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 … Continue reading Mixture Density Networks: Probabilistic Regression for Uncertainty Estimation
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, … Continue reading Neural Oblivious Decision Ensembles(NODE) – A State-of-the-Art Deep Learning Algorithm for Tabular Data
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 … Continue reading PyTorch Tabular – A Framework for Deep Learning for Tabular Data
How to Train and Deploy Custom AI-Generated Quotes using GPT2, FastAPI, and ReactJS
The Problem Good quotes help make us stronger. What is truly inspiring about quotes is not their tone or contentedness but how those who share them reflect life experiences that really serve others. I didn't write the above quote about quotes(Quote-ception), but an AI model I trained did. And it says it better than I … Continue reading How to Train and Deploy Custom AI-Generated Quotes using GPT2, FastAPI, and ReactJS
Intermittent Demand Forecasting with Deep Renewal Processes
Let's face it, anyone who has worked on Time Series Forecasting problems in the retail, logistics, e-commerce etc. would have definitely cursed that long tail which never behaves. The dreaded intermittent time series which makes the job of a forecaster difficult. This nuisance renders most of the standard forecasting techniques impractical, raises questions about the … Continue reading Intermittent Demand Forecasting with Deep Renewal Processes
Forecast Error Measures: Intermittent Demand
In the previous few blog posts, we've seen all the popular forecast measures used in practice. But all of them were really focused on smooth and steady time series. But there is a whole different breed of time series in real life - intermittent and lumpy demand. Source Casually, we call intermittent series as series … Continue reading Forecast Error Measures: Intermittent Demand
Forecast Error Measures: Scaled, Relative, and other Errors
Following through from my previous blog about the standard Absolute, Squared and Percent Errors, let's take a look at the alternatives - Scaled, Relative and other Error measures for Time Series Forecasting. Taxonomy of Forecast Error Measures Both Scaled Error and Relative Error are extrinsic error measures. They depend on another reference forecast to evaluate … Continue reading Forecast Error Measures: Scaled, Relative, and other Errors
Forecast Error Measures: Understanding them through experiments
Measurement is the first step that leads to control and eventually improvement.H. James Harrington In many business applications, the ability to plan ahead is paramount and in a majority of such scenario we use forecasts to help us plan ahead. For eg., If I run a retail store, how many boxes of that shampoo should … Continue reading Forecast Error Measures: Understanding them through experiments
The Gradient Boosters VII: Battle of the Boosters
We have come a long way in the world of Gradient Boosting. If you have followed the whole series, you should have a much better understanding about the theory and practical aspects of the major algorithms in this space. After a grim walk through the math and theory behind these algorithms, I thought it would … Continue reading The Gradient Boosters VII: Battle of the Boosters
The Gradient Boosters VI(B): NGBoost
The reign of the Gradient Boosters were almost complete in the land of tabular data. In most real world as well as competitions, there was hardly a solution which did not have at least one model from one of the gradient boosting algorithms. But as the machine learning community matured, and the machine learning applications … Continue reading The Gradient Boosters VI(B): NGBoost
