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teaching:ws2024:create_dl [2024/06/28 14:12] (current) Dr. Vladimir Golkov created |
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| + | ~~NOTOC~~ | ||
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| + | ===== Practical Course: Creation of Deep Learning Methods (10 ECTS) ===== | ||
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| + | ** <fc # | ||
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| + | **Please send applications (including learning goals, programming skills description, | ||
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| + | **Organizers**: | ||
| + | [[members: | ||
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| + | Preliminary meeting (attendance **not** obligatory): | ||
| + | Slides from an earlier semester' | ||
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| + | < | ||
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| + | Details about the matching system can be found [[http:// | ||
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| + | < | ||
| + | If you ask for a spot after the matching phase, but do not hear from us soon, it means that we cannot offer you a spot. | ||
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| + | == Content == | ||
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| + | Using deep learning to solve real problems often requires the creation of novel appropriate deep learning methods, rather than just out-of-the-box usage of existing architectures. In this practical course, students will choose <fc # | ||
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| + | Some of the projects that can be chosen also include the analysis of design principles of existing methods, and subsequent usage of these design principles to create new methods. | ||
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| + | **If you want to propose an own project instead of choosing from the projects that we will offer, please discuss with us before 16 July. Use the email subject " | ||
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| + | == Prerequisites == | ||
| + | Good programming skills. Eagerness to acquire and deepen knowledge about how to solve complex problems with machine learning. Passion for mathematics. Knowledge of Python and array programming in NumPy (or Matlab or similar) is recommended. Having good soft skills (or the willingness to acquire them quickly) and using them is a prerequisite, | ||
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| + | == Course Structure == | ||
| + | The students will work individually and in groups on practical deep learning projects. At the end of the project, each student or group will present their project with a following Q&A session. There will be no additional written or oral exam. Both the theoretical and practical part of the project will be considered in the final grading. | ||
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| + | //Note: ECTS credits are the measure of workload. So-called semester weekly hours (Semesterwochenstunden, | ||
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| + | == Introductory Lectures == | ||
| + | The students will receive recordings of introductory lectures as early as they want.\\ | ||
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| + | Lecture 1: Machine Learning; Artificial Neural Networks; Convolutional Neural Networks; Q&A about Deep Learning\\ | ||
| + | Lecture 2: Recap; Network Architecture Design; Q&A about Deep Learning\\ | ||
| + | Lecture 3: Recap; Network Training; Understanding and Visualizing; | ||
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| + | == Literature == | ||
| + | * [[https:// | ||
| + | * [[http:// | ||
| + | * [[https:// | ||
| + | * [[http:// | ||
| + | * Good current papers | ||
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