In this video we explain how to employ Neural Networks trained without supervision to identify features in Charge Stability Diagrams. We explain the notion of latent space, and show how to analyze it.
- Neural networks can be trained even without labels
- Autoencoder is an example of a neural network generative model that can be trained on a label-free dataset
- The latent space of an autoencoder can be used to identify relevant features in a dataset
True or False: The Encoder network in an Autoencoder is responsible for mapping an input to a latent space.
More About Clustering - PCA and more: https://ml-lectures.org/docs/structuring_data/ml_without_neural_network.html
Kernel principal component analysis: https://link.springer.com/chapter/10.1007/BFb0020217
More about Unsupervised Learning - Autoencoders and more: https://ml-lectures.org/docs/unsupervised_learning/ml_unsupervised.html
Reducing the Dimensionality of Data with Neural Networks: Book Chapter: https://ml-lectures.org/docs/structuring_data/ml_without_neural_network.html