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Programa del Curso
- Backprop, modelos modulares
- Módulo Logsum
- RBF Neto
- Pérdida de MAP/MLE
- Transformaciones de espacio de parámetros
- Módulo convolucional
- Aprendizaje basado en gradientes
- Energía para la inferencia
- Objetivo para el aprendizaje
- PCA, NLL
- Modelos de variables latentes
- LVM probabilístico
- Función de pérdida
- Reconocimiento de escritura a mano
Requerimientos
Buena base en aprendizaje automático básico. Conocimientos de programación en cualquier lenguaje (idealmente Python/R).
21 horas
Testimonios (4)
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Curso - Introduction to Deep Learning
The deep knowledge of the trainer about the topic.
Sebastian Görg
Curso - 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
Curso - 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.