Arquitectura de Aplicaciones de Machine Learning
Arquitectura de Aplicaciones de Machine Learning
Viernes 11 de enero de 18.30 a 21.30 hs.
Educación IT - Lavalle 648 - Piso 8

ML Strategy

Learning Objectives

Understand what multi-task learning and transfer learning are
Recognize bias, variance and data-mismatch by looking at the performances of your algorithm on train/dev/test sets

* Error Analysis

Carrying out error analysis
Cleaning up incorrectly labeled data
Build your first system quickly, then iterate

* Mismatched training and dev/test set

Training and testing on different distributions
Bias and Variance with mismatched data distributions
Addressing data mismatch

* Learning from multiple tasks

Transfer learning
Multi-task learning

* End-to-end deep learning

What is end-to-end deep learning?
Whether to use end-to-end deep learning

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