Very deep learning - recent methods and technologies
UE-SIN.07614
| Teacher(s): Liwicki Marcus |
| Level: Master |
| Type of lesson: Lecture |
| ECTS: 5 |
| Language(s): English |
| Semester(s): SA-2018 |
In this lecture the most recent advances of deep learning will be presented.The intended schedule is:
- Introduction, Motivation
- Advanced Convolutional Networks (ConvNet, AlexNet, GoogLeNet)
- SqueezeNet
- Extended Recurrent Neural Networks (LSTM, MD-LSTM, Dynamic Cortex Memories)
- Spiking Neural Networks
- Reinforcement Learning (Policy and Value Networks)
- Bleeding-Edge Architectures (depending on the most recent publications in Deep Learning).
- Introduction, Motivation
- Advanced Convolutional Networks (ConvNet, AlexNet, GoogLeNet)
- SqueezeNet
- Extended Recurrent Neural Networks (LSTM, MD-LSTM, Dynamic Cortex Memories)
- Spiking Neural Networks
- Reinforcement Learning (Policy and Value Networks)
- Bleeding-Edge Architectures (depending on the most recent publications in Deep Learning).
Training aims
Expected outcomes:
Understanding and Implementing advanced deep learning methods
Solving difficult tasks in Pattern Recognition, Data Science, and Big Data Analytics Literatur
Understanding and Implementing advanced deep learning methods
Solving difficult tasks in Pattern Recognition, Data Science, and Big Data Analytics Literatur