Join us in the KNIME Berlin Office for our next meetup! This time we will focus on deep learning and natural language processing!
This is a free event, open to everybody who is interested. Food and drinks will be provided. A quick heads up: we still have ongoing construction and no elevator in the building. Please be prepared to climb four floors on foot!
Talk 1: Introduction to LSTM units in Deep Learning Architectures - Kathrin Melcher
LSTM units in deep learning architectures are the state-of-the-art for sequence analysis. In this presentation we’ll find out what recurrent neural networks are and how they are trained, what LSTM units are and how they can remember or forget the past. We’ll also learn the difference between many to one, many to many, and one to many neural architectures.
Kathrin Melcher is a data scientist at KNIME. She holds a master's degree in Mathematics. She has a strong interest in data science, machine learning and algorithms, and enjoys teaching and sharing her knowledge about it.
Talk 2: Yo! AI Generated Rap Songs - Rosaria Silipo
This post is about generating free text
with a deep learning network
particularly it is about Brick X6,
make you feel soom the way
I probably make
More money in six months,
Than what's in your papa's safe
Look like I robbed a bank …
You’d think I can rap. I cannot. The rap song up here was written by my deep learning LSTM-based, rap-trained recurrent neural network. We’ll show you how to prepare the data and build, train, and deploy a deep learning LSTM-based recurrent neural network for free text generation. Once built and trained on an appropriate training set, the network can be used to generate other kinds of free texts - for example Shakespearean text.
Rosaria Silipo is a Principal Data Scientist at KNIME. Rosaria holds a doctorate degree in bio-engineering and has spent most of her professional life working on data science projects for a number of different customer companies in a number of different fields, such as for example IoT, customer intelligence, financial industry, cybersecurity.