Worum es bei uns geht

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/

Bevorstehende Events (3)

Hyperparameter Optimization Libraries; and more

Betahaus (New Location)

Talk 1: Hyperparameter Optimization in Python (40 min + 10 min Q&A) Speaker: Jakub Czakon Abstract: Hyperparameter optimization (HPO) is quickly becoming common knowledge. But which library should you use? In this talk, I will review some main HPO libraries including Hyperopt, Scikit-Optimize, Optuna, and HpBandSter. I will present what the algorithms used are doing, and compare the libraries based on the API, speed, visualization utilities, results and more. Bio: Jakub is a Senior Data Scientist at neptune.ml. He graduated in Physics from the University of Silesia in Katowice, and in Finance from the University of Economics in Wroclaw. He's worked on data science for facial recognition, OCR, cancer detection and classification, satellite image segmentation, text mining labor market data, and more. He was a member of the teams that won MICCAI Munich 2015 "Combined Imaging and Digital Pathology Classification Challenge", won MICCAI Athens 2016 "Pet segmentation challenge using a data management and processing infrastructure", and won crowdAI "Mapping Challenge" competition in 2018. Talk 2: TBD Speaker: TBD Abstract: TBD Bio: TBD

Berlin ML Group - Topics TBD

deepset GmbH

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

AI for Real-time Visual Analysis

Betahaus (New Location)

In this meetup, we have two talks about real-time visual analysis. Talk 1: Towards real-time interpretation of the physical world with FPGA and DNNs Speakers: Nicolas von Roden, Hirad Rezaeian Abstract: We aim for a compact, affordable and accurate real-time interpretation engine of the physical world. Processing of high-resolution input data from visual sensors in real-time to achieve correct and high accuracy scene understandings using scalable, robust and price efficient hardware is a huge challenge. We approach this via co-design of FPGA and DNN. On the software side, we exploit multi-task learning to combine different single-task models. On the hardware side, we aim to quantize model weights and activation functions for efficient deployment on FPGA-based hardware. Bios: Nicolas is a Computer Vision Engineer at Advertima AG. He graduated in CS from the U of Erlangen-Nuremberg with a focus on image processing and ML. He is currently working on computer vision tasks for face recognition, pose detection and tracking in real-time as well as combining the various single-task models into a multi-task framework. He previously worked on tumor detection in magnetic resonance images for Siemens Healthineers. Hirad is a Hardware Digital Design Engineer at Advertima AG. He graduated from ETH University as an electrical engineer with a focus on micro electronics and signal processing. His experience in digital signal processing and algorithm improvements regarding hardware implementation (ASIC and FPGA) led him work on ASICs for AI workload acceleration. Currently he is working on the quantization of the model weights and activation functions to reach a ternary weight network on a FPGA-based platform. -- Talk 2: A divide & conquer approach to real time video segmentation on smartphones Speaker: Noah Kutscher Abstract: Real-time video segmentation for extracting humans from images has two challenges: compute speed, and segmentation quality. We are testing a semantic unit to make foreground estimation via a pre-trained Deep Neural Network. This information does not have to be provided in real-time and can therefore have complex computation. This estimation is used in the second stage to train a small but fast model to classify without the need to search for semantic connections between the different image pixels. Both methods are combined for a semantically correct, stable, and fast approach. Bio: Noah is an Undergrad Student at University of Applied Science Mittweida, studying Digital Forensics. After many years of programming, his focus shifted to ML. Since early 2019, he researches real time video segmentation at Cinector.

Vergangene Events (60)

Continual Learning & Fraud Detection

Fotos (34)