Time Series Imputation using Matrix Recovery
Missing values in time series are not straightforward to deal with. In my book on time series forecasting, Modern Time Series Forecasting with Python, I talk about a few techniques, right from thinking about the missing values in the right way to some algorithmic techniques to deal with missing data (like seasonal interpolation). Originally, that…
Neural Networks – A Linear Algebra Perspective
Unlike my regular blog posts, this one is going to be a very short one – crisp and to the point. Deep Learning has been touted as the next big thing in data analytics and things have gotten so hyped that a lot of people (even practitioners) have started to consider it as magic. I’m…
Mixture Density Networks: Probabilistic Regression for Uncertainty Estimation
Uncertainty is all around us. It is present in every decision we make, every action we take. And this is especially true in business decisions where we plan for the future. But in spite of that, all of our predictive models that we use in business ignore uncertainty. Suppose you are the manager of the…
Neural Oblivious Decision Ensembles(NODE) – A State-of-the-Art Deep Learning Algorithm for Tabular Data
Deep Learning brought about revolutions in many machine learning problems from the field of Computer Vision, Natural Language Processing, Reinforcement Learning, etc. But tabular data still remains firmly under classical machine learning algorithms, namely the gradient boosting algorithms(I have a whole series on different Gradient Boosting algorithms, if you are interested). Intuitively, this is strange,…
PyTorch Tabular – A Framework for Deep Learning for Tabular Data
It is common knowledge that Gradient Boosting models, more often than not, kick the asses of every other machine learning models when it comes to Tabular Data. I have written extensively about Gradient Boosting, the theory behind and covered the different implementations like XGBoost, LightGBM, CatBoost, NGBoost etc. in detail. The unreasonable effectiveness of Deep…
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Deep & Shallow 