#LondonAI Feb Meetup: Operational AI, Best Coding Practices, and Generative DL
February 28 @ 6:00 pm - 9:00 pm
Dear London Makers,
Thanks to our friends from Microsoft, we will return to Microsoft Reactor for our first London meetup of the decade! This is a joint event with Deep Learning Lab (https://www.meetup.com/Deep-Learning-Lab/).
We are a community partner of ML Prague (www.mlprague.com). Thanks to the organiser, we will give away one FREE conference ticket.
Would you like to enter the draw to win this ticket? Complete this form https://forms.gle/rZSdNhEkkGM9FtbL9 AND attend our meetup on 28th Feb. You can also use this code “h2olondon20” to purchase conference tickets with a 20% discount.
If you are planning to attend ML Prague, don’t forget to check out our workshop (March 20) and exhibition stand (March 21-22).
-[masked]: Pizza time
– Welcoming remarks by H2O.ai team
– ML Prague conference ticket lucky draw
– Tech talks
– Networking until 9pm
From Research and Prototyping to Operational AI by Andreas Vrålstad
Talk 2: Best Coding Practices for Data Science by Nikolay Manchev
We all use notebooks for exploratory data analysis, visualisation, and trying different machine learning models. Sometimes these notebooks find their way into production, but their code and structure are often far from ideal. In this session, we cover some best practices around creating and operationalising notebooks. We will talk about structure, code style, refactoring in notebooks, unit testing, reproducibility and more.
Nikolay Manchev is a machine learning enthusiast and speaker. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks with emphasis on biological plausibility. Nikolay was a Senior Data Scientist and Developer Advocate at IBM [masked]) and currently acts as the Principal Data Scientist for EMEA at Domino Data Lab.
Talk 3: Generative Deep Learning – The Key To Unlocking Artificial General Intelligence by David Foster
Generative modelling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavours such as painting, writing, and composing music. In this talk, we will cover:
– A general introduction to Generative Modelling
– A walkthrough of one of the most utilised generative deep learning models – the Variational Autoencoder (VAE)
– Examples of state-of-the-art output from Generative Adversarial Networks (GANs) and Transformer based architectures.
– How generative models can be used in a reinforcement learning setting (World Models paper)
– Why I believe generative models will play a crucial part in the quest to build Artificial General Intelligence (AGI)
David Foster is a Founding Partner of Applied Data Science Partners (https://adsp.ai/), a data science consultancy building innovative AI solutions for clients. He holds an MA in Mathematics from Trinity College, Cambridge, UK and an MSc in Operational Research from the University of Warwick. David has won several international machine learning competitions and is the author of the best-selling book ‘Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play’. He has also authored several successful blog posts on deep reinforcement learning including ‘How To Build Your Own AlphaZero AI using Keras’.