Thank you ! Indeed the voiceover is generated by an AI, but it is my own voice that I cloned. I'm using Elevenlabs. Did that annoy you or got you out of the video ? :(
Nice explanation but I think two key aspects are missing (maybe planned to show up in later videos): 1. the connection to transformerts. 2. the fact that latent space allows you to make two models speek the same language (like the idea of CLIP and how its used in DallE)
Hi, thank you for the feedbacks ! Indeed these aspects are very important in modern architectures, but I feel like I would need to introduce a lot of other concepts to get there. It's definitely something I'll treat in future videos.
Could you make a video on common dimensionality reduction methods like PCA and projection (linear discrimants) etc? I’ve always been interested in when they should be applied but not the other. Anyways, nice video very underrated! Deserves more exposure! T^T
Thank you ! Yep that's the plan for the very next video: it will be an explanation of how several visualization methods work, there will probably be PCA, t-SNE and UMAP
great video. knew about encoders from the transformer model where the optimization criterion for embedding is the output of the decoder for the classification/generation task measured by eg. cross entropy loss and i know about word2vec where the optimization criterion is dot product similarity of co-occuring words. i did not know that in autoencoders the optimization criterion is minimizing the loss over reconstructing the original input. nice.
