Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide
Indeed, RL provides useful solutions to a variety of sequential decision-making problems. Temporal-Difference Learning (TD learning) methods are a popular subset of ...
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How to Improve the Efficiency of Your PyTorch Training Loop
models isn’t just about submitting data to the backpropagation algorithm. Often, the key factor determining the success or failure of a ...
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Beyond ROC-AUC and KS: The Gini Coefficient, Explained Simply
discussed about classification metrics like ROC-AUC and Kolmogorov-Smirnov (KS) Statistic in previous blogs. In this blog, we will explore another ...
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Learning Triton One Kernel At a Time: Vector Addition
, a little optimisation goes a long way. Models like GPT4 cost more than $100 millions to train, which makes ...
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Why MissForest Fails in Prediction Tasks: A Key Limitation You Need to Keep in Mind
The of this article is to explain that, in predictive settings, imputations must always be estimated on the training set ...
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This AI-Powered Robot Keeps Going Even if You Attack It With a Chainsaw
A four-legged robot that keeps crawling even after all four of its legs have been hacked off with a chainsaw ...
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The Kolmogorov–Smirnov Statistic, Explained: Measuring Model Power in Credit Risk Modeling
days, people are taking more loans than ever. For anyone who wants to build their own house, home loans are ...
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Analysis of Sales Shift in Retail with Causal Impact: A Case Study at Carrefour
Disclosure: I work at Carrefour. The views expressed in this article are my own. The data and examples presented are ...
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Building a Unified Intent Recognition Engine
systems, understanding user intent is fundamental especially in the customer service domain where I operate. Yet across enterprise teams, intent ...
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A Visual Guide to Tuning Gradient Boosted Trees
Introduction My previous posts looked at the bog-standard decision tree and the wonder of a random forest. Now, to complete ...
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