<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I am kind of desperate for help right now on my current project. I'll try and be as clear as possible.</p> <p>I'm working on
<!-- SC_OFF --><div class="md"><p>I built a calibrated predictive model on point-in-time fundamentals and two results surprised me enough to write up. Sharing the project and the
<!-- SC_OFF --><div class="md"><p><strong>Hello all, Welcome to my free ML bootcamp.</strong></p> <p>In Intro ML Bootcamp (5/22), we discuss Uncertainty. </p> <p>In Machine
<!-- SC_OFF --><div class="md"><p>i'm trying to train an ASR model using the <a
<!-- SC_OFF --><div class="md"><p>I plotted the Expected Calibration Error (ECE) for an LLM (Gemini 2.5 Pro) forecasting 30 different real-world time-series targets over 38 days
<!-- SC_OFF --><div class="md"><p>The BDH (Dragon Hatchling) paper (arXiv:2509.26507) describes a Hebbian synaptic plasticity mechanism where model weights update during
<!-- SC_OFF --><div class="md"><p>​</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find
<!-- SC_OFF --><div class="md"><p>Been a UFC fan for years and recently had some spare time to build a web app that uses Machine Learning to predict which fighter will win as a
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