Deep
  • Introduction
  • First Chapter
  • LSTM - 1. encoder
  • LSTM - 2. decode_training_set
  • Symbolic Reasoning
  • GAN & VA
  • Unsupervised 1 - traditional
  • Unsupervised 2 - Autoencoder
  • Torch - Basic n NN Basic
  • Torch - nn
  • Torch - nn - Save/Load & STATE_DICT
  • Torch - cnn
  • TF
  • Trick_1
  • Trick_2
  • Trick_TODO
  • F1
  • Save & restore
  • GRAPH 1
  • GRAPH 2
  • Spark RDD
  • Secure AI
  • FasterRCNN
  • Terms
  • HandsonML 8. Dimension Reduction
  • HandsonML 9. Unsupervised Learning
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Terms

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Last updated 5 years ago

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1. Multi

multi-class

multi-label

multi-task

one sample has one true label

one sample has a set of labels

generalize multi-label

softmax

log-loss

ex: MNIST

ex: document has multiple topics: sport, military, and/or finance.

ex: a fruit should be apple, orange or pearl

Multi-label can be replaced by several binary classifiers. However, multi-label shares features and reduces training resource.

Multi-label is better for balanced dataset.

https://www.coursera.org/lecture/machine-learning-projects/multi-task-learning-l9zia