Covers: theory of Topological Convolutional Neural Networks

- How can we use the latent manifolds of image data in the structure of convolutional neural networks?

Read sections 2 through 5

Gunnar Carlsson, Ephy R. Love, Benjamin Filippenko, Vasileios Maroulas

Nour Fahmy**Total time needed: **~20 minutes

- Learning Objectives
- Introduction to topological deep learning, and more specifically topological convolutional neural networks.
- Potential Use Cases
- TCNNs learn faster, on less data, with fewer learned parameters, and with greater generalizability and interpretability than conventional CNNs.
- Target Audience
- INTERMEDIATEML practitioners and enthusiasts who are interested in developments of TDA

Go through the following **annotated items** *in order*:

OTHER 1. Convolutional Neural Network

- What is a convolutional neural network?

10 minutes

PAPER 2. Topological Approaches to Deep Learning

- How can we use topology to understand the internal states of a CNN?

10 minutes

ARTICLE 3. Topological Deep Learning

- How can we use the latent manifolds of image data in the structure of convolutional neural networks?

20 minutes

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