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The Math Behind Fine-Tuning Deep Neural Networks
Towards Data Science
Dive into the techniques to fine-tune Neural Networks, understand their mathematics, build them from scratch, and explore their...
7 months ago
Firas Laakom: Unlocking the Secret to Neural Network Success with Feature Diversity
Tampereen korkeakouluyhteisö
In a world where the power of neural networks is reshaping industries and revolutionizing technology, understanding how these networks learn...
8 months ago
Replica theory shows deep neural networks think alike
Tech Xplore
This data visualization shows the geodesic training trajectory of different deep neural networks as they advance from total ignorance to full certainty.
8 months ago
Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data
Nature
We develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of...
40 months ago
A lifted Bregman formulation for the inversion of deep neural networks
Frontiers
We propose a novel framework for the regularized inversion of deep neural networks. The framework is based on the authors' recent work on training feed-forward...
18 months ago
Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings
Nature
Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing...
11 months ago
Top 8 Free Must-Read Books on Deep Learning
KDnuggets
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind,...
79 months ago
Ablation Testing Neural Networks: The Compensatory Masquerade
Towards Data Science
In a similar fashion to how a person's intellect can be stress tested, Artificial Neural Networks can be subjected to a gamut of tests to evaluate how...
10 months ago
A review of some techniques for inclusion of domain-knowledge into deep neural networks
Nature
This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks.
33 months ago
Dropout in Neural Networks. Dropout layers have been the go-to… | by Harsh Yadav
Towards Data Science
Dropout layers have been the go-to method to reduce the overfitting of neural networks. It is the underworld king of regularisation in the modern era of deep...
28 months ago