Reliable Machine Learning @SirajRaval
Reliable Machine Learning  @SirajRaval
Uploaded January 2020 | Updated September 2026, 2 weeks ago
Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that's not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it's important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I'll explain the work of these researchers, we'll write some code, do some math, do some visualizations, and by the end I'll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!

Likelihood Ratios for Out-of-Distribution Detection paper:
arxiv.org/pdf/1906.02845.pdf

The researcher's code:
github.com/google-research/google-research/tree/master/genomics_ood

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My notes from the paper (the images are from across the Web):
colab.research.google.com/drive/1BKX05LlnqRY74wzvuER9OS6yM9QemJNt

Visualization script:
gist.github.com/llSourcell/21a29a43a3398d301f51328dbbaa1822

Generative Modeling:
youtube.com/watch?v=FZBFV7xfGaY
youtube.com/watch?v=HyuBTMaKFmU

Defense Against Adversarial Attacks:
youtube.com/watch?v=JjlV62_kGUc

LSTM Networks Explained:
youtube.com/watch?v=9zhrxE5PQgY

Recurrent Networks Explained:
youtube.com/watch?v=BwmddtPFWtA

Are you a total beginner to machine learning? Watch this:
youtube.com/watch?v=Cr6VqTRO1v0

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Reliable Machine Learning

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