Neural Networks and Deep Learning
IMPORTANT NOTICE:
Given the current COVID situation, all lectures will be given online at the following
Channel.
Please, register at the link below to be invited to access the channel.
The first lecture is scheduled on January 11, 2022 at 9:00. Please, see the full course schedule below.
Make sure to connect 5 min before start time, because I may not be able to admit participants once started.
Registration
To attend the lectures and receive notifications about any change on course lectures,
please register in this file.
This course (6 CFUs) includes two modules of 3 CFUs each: the first module focuses on the theoretical foundations
of neural networks and deep learning, while the second module covers more practical and implementation issues.
Course Program
Course topics: theoretical part
- Introduction to neural computing
- Hopfield networks
- Unsupervised learning
- Clustering 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
Slides on the practical part
- Implementing neural networks in C
- Implementing unsupervised models in C
- Implementing Reinforcement Learning in C
- Implementing Backpropagation in C
- Implementing convolutional networks 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 - Part I
- Implementing DNNs on GPUs platforms - Part II
- Accelerating DNNs on FPGA platforms
- Design and optimization of DNNs accelerators on FPGA
Projects
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