DS405B Practical Deep Learning for Language Processing
| Lecturer: | Dr. Aseem Behl |
| Course description: | DS405B |
| Language: | English |
| Recommended for this semester or higher: | 1 |
| ECTS-Credits: | 6 |
| Course can be taken as part of following programs/modules: | For more information please refer to alma. |
| Prerequisite for: | --- |
| Prerequisites: | Prior experience with programming in Python ideally but not necessarily through an introductory Python course like DS404 Data Science with Python or DS400 Data Science Project Management. |
| Limited attendance: | 28 |
| Course Type: | Lecture |
| Date: | Lecture on: Tuesdays from 10 - 12 am c.t. (Beginning of the first lecture October 17, 2023) - Lecture Hall 002, Verfügungsgebäude building on Wilhelmstraße 19 |
| Registration: | Limited to 28 participants. Registration open for all (no first-come, first-served): Registration will open on September 1, 2023 on alma - end of registration time: Sunday, October 15, 2023. If the number of applications (limited to 28 participants) exceeds the number of places available, we will randomly select from all applications. Preferred access for students from the M.Sc. Data Science in Business and Economics program. |
| Downloads: | ILIAS |
| Method of Assessment: | Written Exam or Presentation or Assignments or Term Paper |
| Content: | Deep learning has become widely successful in tackling fundamental tasks arising in computer vision, language processing and robotics. Many of these fundamental tasks are relevant to a much broader array of applications in business and scientific domains. This module starts with a broad view of machine learning and neural networks, and it subsequently covers the theory of neural networks in the context of practical examples and implementation of deep learning methods with the help of prominent frameworks in Python. The focus will be on applications deriving business intelligence from text data, however, several concepts learned in the module can be applied to other data modalities like tabular or visual data. |
| Objectives: | After this module, students can develop an understanding of how neural network models work and how to implement neural network architectures in Python with the help of deep learning frameworks. They can exploit text datasets to reliably train and debug modern deep learning techniques for natural language processing tasks. They can appreciate the effectiveness of deep learning as a tool in their machine learning toolbox. |
| Literature: | There is no required textbook for this module. Some lectures may recommend readings from the following books: 1. Speech and Language Processing by Dan Jurafsky and James H. Martin 2. Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville 3. Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola |