Machine learning and data science are rapidly evolving fields. Keep on top of the latest and most fascinating advancements with this podcast.
ML & DS Papers On The Go
Claim This Podcastby Andre Ye
4 episodes
Updated Daily
Accepts GuestsHas Sponsors
Podcast Overview
Machine learning and data science are rapidly evolving fields. Keep on top of the latest and most fascinating advancements with this podcast.
Language
🇺🇲
Publishing Since
2/8/2021
1 verified contact email on file for ML & DS Papers On The Go
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Recent Episodes
February 26, 2021
Ep. 3 | Our Image Recognition Models Are Too Vulnerable: Strategies and Defenses Against Adversarial Attacks
<p>Adversarial attacks can completely screw up an image recognition model's performance - without humans noticing a single difference. What are these attacks and how can we defend against them? - we'll explore papers and perspectives on this topic in this episode.</p> <p>View article summaries and links at the website here: http://papers-on-the-go.ml</p>
February 20, 2021
Ep. 2 | Deep Learning’s Million Dollar Question Pt. 2: Pruning, Lottery Tickets, and High-Performance Random Networks
<p>How can a completely random neural network attain high performance? The answer lies in a very fascinating hypothesis - the Lottery Ticket Hypothesis - and allows us to make some very intriguing paradigm shifts in how we view neural network generalization.</p> <p>View article summaries and links at the website here: http://papers-on-the-go.ml</p>
February 12, 2021
Ep. 1 | Deep Learning’s Million-Dollar Question Pt. 1: Rethinking Generalization and Why We Should Care
<p>Why don't neural networks overfit when they have so many parameters? It's deep learning's million-dollar question and one we'll try to get a few steps closer to answering.</p> <p>We'll get an introduction to the million-dollar question, get an understanding for the surprising amount of information neural networks can carry, and explore the discussion and research around Deep Double Descent phenomena.</p> <p><strong>Links to the papers discussed: </strong><a href="https://arxiv.org/pdf/1611.03530.pdf">Understanding Deep Learning Requires Rethinking Generalization</a>: arxiv.org/pdf/1611.03530.pdf || <a href="https://arxiv.org/pdf/1812.11118.pdf">Reconciling modern machine learning practice and the bias-variance trade-off</a>: arxiv.org/pdf/1812.11118.pdf || <a href="https://arxiv.org/pdf/1901.01608.pdf">Scaling description of generalization with number of parameters in deep learning</a>: arxiv.org/pdf/1901.01608.pdf</p> <p><strong>Reach out to me </strong>at my email, ye-andre@outlook.com, or at my <a href="//www.linkedin.com/in/andre-ye/">LinkedIn</a>, www.linkedin.com/in/andre-ye/.</p>
4 total episodes available
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- What is ML & DS Papers On The Go?
- How often does this podcast release new episodes?
This podcast updates daily.
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This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
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No, this podcast does not typically feature guests.
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