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    • Normalize Input
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    • Input
      • Normalize input
      • Initialize weight
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      • Learning rate finder
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  • Fine-tuning
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    • Freeze layer/weight
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  • deeplearning.ai
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      • Wk2 - Python Basics with Numpy
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    • Bayesian Delta-Rule Model Explains the Dynamics of Belief Updating
    • Sensory uncertainty and spatial decisions
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    • Place cells, spatial maps and the population code for memory (Hopfield)
    • Spatial Cognitive Map
    • Event Perception and Memory
    • Interplay of Hippocampus and Prefrontal Cortex in Memory
    • The Molecular and Systems Biology of Memory
    • Reconsidering the Evidence for Learning in Single Cells
    • Single Cortical Neurons as Deep Artificial Neural Networks
    • Magnetic resonance-based eye tracking using deep neural networks
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  1. Hyperparameter tuning
  2. Input

Normalize input

PreviousInputNextInitialize weight

Last updated 3 years ago

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Left: need to be careful about the learning rate, or else will oscillate around the optimal point

Right: normalized data can use bigger learning rate and learn faster

Two steps:

Zero center:

  1. subtract mean: all features around 0

  2. normalize variable: make it round (variance around 1)

Use the same parameter (µ and σ) for both train and test

x = np.mean(x, axis = 0)