Introduction to Deep Learning (IN2346)
Course 0000002767 in SS 2024
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 4 SWS |
| Organisational Unit | Informatics 28 - Associate Professorship of Visual Computing (Prof. Nießner) |
| Lecturers |
Yujin Chen Haoxuan Li Matthias Nießner Susanne Weitz |
| Dates |
Tue, 14:00–16:00, virtuell Thu, 14:00–16:00, virtuell Mon, 14:00–16:00, MI HS1 Tue, 14:00–16:00, GALILEO Audimax |
Assignment to Modules
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IN2346: Introduction to Deep Learning / Introduction to Deep Learning
This module is included in the following catalogs:- Focus Area Bio-Sensors in M.Sc. Biomedical Engineering and Medical Physics
- Catalogue of non-physics elective courses
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | - Introduction to Computer Vision and history of Deep Learning.- Machine learning Basics 1: linear classification, maximum likelihood- Machine learning basics 2: logistic regression, perceptron - Introduction to neural networks and their optimization, SGD, Back-propagation- Training Neural Networks Part 1:regularization, activation functions, weight initialization, gradient flow, batch normalization, hyperparameter optimization- Training Neural Networks Part 2: parameter updates, ensembles, dropout- Convolutional Neural Networks - CNN for object detection (from MNIST to ImageNet), visualizing CNN (DeepDream)- Recurrent networks and LSTMs- Research 1: Prominent architectures, e.g. GoogleNet, ResNet - Research 2: Reinforcement learning- Research 3: Adversarial networks |
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| Links |
E-Learning course (e. g. Moodle) Contact TUMonline entry |