| Code |
Course Name |
Language |
Type |
| VBA 313E |
Deep Learning |
English |
Compulsory |
| Local Credits |
ECTS |
Theoretical |
Tutorial |
Laboratory |
| 3 |
8 |
3 |
0 |
0 |
| Course Prerequisites and Class Restriction |
| Prerequisites |
VBA 312E MIN DD
or END 305E MIN DD
|
| Class Restriction |
None |
| Course Description |
| This course provides a comprehensive introduction to deep learning, covering fundamental neural network
concepts and advanced topics. Students will learn key techniques such as backpropagation, regularization, and
dropout, and gain practical experience with popular deep learning frameworks like TensorFlow, Keras, and
PyTorch. Specialized topics include Convolutional Neural Networks (CNNs), advanced architectures like VGG
and ResNet, recurrent networks (RNNs, GRUs, LSTMs), and their applications in natural language processing
(NLP) and generative models (autoencoders, GANs). Additionally, the course explores cutting-edge
advancements in large language models and deep reinforcement learning. |
|
