Visualizing Neural Networks with the Grand Tour
distill.pub · 10,344 words · saved by 1 readers
By focusing on linear dimensionality reduction, we show how to visualize many dynamic phenomena in neural networks.
--> Visualizing Neural Networks with the Grand Tour Visualizing Neural Networks with the Grand Tour Visualizing Neural Networks with the Grand Tour style="background-color:rgb(247, 247, 247);" --> --> --> The Grand Tour in action. This visualization shows the behavior of the final 10-dimensional layer of a neural network as it is trained on the MNIST dataset. With this technique, it is possible to see interesting training behavior. For example, the network appears to learn to classify digits 1 and 7 in an almost discontinuous manner, after training epochs 14 and 21 respectively. --> Authors Af
saved by
related reading
- Feature Visualizationdistill.pub
- Neural Networks, Manifolds, and Topology -- colah's blogcolah.github.io
- A Gentle Introduction to Graph Neural Networksdistill.pub
- Toy Models of Superpositiontransformer-circuits.pub
- Feature Visualizationdistill.pub
- Dimensionality reduction in neural data analysisxcorr.net
- Neural networks and deep learningneuralnetworksanddeeplearning.com
- Modular Manifolds - Thinking Machines Labthinkingmachines.ai
- Visualizing Representations: Deep Learning and Human Beings - colah's blogcolah.github.io
- nn-notes.pdfboris-hanin.github.io
- Visualizing Algorithmsbost.ocks.org
- What Would Non-Linear Features Actually Look Like? — Liv Gortonlivgorton.com