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Latent Space Visualisation: PCA, t-SNE, UMAP | Deep Learning Animated

In this video you will learn about three very common methods for data dimensionality reduction: PCA, t-SNE and UMAP. These are especially useful when you want to visualise the latent space of an autoencoder. If you want to learn more about these techniques, here are some key papers: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction https://arxiv.org/abs/1802.03426 Stochastic Neighbor Embedding https://papers.nips.cc/paper_files/pa... Visualizing Data using t-SNE https://www.jmlr.org/papers/volume9/v... And if you want to learn about even more recent techniques such as TriMAP and PACMAP, here are the papers: TriMap: Large-scale Dimensionality Reduction Using Triplets https://arxiv.org/abs/1910.00204 PaCMAP https://arxiv.org/abs/2012.04456 Chapters: 00:36 PCA 05:15 t-SNE 13:30 UMAP 18:02 Conclusion This video features animations created with Manim, inspired by Grant Sanderson's work at @3blue1brown. Here is the code that I used to make this video: https://github.com/ytdeepia/Latent-Sp... If you enjoyed the content, please like, comment, and subscribe to support the channel! #DeepLearning #PCA #ArtificialIntelligence #tsne #DataScience #LatentSpace #Manim #Tutorial #machinelearning #education #somepi