Mapping Photography Portfolios by Visual Similarity With Machine Learning

I scraped the profile of [@withluke](https://www.instagram.com/withluke/?hl=en) and created these visualizations (check the web viewer), optimized to load around
_**2250**_ images. The methods used were inspired by: - 3D View — [Google Arts Experiments TSNE Viewer](https://artsexperiments.withgoogle.com/tsnemap/) - Spherical View — [IsoMatch: Creating Informative Grid Layouts](https://gfx.cs.princeton.edu/pubs/Fried_2015_ICI/index.php) - Grid View — [IsoMatch: Creating Informative Grid Layouts](https://gfx.cs.princeton.edu/pubs/Fried_2015_ICI/index.php) Prior to this, I built a Python implementation, which you can find here: [Grid-Layout-Image-Artwork](https://sasikaa073.github.io/projects/5-t_SNE/). This pipeline can be replicated for any image dataset (including any Instagram profile) to create a grid layout using PCA or non-linear dimensionality reduction methods such as t-SNE or UMAP.