Deep Learning

Deep learning

  • Type of supervised learning problem.
  • Good at capturing interactions and non-linearities.

Useful Concepts

  • Softmax – Useful for converting model scores into probabilities
    • scores * n makes the softmax outputs closer to 0 and 1.
    • scores / n makes the softmax outputs resemble a uniform distribution.
  • Cross Entropy
    • Useful for measuring the distance between model output probabilities and one-hot encoded class labels
    • D(Scores, Labels) = sum (label_i * log(score_i))
  • Normalize the inputs to 0 mean and equal variance
  • Sample the parameters from a normal distribution with 0 mean and a small sigma. (so that the output scores are small which in turn means that the softmax outputs resemble a uniform distribution)

Deep Learning Fundamentals

Image Recognition

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