Wednesday 23 October 18:00 - 20:15

Rise London
41 Luke St, London
London
EC2A

Registration
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London Meetup: Deep Dive into TensorFlow #26

Science & Technology

Welcome to TensorFlow London Meetup #26!

AGENDA:

6:00 - Doors open. Networking. Drinks & Pizza

6:45 - Introduction

7:00 - 7.25 Talk 1: Introduction to Convolutional Neural Networks with Tensorflow by David Tyler

7:25 - 7:30 Q&A

7:30 - 7:55 Talk 2: DevOps for Machine Learning, why is it different by Ryan Dawson

7:55 - 8.00 Q&A

TALK DETAILS:

Talk #1

Speaker: David Tyler, Managing Director at Outlier Technology

Title: Introduction to Convolutional Neural Network with Tensorflow

Bio: David is a techie at heart and has been working in software development, data analysis and data science for the last 20 years.

Abstract: Representing a significant advance in neural network design, CNNs present some interesting concepts and approaches that can be somewhat confusing at first glance. This talk aims to dig into some of those concepts and explain them in terms that reveal what’s happening behind the lines of we can so easily pull together in frameworks like Tensorflow and Keras. In particular we’ll be exploring one of the first CNN tutorials from the Tensorflow site, that builds a CNN for recognising handwriting.

Talk #2

Speaker: Ryan Dawson, Cloud Engineer at Seldon

Title: DevOps for Machine Learning, why is it different?

Bio: Ryan Dawson is a core member of the seldon open source team, providing tooling for machine learning deployments to Kubernetes (https://github.com/SeldonIO/seldon-core/). He has spent 10 years working in the Java Development scene in London across a variety of industries.

Abstract: DevOps instincts tend to be shaped by what has worked well before. Instincts derived from mainstream software development projects get challenged when we turn to enabling machine learning projects. The key reasons are that the development/delivery workflow is different and the kind of software artefacts involved are different. We will explore the differences and look at emerging open source projects in order to appreciate why the DevOps for machine learning space is growing and the needs that it addresses.

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