@-beee-
Excellent high-level overview. Very concise and clear!
@sander1426-2
Great explanation and high quality video. Thanks!
@simayakar7869
Thank you so much for the clear explanation! ☺
@daalhead1098
In the final year of my undergrad, doing my dissertation on forecasting volatility of financial time series. Thanks for all your work. If you could do any videos that are applicable to this that would be great! (I'm sure you get requests every second of the day but yeah, thanks)
@amnont8724
Thanks for the explanation Ritvik! Could u pls make a video about LDA too?
@samgraham6355
Well done video
@synchro-dentally1965
Thank you for the video. At 6:30 You mention using Cosine Simularity here. Have you heard of a recent paper titled "Surpassing Cosine Similarity for Multidimensional Comparisons: Dimension Insensitive Euclidean Metric (DIEM)"? It'd be great to hear your opinion on it.
@bin4ry_d3struct0r
During the LLM training phase, the vocabulary or text corpus gets tokenized and then each token gets embedded into its own vector. When the cosine similarity comparison is made during the retrieval phase, is the query embedded as one vector or is each token in the query embedded into its own vector? If the latter, is vector addition then used to combine them all into one vector? Cosine similarity only compares two vectors at a time, right? If so, I'm curious as to how queries (and docs), each comprising multiple tokens, are prepared for similarity search. I hope my question makes sense.
@YourDailyR
Thanks mate
@kTonpa
Nicely explained! What do you use for your illustrations?
@Rasa_b
What vectro databases are widely used for this purpose?
@KarthikShamsundar-dh4pv
Hello Ritvik, I would appreciate it greatly if you could please make a video on modelling the ARIMAX model in excel. It would be hugely beneficial for me. Please please please see this comment 🙏
@rahulkiroriwal8779
have you ever thought of making a course on applied stats ? Please do
@ritvikmath