This course is designed to provide students with an in-depth exploration of Deep Learning, particularly focusing on Neural Network architectures. Throughout the semester, students will gain a comprehensive understanding of how Deep Neural Networks work, from the fundamental theory behind their design to practical implementation skills. The course primarily covers Supervised Deep Learning techniques and equips students with hands-on experience using PyTorch, a popular Deep Learning framework. By working through exercises and assignments in PyTorch, students will learn to effectively build, train, and optimize neural networks.
The course also emphasizes ethical considerations in AI development, ensuring that students not only learn the technical aspects of Deep Learning but also understand its broader impact on society.
Linear Algebra
Probability Theory
Programming Skills (for practice)
Understand the basics of Deep Learning
Understand and implement Neural Network architectures
Learn a popular Deep Learning framework (PyTorch)
Be able to use open-source Neural Network software
- Courville, Goodfellow, Bengio: Deep Learning. https://www.deeplearningbook.org/
- Zhang, Aston, and Lipton, Zachary C., and Li, M,u and Smola, Alexander J.: Dive into Deep Learning. https://d2l.ai/
- Deep Learning Specialization by Andrew NG. https://www.coursera.org/specializations/deep-learning
You can find the Colab links for the practice material here.
- Python and Colab - https://colab.research.google.com/drive/1PCTkAYD_D-EB0VnQjxYjivGSuscXBKxl?usp=sharing
- Python Fundamentals - https://colab.research.google.com/drive/11Gb2R5vjR7_u1v0EQ27ILEW5tNggQ-kr?usp=sharing
- Numpy Introduction - https://colab.research.google.com/drive/11lGGnduGU9m1QnyNhbrl1bdFN4wuItSZ?usp=sharing
- Numpy Fundamentals - https://colab.research.google.com/drive/1NZuk-VUDWbsWejKSHYVR1VE6k86UIZBv?usp=sharing
- GPU PyTorch - https://colab.research.google.com/drive/12VTIeP1MCFyaLHWo1zSWxKcCQ76fi7hu?usp=sharing
- Autograd - https://colab.research.google.com/drive/12eGIhIfzor-5FgfLUdwTQdMwbmh3PLXR?usp=sharing
- Linear Regression - https://colab.research.google.com/drive/1ZPeKUjwnp1UeWFo62ZpcTaeI4JrJllKM?usp=sharing
- Image Classification - https://colab.research.google.com/drive/1sS6vjgYlf6F2YhrC51SMHPwJba20DxxJ?usp=sharing
- Spectogram Classification with CNNs - https://colab.research.google.com/drive/1DAx-TA67ndHJihb8OQUGpsd31MTlbNUN?usp=sharing
- Transfer Learning - https://colab.research.google.com/drive/1Urp9rVDzZPQ8Zdz0PoVKZtaPZuAOwkqF?usp=sharing
- Quantization Techniques (Optional) https://colab.research.google.com/drive/1iYEshq_vYtBHVYnlA8xzPOEMOIFWQ78W?usp=sharing
- Object Detection YOLO11 - https://colab.research.google.com/drive/1JhJZSO1xVhuwBorErgrntX6t3jBo6AZt?usp=sharing
- Faster R-CNN for Object Detection - https://colab.research.google.com/drive/1ClHbP4lBDy4IrPXEnRIpjNTxBtAajBEE?usp=sharing
- U-Net - https://colab.research.google.com/drive/1txAqAFxTRLSPhrw7u0w9Vnf1t8_ohCmr?usp=sharing
- Instance Segmentation - https://colab.research.google.com/drive/1asBca8ROMn8swA4r11qMJoQ1oOiQzIfM?usp=sharing
- Pose Estimation - https://colab.research.google.com/drive/1hjzMAEJtTUNmuI3XhHm2nr99SiE29r3Q?usp=sharing
- Segment Anything 2 - https://colab.research.google.com/drive/1qr0PTdUWTUueNHwdW4UJ-Ua8-wBLmdLT?usp=sharing
You can find the Colab links for the homeworks and assignments material here.