I’ve been interested in learning about topological data analysis, haven’t dug in too deep yet, but it definitely looks like an interesting direction to zig in while the field at large zags with ever larger deep learning architectures.
UMAP has already demonstrated its efficacy as a tool in any data scientist’s belt. Ayasdi and Gunnar Carlson’s work is certainly interesting, but unsure how much business value it can actually unlock. Seems like there is also opportunity to draw inspiration from the applied category theory crew (Spivak, Fong etc) to use some CT tools to approach data science from a fresh perspective.
Some of the research coming out is interesting, but as a practitioner I’m more interested in seeing how TDA can add differentiated value in a business context. Interested to hear where people see the field moving next.
UMAP has already demonstrated its efficacy as a tool in any data scientist’s belt. Ayasdi and Gunnar Carlson’s work is certainly interesting, but unsure how much business value it can actually unlock. Seems like there is also opportunity to draw inspiration from the applied category theory crew (Spivak, Fong etc) to use some CT tools to approach data science from a fresh perspective.
Some of the research coming out is interesting, but as a practitioner I’m more interested in seeing how TDA can add differentiated value in a business context. Interested to hear where people see the field moving next.