What we're about

A meetup for academics, professionals and hobbyists interested in applications and latest developments in Machine Learning, and AI more broadly. We talk about:

• Computer vision, speech recognition, text mining, generative design

• New papers that we're excited about, and software that we use

• Cool applications of AI & machine learning, and how we made them

We strive to focus on the science & technology side, as opposed to the commercial side.

We typically meet the first Monday of every month.

We're always looking for interesting presentations. If you have a topic you want to talk about, anything from 10 to 45 minutes long, then please email gtrent@gmail.com. For talks we are explicitly *not* commercial. We organize this meetup because we are passionate about AI & ML, not to promote some product or service.

If an organization would like to host us, or sponsor food & drink, let us know.

Our official Twitter hashtag is #MLBerlin (https://twitter.com/search?q=%23MLBerlin).

VISIT US AT: http://machinelearning.berlin/

Upcoming events (5+)

Serverless Inference; and A New Stock Prediction Architecture

Betahaus (New Location)

Talk 1: Running inference as a serverless cloud function (45 min) Speaker: Michael Perlin, innoq Abstract: Abstract: When deploying your model into production, you have to care about configuring and maintaining the runtime environment, scaling, monitoring and more – all tasks, which are more related to DevOps than to ML. In some contexts, you can achieve your goals in a much simpler way by establishing a "serverless" approach. We’ll take a look at the cloud services "AWS Lambda", "Google Cloud Functions", and "Azure Functions" and show how they enable running ML inference. Bio: Michael Perlin is Senior Consultant at INNOQ. Since more than fifteen years he has been working on multiple topics around Software Development, DevOps and Machine Learning. - Talk 2: The Architecture of a Stock Prediction System Speakers: Stefan Savev & Rey Farhan Abstract: In this talk, we will share our experience of building a stock prediction system based on the recently released Deutsche Börse Public Dataset (https://registry.opendata.aws/deutsche-boerse-pds/). Architecture components include the following. 1) achieving insights about stock market behavior via the available data and validating existing predictive approaches; 2) encoding ML models with a Domain Specific Language (DSL) that targets explicitly the properties of financial data; 3) encoding trading strategies based on the results of the ML model using another DSL. We combine approaches from data science, engineering, and stock market traders. We believe is a rare open source attempt to use ML for stock prediction in combination with a strategy and which evaluates the predictions reliably. Code: https://github.com/Originate/dbg-pds-tensorflow-demo https://github.com/Originate/dbg-pds-tensorflow-demo/blob/ss-add-strategy-image/ROADMAP.md.

Berlin ML Group - Topics TBD

Betahaus (New Location)

Talk 1: TBD Speaker: TBD Abstract: TBD Bio: TBD - Talk 2: TBD Speaker: TBD Abstract: TBD Bio: TBD

Berlin ML Group - Topics TBD

Betahaus (New Location)

Talk 1: TBD Speaker: TBD Abstract: TBD Bio: TBD - Talk 2: TBD Speaker: TBD Abstract: TBD Bio: TBD

Berlin ML Group - Topics TBD

Betahaus (New Location)

Talk 1: TBD Speaker: TBD Abstract: TBD Bio: TBD - Talk 2: TBD Speaker: TBD Abstract: TBD Bio: TBD

Past events (53)

Berlin ML Group - Conv Nets for ranking and improving images

Photos (33)