Recent advances in machine learning have rekindled the quest to build machines that can interact with outside environment like we human do - using visual clues, voice and text. An important piece of this trilogy are systems that can process and understand text in order to automate various workflows such as chat bots, named entity recognition, machine translation, information extraction, summarization, FAQ system, etc.

A key step towards achieving any of the above task is - using the right set of techniques to represent text in a form that machine can understand easily.

This would be a 2-day instructor-led hands-on training session to learn and implement an end-to-end deep learning model for natural language processing.

Outline

Unlike images, where directly using the intensity of pixels is a natural way to represent the image; in case of text there is no such natural representation. No matter how good is your ML algorithm, it can do only so much unless there is a richer way to represent underlying text data. Thus, whatever NLP application you are building, it’s imperative to find a good representation for your text data.

In this bootcamp, we will understand key concepts, maths, and code behind the state-of-the-art techniques for text representation. We will cover mathematical explanations as well as implementation details of these techniques. This bootcamp aims to demystify, both - Theory (key concepts, maths) and Practice (code) that goes into building these techniques. At the end of this bootcamp participants would have gained a fundamental understanding of these schemes with an ability to implement them on datasets of their interest.

Requirements

Laptop and Lots of enthusiasm

Links

Sarcasm detection, ODSC 2018 . https://www.youtube.com/watch?v=FoR_-ELAcfE
Synthetic Gradients, AntHill 2017 . https://www.youtube.com/watch?v=toZprSCCmNI

Slides

https://www.slideshare.net/anujgupta5095/nlp-bootcamp-2018

Speaker bio

Anuj Gupta is part of Intuit AI team. Prior to this, he was heading ML efforts for Huawei Technologies, Freshworks, Chennai and Airwoot, Delhi. Anuj dropped out of Phd from IIT Delhi to work with startups. Before this Anuj did his masters in theoretical computer science from IIIT Hyderabad and

Anuj is a regular speaker at ML conferences like ODSC, Pydata, Nvidia forums, Fifth Elephant, Anthill. Anuj has conducted workshops, mostly attended by machine learning practitioners. Anuj is the co-organizer for one of the early Deep Learning meetup group in Bangalore. He was alsi was the Editor of “Anthill-2018”.