About the deep learning course:
Machine perception of natural signals has improved a lot in the recent years thanks to deep learning (DL). Improved vision systems with DL will make self-driving cars possible and is leading to more accurate image-based medical diagnosis.
Improved speech recognition and natural language processing with DL will lead to many new intelligent applications within health-care and IT.
Pattern recognition with DL in large datasets will give new tools for drug discovery, condition monitoring and many other data-driven applications. Applications in other areas such as natural language processing, biology, finance and robotics are numerous.
Deep learning is an important tool for the leading IT companies' ambition about becoming machine learning and AI first companies. This course is designed to help you integrate deep learning into your organisation.
The deep learning course provides:
- Knowledge about the latest developments in the field.
- Opportunities and pitfalls.
- Company access to computational frameworks making it possible to apply learnings directly to your company context.
- Insight into well-established methods like feed-forward, convolutional and recurrent neural networks.
- Insight into frontier technology like un-, semi- and reinforcement learning that can be expected to play a larger role in the coming years.
Who is the deep learning course for:
The course is aimed at all who have a professional interested in deep learning and who have knowledge of mathematics, which is in line with the mathematics that will be used, as first year students at DTU. (Specifically linear algebra and probability theory).
Prerequisites:
Programming preferably in Python, basic probability theory and basic linear algebra. Please bring your own laptop.
Computer frameworks
PyTorch og AWS GPU computing.
More about the course and practical information is found here.