Sign up to save your podcasts
Or
Share Episode 2: Watch the Weights of your Neural Network with this Amazing Diagnostic Tool
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Episode 2: Watch the Weights of your Neural Network with this Amazing Diagnostic Tool
WATCH THE WEIGHTs of the layers of your pre-trained or custom-trained models; peek inside the hidden layers to understand how your models make predictions.
Welcome to my latest podcast with Dr. Charles Martin, the founder of Calculation Consulting, on his team's data-free open-source diagnostics tool for deep learning called "WeightWatcher (w|w)" (https://weightwatcher.ai/).
In this video, he will walk us through the theory behind WeightWatcher (w|w) – the ultimate diagnostic tool for analyzing Deep Neural Networks (DNN) without needing access to any training or test data.
What's even more impressive is that this tool is based on the latest theoretical research into the fascinating topic of "Why Deep Learning Works?" using the new Theory of Heavy-Tailed Self-Regularization (HT-SR).
With WeightWatcher (w|w), you can quickly and efficiently diagnose potential problems with your DNN architecture and optimize its performance with minimal effort. So, whether you're a seasoned deep learning practitioner or a curious beginner, this tool is a must-have for anyone serious about their neural network performance.
So, sit back, relax, and get ready to learn all about WeightWatcher (w|w) and how it can help you improve your deep learning game – all without needing any data!
IMPORTANT LINKS:
✅ Dr. Charles Martin's LinkedIn: https://www.linkedin.com/in/charlesmartin14/
✅ WeightWatcher website: https://weightwatcher.ai/
✅ WeightWatcher Community: https://discord.com/invite/uVVsEAcfyF
PODCAST DETAILS:
✅ Spotify: https://open.spotify.com/show/53meKeZGCMQdVKUoWfnu5x?si=pRxpMGYpSUKaqjAhauTP8w
✅ Apple Podcast: https://podcasts.apple.com/ca/podcast/ai-podcast/id1689350103
✅ YouTube Podcast: https://youtube.com/playlist?list=PLV9VnT1svgQTRrlf_GopP0JiSUIpzOtjr
Follow me on:
✅ LinkedIn: https://www.linkedin.com/in/debangsha-sarkar/
✅ Twitter: https://twitter.com/DebangshaSarkar
✅ Youtube: https://www.youtube.com/@artificialliai
View all episodes
WATCH THE WEIGHTs of the layers of your pre-trained or custom-trained models; peek inside the hidden layers to understand how your models make predictions.
Welcome to my latest podcast with Dr. Charles Martin, the founder of Calculation Consulting, on his team's data-free open-source diagnostics tool for deep learning called "WeightWatcher (w|w)" (https://weightwatcher.ai/).
In this video, he will walk us through the theory behind WeightWatcher (w|w) – the ultimate diagnostic tool for analyzing Deep Neural Networks (DNN) without needing access to any training or test data.
What's even more impressive is that this tool is based on the latest theoretical research into the fascinating topic of "Why Deep Learning Works?" using the new Theory of Heavy-Tailed Self-Regularization (HT-SR).
With WeightWatcher (w|w), you can quickly and efficiently diagnose potential problems with your DNN architecture and optimize its performance with minimal effort. So, whether you're a seasoned deep learning practitioner or a curious beginner, this tool is a must-have for anyone serious about their neural network performance.
So, sit back, relax, and get ready to learn all about WeightWatcher (w|w) and how it can help you improve your deep learning game – all without needing any data!
IMPORTANT LINKS:
✅ Dr. Charles Martin's LinkedIn: https://www.linkedin.com/in/charlesmartin14/
✅ WeightWatcher website: https://weightwatcher.ai/
✅ WeightWatcher Community: https://discord.com/invite/uVVsEAcfyF
PODCAST DETAILS:
✅ Spotify: https://open.spotify.com/show/53meKeZGCMQdVKUoWfnu5x?si=pRxpMGYpSUKaqjAhauTP8w
✅ Apple Podcast: https://podcasts.apple.com/ca/podcast/ai-podcast/id1689350103
✅ YouTube Podcast: https://youtube.com/playlist?list=PLV9VnT1svgQTRrlf_GopP0JiSUIpzOtjr
Follow me on:
✅ LinkedIn: https://www.linkedin.com/in/debangsha-sarkar/
✅ Twitter: https://twitter.com/DebangshaSarkar
✅ Youtube: https://www.youtube.com/@artificialliai