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
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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
Does Imagenet Pretraining work for Chest Radiography Images(COVID-19)?
We are at siege. A siege by an unknown enemy. An enemy with which we are befuddled. And unless you were living under a rock for the past couple of months(like Jared Leto), you know what I'm talking about - COVID-19. Whether you turn on the news, or scroll through social media, the majority of … Continue reading Does Imagenet Pretraining work for Chest Radiography Images(COVID-19)?
The Gradient Boosters VI(A): Natural Gradient
We are taking a brief detour from the series to understand what Natural Gradient is. The next algorithm we examine in the Gradient Boosting world is NGBoost and to understand it completely, we need to understand what Natural Gradients are. Pre-reads: I would be talking about KL Divergence and if you are unfamiliar with the … Continue reading The Gradient Boosters VI(A): Natural Gradient
The Gradient Boosters V: CatBoost
While XGBoost and LightGBM reigned the ensembles in Kaggle competitions, another contender took its birth in Yandex, the Google from Russia. It decided to take the path less tread, and took a different approach to Gradient Boosting. They sought to fix a key problem, as they see it, in all the other GBMs in the … Continue reading The Gradient Boosters V: CatBoost
