DSF Day 4 - Neural Network Architecture for NLP at Dataiku

Date: 
Thursday, April 19, 2018 - 18:30
Source: 
Data Science Festival - London
Attendees: 
75
City: 
London

Data Science Festival Day 4 - Dataiku

Please note registering on Meetup does not guarantee you a place at this event.

Please register for a ballot ticket here: http://datasciencefestival.com/event/live/2018/dsf-day-4-neural-network-architecture-for-nlp-at-dataiku/

Due to the popularity of Data Science Festival events, we are now allocating event tickets via a random ballot. Registering here enters you into the ticket ballot for the Data Science Festival Event at Dataiku on April 19th 2018, the ballot will be drawn on the 17th April 2018. Those randomly selected will then be e-mailed tickets for the event, with the joining details.

If you get an allocated ticket, please bring a copy of your paper ticket or your ticket on your phone to the event to check in with your QR code. Tickets are non-transferable.

The Data Science Festival is the first of its kind as the only community led, free to attend Data Science Festival in the UK.

Schedule

6.00pm Guests arrive

6.30pm- Alexandre Hubert - A Novel Neural Network Architecture for Natural Language Processing (NLP)

7.15pm - Harvinder Atwal from MoneySuperMarket on "DataOps: Delivering Agile Data Science

8.00pm – Networking, food, drinks

9.00pm - Close

Speaker 1: Alexandre Hubert - A Novel Neural Network Architecture for Natural Language Processing (NLP)

Talk Abstract: Deep Learning in NLP has been dominated in the past years by recurrent and convolutional models. But other models emerge to improve translation quality and performance. Alex has developed a translator for his team and clients using a new neural network architecture called the Transformer. Unlike traditional translator models, this one solely focuses on attention instead of recurrence and develops powerful NLP models in a fraction of the training time. Alex will explain how the translator has been built, give a live demo, and discuss how the Transformer is able to overcome pitfalls of RNN models.

Bio: As a data scientist, Alexandre has worked on a range of use cases, from creating models that predict fraud to building specific recommendation systems. He especially loves using deep learning with text or sports data. Even when he’s playing sport or having fun with friends, Alexandre sees numbers and patterns everywhere, bringing him quickly back to his laptop to try out new ideas. He has been a data scientist at Dataiku for more than two years. He works on several bank use cases as loan delinquency for leasing and refactoring institutions but also marketing use cases for retailers.

Speaker 2: Harvinder Atwal from MoneySuperMarket on "DataOps: Delivering Agile Data Science

Talk Abstract: DataOps: Delivering Agile Data Science. The majority of organisation fail to create business value from their investments in data. Most implementations are either high cost IT projects, local applications that are not built to scale for production workflows or laptop data insight projects that never impact customers. The key to adding value is to adapt and borrow principles from Agile, Lean and Dev Ops. However, Agile Data Science is not just about shipping working machine learning models, but starts with better alignment of data science with the rest of the organisation and its goals. This presentation will outline practical solutions to increasing the velocity of value creation including: prioritisation, new processes for an end-to-end data lifecycle, people and organisation changes to improve collaboration, tools and solution architectures to reduce data friction.

Bio: Harvinder Atwal leads a team of Data Scientists to deliver data-driven personalisation; develop the Data Strategy to support Advanced Analytics and define Data Science best practise in MoneySuperMarket. Harvinder was previously Insight Director for Tesco Clubcard at Dunnhumby and Senior Manager for Customer Strategy and Insights at Lloyds Banking Group. He is passionately interested in Data Science, Machine Learning and Big Data technologies .

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