Neural Networks and Deep Learning
IMPORTANT NOTICE:
This course will start on January 10, 2023.
Registration
To attend the lectures and receive notifications about any change on course lectures,
please register in this file.
This course (9 CFUs) includes three modules of 3 CFUs each: the first module focuses on the theoretical foundations
of neural networks and deep learning; the second module covers more advanced topics and recent research trends;
the third module covers practical and implementation issues.
Course Program
- Module 1: Deep Neural Networks: Theoretical Foundations
- Module 2: Deep Neural Networks: Advanced Topics
- Module 3: Deep Neural Networks: Implementation Issues
- Lecture schedule
Part I: Theoretical Foundations
- Introduction to neural computing
- Hopfield networks
- Unsupervised learning
- Culstering algorithms
- Reinforcement learning
- Supervised learning - part 1
- Supervised learning - part 2
- Radial Basis Function Networks
- Towards Deep Neural Networks (DNNs)
- Autoencoders
- Convolutional Neural Networks
- DNNs for object classification
- DNNs for object detection
- DNNs for image segmentation
- Recurrent Neural Networks
- Deep Reinforcement Learning
- Sample applications and open issues
Part II: Advanced Topics
- Generative Adversarial Networks (GANs)
- Invariance and equivariance properties
- Capsule Networks
- Model compression
- Semi-supervised learning
- Contrastive learning
- Deep Clustering
- Anchor-free dectection networks
- Deformable convolutions
- Neural networks for real-time tracking
- Adversarial examples
- Towards trustoworty AI
- Explainable AI
- Natural Language Processing
- Transformers
Part III: Implementation Issues
- Implementing Hopfield networks in C
- Implementing Kohonen networks in C
- Implementing Reinforcement Learning in C
- Implementing Backpropagation in C
- Frameworks for deep learning: Keras and Caffe
- Frameworks for deep learning: Tensorflow
- Overview of the Apollo framework for Autonomous Driving
- Networks in Apollo: Perception Module
- Introduction to Adversarial Attacks and Defenses
- Explainable and Trustworthy AI for safety-critical systems
- Introduction to Transformers in Natural Language Processing
- Attention mechanisms in computer vision
- Implementing DNNs on GPUs platforms
- Design and optimization of DNNs accelerators on FPGA
Projects
- Projects rules
- Projects list (to be published soon)
- Project availability (to be published soon)
Suggested readings
Books
- Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning, MIT Press, 2017.
- François Chollet. Deep Learning with Python, Manning, 2017.
- Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction, Second edition, The MIT Press, 2018.
Introductory readings
For those who like to look into the future