Concept to Code: Learning Distributed Representation of Heterogeneous sources for Recommendation (Deep Learning Beginner/Intermediate)

by Omprakash Sonie (Flipkart, India)

Deep Learning attempt to learn multiple levels of representations and abstractions from data. Deep Learning techniques fall under the following categories: Embedding methods, feedforward and auto-encoders for collaborative filtering, deep feature extraction methods, and session-based recommendation with recurrent neural networks.

The aim of this tutorial is to give conceptual understanding of learning distributed representation techniques with sufficient mathematical background along with actual code. Combine the learnt distributed representation from various data sources including items, users, product images, review texts and ratings for a recommender system. A case study covering embeddings, RNNs, and CNNs will be presented along with the code walkthrough and what is going on behind the scenes.

The tutorial is for beginner and intermediate participants who have run a few projects in Python/Jupyter notebooks and would like to understand the high level math behind the techniques.


Tuesday, Oct 2, 2018, 09:00-10:30

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