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Manu Joseph deep learning

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 … Continue reading Mixture Density Networks: Probabilistic Regression for Uncertainty Estimation

Manu Joseph deep learning

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

Manu Joseph deep learning

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

Manu Joseph deep learning, natural language processing

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

Manu Joseph deep learning, time series

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

Manu Joseph time series

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

Manu Joseph time series

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