Study programme 2020-2021 | Français | ||
Advanced Deep Learning | |||
Learning Activity |
Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
---|---|---|---|---|
I-ILIA-202 |
|
|
Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
---|---|---|---|---|---|---|---|
Anglais | Anglais | 6 | 6 | 0 | 0 | 0 | Q1 |
Organisational online arrangements for the end of Q3 2020-2021 assessments (Covid-19) |
---|
|
Description of the modifications to the Q3 2020-2021 assessment procedures (Covid-19) |
Evaluation methods of Deep Neural Networks Comparative analysis between the different types of deep neural networks (MLP, CNN, RNN, LSTM, Auto-encoders, etc.) |
Organisational arrangements for the end of Q1 2020-2021 assessments (Covid-19) online or face-to-face (according to assessment schedule)
Description of the modifications to the Q1 2020-2021 online assessment procedures (Covid-19) online or face-to-face (according to assessment schedule)
Evaluation methods of Deep Neural Networks
Comparative analysis between the different types of deep neural networks (MLP, CNN, RNN, LSTM, Auto-encoders, etc.)
Content of Learning Activity
- Implemenation and exploitation of new neural architectures (CNN, RNN, LSTM, etc.) ;
- Implementation of the techniques of performance evaluation, interpretation and visualization of deep learning results ;
- Performance optimization: setting hyper parameters, regularization, batch normalization, cross validation, etc.
- Efficient exploitation and portage on embedded hardware
Required Learning Resources/Tools
Not applicable
Recommended Learning Resources/Tools
Not applicable
Other Recommended Reading
Not applicable
Mode of delivery
Type of Teaching Activity/Activities
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)