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Course Outline
- Backprop, modular models
- Logsum module
- RBF Net
- MAP/MLE loss
- Parameter Space Transforms
- Convolutional Module
- Gradient-Based Learning
- Energy for inference
- Objective for learning
- PCA, NLL
- Latent Variable Models
- Probabilistic LVM
- Loss Function
- Handwriting recognition
Requirements
Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).
Testimonials
The topic is very interesting.
Wojciech Baranowski
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training.
Grzegorz Mianowski
Topic. Very interesting!.
Piotr
Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.
Dolby Poland Sp. z o.o.
I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.
Radek
The deep knowledge of the trainer about the topic.
Sebastian Görg
Big and up-to-date knowledge of leading and practical application examples.
- ING Bank Śląski S.A.
A lot of exercises, very good cooperation with the group.
Janusz Chrobot - ING Bank Śląski S.A.
work on colaborators,
- ING Bank Śląski S.A.
It was obvious that the enthusiasts of the presented topics were leading. Used interesting examples during exercise.
- ING Bank Śląski S.A.
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