Erlangen Artificial Intelligence & Machine Learning Meetup

---- DIFFERENT LOCATION ---- Dear AI & ML Enthusiasts, This time we have three exciting speakers. Agenda: Welcome Intro Round ---- TALK 1: ---- Title: AI applications in Education Pascal Zoleko, Flexudy Education GbR www.flexudy.com Abstract: In the quest of improving the quality of education, Flexudy leverages the power of AI to help people learn more efficiently. During the talk, I will show how we trained an automatic extractive text summarizer based on concepts from Reinforcement Learning, Deep Learning and Natural Language Processing. Also, I will talk about how we use pre-trained NLP models to generate simple questions for self-assessment. ---- TALK 2: ---- Optimizing AI pipeline with Kubeflow Muneer Ahmad and Steve Guhr, NetApp Deutschland GmbH https://www.linkedin.com/in/muneer-ahmad-dedmari/ https://www.linkedin.com/in/steveguhr/ Abstract: Nowadays we are getting in touch with different kinds of technologies. Especially the AI and Machine Learning market is growing with a velocity which has never been seen before. For all those different challenges we have to face in our daily business there is never this “one size fits it all” solution to everything. Hundreds of tools are available to help us in getting faster, better and more reliable. “Time spent” and “Go-To-Market” is more important than ever before and therefore we need to optimize our processes and pipelines. In this talk we want to share our findings and experiences on building platform services for managing AI and ML-pipelines in an efficient manner. Using container-based technologies like Docker and Kubernetes we can improve the data scientists’ workflows without the need to know what happens in the background. It’s all about automation and self-service, whether it’s in the cloud or built as an on premises infrastructure. Managing data is also a very important yet often underestimated challenge to cover and talk about as this is the asset from which we generate the most value. Kubeflow is one tool chest on which we focused our work and which we want to demo – showing an AI use case within its usual lifecycle compared to what gets possible when using the right tools at the right spot e.g. auto parameter fine tuning and event based workflow management alongside your own jupyter notebook environment. Looking forward to seeing you all! We are grateful to Design Offices Erlangen for hosting us! Alex *************** IMPORTANT *************** Photography / filming / audio recording is taking place during the event for documentation purposes. The photographs and recordings will likely be published for the purpose of reporting on the event on all communication channels of the event organizers. If you do not wish this, please inform the organizer on site before the start of the event.

Dear AI & ML Enthusiasts, This time we have an exciting speaker. Agenda: Welcome Intro Round Talk 1: Machine Learning for Audio Signal Processing Hendrik Purwins, Accenture https://de.linkedin.com/in/hendrik-purwins-51451b75 I will present our recent overview article with the same title (IEEE JSTSP 14, 2019 by myself, Bo Li, Tuomas Virtanen, Jan Schlüter, Shuo-yiin Chang, and Tara Sainath). Given the recent surge in developments of deep learning, this talk provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-melspectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified. Talk 2: Hot Topics: As usual, the organizer will present hot news and latest papers in AI & ML. If you would like to contribute to this section, let me know beforehand. Looking forward to seeing you all! We are grateful to Coworking Erlangen for hosting us! Alex *************** IMPORTANT *************** Photography / filming / audio recording is taking place during the event for documentation purposes. The photographs and recordings will likely be published for the purpose of reporting on the event on all communication channels of the event organizers. If you do not wish this, please inform the organizer on site before the start of the event.

Dear AI & ML Enthusiasts, This time we have an exciting speaker from Google. Agenda: Welcome Intro Round Talk 1: Title: TBA Dr. Stefan Ebener, Google https://www.linkedin.com/in/dr-stefan-ebener/ Abstract: TBA Talk 2: Title: Challenges in deploying AI for corporates Michael Streit, Novartis https://www.linkedin.com/in/michael-streit/ Abstract: TBA Looking forward to seeing you all! We are grateful to Coworking Erlangen for hosting us! Alex *************** IMPORTANT *************** Photography / filming / audio recording is taking place during the event for documentation purposes. The photographs and recordings will likely be published for the purpose of reporting on the event on all communication channels of the event organizers. If you do not wish this, please inform the organizer on site before the start of the event.