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In the video, I discussed the importance of search services for food aggregators like Zomato, focusing on handling complex search queries containing multiple entities. I delved into how Zomato uses natural language processing to enhance its search experience. Additionally, I shared details about a system design course I offer, emphasizing practical learning and real-world system building. Zomato's search challenges include understanding user intent and multilingual queries. Techniques like word embeddings using Word2Vec and tokenization address these challenges effectively.
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This is fabulous. Thanks a lot for recording this. Notes: - Give corpus to Byte-Pair encoding to get tokens - Convert tokens to vec using Word2Vec - Train neural network on above dataset using Bidirectional LSTM (with crf) - Use "sequence tagging" for named entity recognition
I love how Arpit simplifies complex concepts. Truly a great engineer. Hope I can learn to become like him. Thank you for these.
Wow!, so much info in this short video really man you're great
Awesome content! Thanks Arpit.
So Insightful! Thanks buddy
Very Informative Content . Thanks a lot Arpit !!
Typesense is yet another good production ready package on the lines of elastic search and solar.
This is dayummm....awesome best explaination
While the general idea has been covered, any discussion on modern NLP is incomplete without transfer learning and transformers. For most practical purpose word2vec and lstms are dead, expect for rare cases where your scale and latency req. demands it, like in the case of gmail smart compose.
questions how can i handle searches in which there are some spelling mistakes
Next video : How Zomato fired top college grads just after 4 months of joining ☹️
@kalyanben10
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How Zomato Scaled its Loss... and then scaled firing
@socialmedia5173
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Zomato is founded to make money not to give jobs, have they promised u that they will not fire 🤷