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# # Week 1. Hyperparameter tuning: Searching the best architecture
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# ### Neural Architecture Search
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# - Is a technique for automating the design of ANN.
# - It helps finding the optimal architecture
# - This is a search over a huge space
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# ### Types of parameteres in ML Models
# Trainable parameters:
# - Learned by the algorithm during training
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# Hyperparameters:
# - Set before launching the learning process
# - Not updated in each training step
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# ### Manual hyperparameter tuning is not scalable
# - Hyperparameters can be numerous even for small models
# - Tuning them manually can be a real brain teaser
# - Tuning helps with model performance
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# ### Automating hyperparameter tuning with Keras Tuner
# - Automation is key: open source resources to the rescue
# Keras Tuner: 
# - Hyperparameter tuning with TF.2
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# ## Keras Autotuner
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# - Do the model need more or less hidden units to perform well?
# - How does model size affect the convergence speed?
# - Is there any trade off between convergen speed, model size and accuracy?
# - Search automation is the natural path to take
# - Keras tunner built in search functionality

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