Deep Learning (englisch) - Interaktive Studienpläne der TU Ilmenau

Theory: (evaluation by written exam)

• Knowledge on theoretical foundations of deep neural networks
• Knowledge on CNN architectures and applications
• Knowledge on architectures for sequence modeling and their applications

    Practice: (evaluation by practical assignments)

• Ability to implement and apply of a variety of deep learning algorithms
• Ability to evaluate and troubleshoot deep learning models
• Ability to use computational resources for train and application of deep learning models

Deep learning has recently revolutionized a variety of application like speech recognition, image classification, and language translation mostly driven by large tech companies, but increasingly also small and medium-sized companies aim to apply deep learning techniques for solving  an ever increasing variety of problems. This course will give you detailed insight into deep learning, introducing you to the fundamentals as well as to the latest tools and methods in this rapidly emerging field.

Deep learning thereby refers to a subset of machine learning algorithms that analyze data in succeeding stages, each operating on a different representation of the analyzed data. Specific to deep learning is the ability to automatically learn these representations rather than relying on domain expert for defining them manually.

The course will teach you the theoretical foundations of deep neural networks, which will provide you with the understanding necessary for adapting and successfully applying deep learning in your own applications. Additionally, by completing the course, you will be able to implement, parametrize and apply a variety of deep learning algorithms. You will learn how to use deep convolutional neural networks (CNNs) as well as recurrent neural networks (RNNs) for image, text, and time series analysis. You will further become familiar with advanced data science tools and in using computational resources to train and apply deep learning models.

- Projector presentation

- Slide decks available

- Assignment management through Moodle

- Cloud services (personal computer required)

- Deep Learning: Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press (2016)

- Pattern Recognition and Machine Learning: Christoper M. Bishop, Springer (2006)

- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Aurélien Géron, O'Reilly Media (2017)