from the website Sketchfab. I then claimed I would write about how to actually build a recommendation system with this data. Well, here we are! Let's build.

*tl;dr -> I collected an implicit feedback dataset along with side-information about the items. This dataset contains around 62,000 users and 28,000 items. All the data lives here inside of this repo. Enjoy!*

Last post I talked about how data scientists probably ought to spend some time talking about optimization (but not too much time - I need topics for my blog posts!). While I provided a basic optimization example in that post, that may have not been so interesting, and there definitely wasn't any machine learning involved.

Next →
Page 1 of 3