Mackenzie is the Global Startup Evangelist at AWS. His days are spent traveling the globe to meet startups, share their stories, and connect engineering teams together. Every day there are a large number of startups launching on AWS across every imaginable industry. It’s Mackenzie’s mission to find stories of startups that are helping to improve the world and share these stories with a wide audience.
Join us the AWS User Group Meetup on September 22 (Thur) at the newly opened AWS Office in Causeway Bay. Machine Learning experts from AWS and Hong Kong Machine Learning Meetup will take you through new innovation of Machine Learning! Three topics will be included in the meetup: Aspect-based sentiment analysis, Ray: Distributed Deep Learning for Common Folks and Hierarchical PCA: Incorporate (fundamental) priors into PCA.
Â
In this evening session, let's hear hot topics in ML delivered by the speakers and exchange ideas and thoughts with other like-minded developers and members!
Â
Join the AWS User Group Hong Kong Facebook Group now!
Â
Look for more new topics? Share your thoughts to help us plan for upcoming user group meetups!
Speaker:
Michelle Hong, Prototyping Solution Architect, AWS
Yanwei Cui, Machine Learning Specialist Solution Architect, AWS
Abstract:
Sentiment analysis is crucial for enterprise to study the thoughts of their customers. However, a generic sentiment is not enough. Aspect-based sentiment analysis (ABSA) can further identify fine-grained opinion polarity towards a specific category associated with a target. During the meet up, we discuss the use cases of ABSA, current research on the topic, and how to implement with Amazon Sagemaker.
Speaker:
KaHei Chan
Abstract:
Empirally it was learned that the more compute resources is invested the better deep neural networks are trained. However, taking training beyond a single node efficiently is in itself a big engineering challenge where many data scientists are not well equipped to tackle. In this talk we will explore how Ray.io airdops concepts of distributed computing and the actor programming model to Python. And as a consequence, we will also explore the easy to use trainer classes from Ray libraries for us common folks to scale our models training to many machines.
Speaker:
Gautier Marti
Abstract:
PCA is a useful tool for quant trading (stat arb) but in its naive implementation suffers from several forms of instabilities which yield to unnecessary turnover (trading cost...) and spurious trades.
In order to regularize the model, several techniques are available.
We will discuss one in particular: The Hierarchical PCA (HPCA).
With HPCA, we modify the empirical correlation matrix such that it incorporates information from a prior (fundamental) hierarchical classification: For example, sectors and industries for stocks and bonds; protocols, layers and use cases for cryptos.
We will illustrate this presentation with some basic python code and results comparing PCA and HPCA for stocks and cryptos.
Privacy | Site Terms |Â