Free
0 Seat
- Non-technical, but informative introduction to deep learning (=neural networks), similarities and differences to classical machine learning techniques
- Important concepts of deep learning: architecture of neural networks (nodes and layers for building complex networks), performance assessment, and tuning of neural networks
- Different types of deep learning: standard (feedforward) neural networks for structured data (e.g. in csv format), convolutional neural networks (e.g. for image/video processing), recurrent neural networks (e.g. for text analysis of social media and natural language processing), generative adversarial networks (e.g. for synthetic data generation, art, etc.)
- One-click deep learning for beginners: automatic network search and parameter selection
- Business cases and practical examples with real data using graphical interfaces in the machine learning platform BigML.com – no programming required!
Top speaker
- Amir Tabakovic – Strategist, Innovator, Investor in ML Technology, Chair of Expert Group Data Privacy and AI at Mobey Forum, Guest lecturer at ICEMD and ESADE
Subject Area Co-ordinator
- Prof. Dr. Michael Burkert, University of Fribourg
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