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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).
21 Hours
Testimonials (7)
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
A lot of exercises, very good teamwork.
Janusz Chrobot - ING Bank Slaski S.A.
Course - Introduction to Deep Learning
Machine Translated
The deep knowledge of the trainer about the topic.
Sebastian Gorg
Course - Introduction to Deep Learning
I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.
Radek
Course - Introduction to Deep Learning
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.
Course - Introduction to Deep Learning
Topic. Very interesting!.
Piotr
Course - Introduction to Deep Learning
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training.
