Please Stop Explaining Black Box Models and Use Interpretable Models Instead,

This lecture focuses mainly on the face that interpretable models can be created to be as accurate as black box models, both for tabular and raw data. The video has a focus on 2 techniques for interpretable neural networks: case-based reasoning and neural disentanglement.

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Interpretable Neural Networks for Computer Vision: Clinical Decisions that are Aided, not Automated

Nobel Conference - Rudin

Nobel Conference - Rudin

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Intuition behind Mamba and State Space Models | Enhancing LLMs!

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AlphaFold - The Most Useful Thing AI Has Ever Done

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#047 Interpretable Machine Learning - Christoph Molnar

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What Is System Identification? | System Identification, Part 1

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Clara Mattei: capitalism is not natural - it’s enforced

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Physicist Brian Cox explains quantum physics in 22 minutes

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Think Faster, Talk Smarter with Matt Abrahams

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Transformers, the tech behind LLMs | Deep Learning Chapter 5

The World's Most Important Machine

The World's Most Important Machine

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System Design Course – APIs, Databases, Caching, CDNs, Load Balancing & Production Infra

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6 Tips on Being a Successful Entrepreneur | John Mullins | TED

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The Strange Math That Predicts (Almost) Anything

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Yann LeCun's $1B Bet Against LLMs [Part 1]

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How To Think SO Clearly People Assume You're Brilliant

Inference, Diffusion, World Models, and More | YC Paper Club

Inference, Diffusion, World Models, and More | YC Paper Club

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But what is a neural network? | Deep learning chapter 1

Interpreting Black-Box Supervised Learning Models Via Accumulated Local Effects

Interpreting Black-Box Supervised Learning Models Via Accumulated Local Effects

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The Tiny Donut That Proved We Still Don't Understand Magnetism