In a few of my earlier posts "designing Q&A retrieval system" and "designing e-commerce system for similar products using deep learning", one of the primary goals was to retrieve the most similar answers (responses) and the most similar e-commerce products respectively in a fast and scalable way. The later post was pretty much generic i.e. find dense representations for each item (questions, product images etc.) using un-supervised and supervised learning […]
This post is motivated from trying to find better unsupervised vector representations for questions pertaining to the queries from customers to our agents. Earlier, in a series of posts, we have seen how to design and implement a clustering framework for customer questions, so that we can efficiently find the most appropriate answer and at the same time find out most similar questions to recommend to the customer.
For the past few days, I have been reading quite a lot of research papers, articles and blogs related to artificial neural networks and its transition towards deep learning. With so many different methods of selecting the best neural network architecture for a problem, the optimal hyper-parameters, the best optimization algorithm and so on, it becomes a little overwhelming to connect all the dots together when we ourselves start to […]