1. Keras

2. Models Model = layers, loss and an optimizer
Add layer to model, compile() and fit()
Model can be saved and chckpoimted for later use

3. Layers Layers are used to define the network Examples: Dense
Convolutional
Pooling
Dropout

4. Loss Function
Measures the error by comparing the network predicted output with the expected output
The loss must be minimized by udatng the weights thgough backpropagation
Common loss functions
Mean squared root
Cross-entropy

5. Optimizers
Optimizers are strategies used to update the weights durig backpropagation
Examples:
RMSpro
Adam
AdaGrad

6. Backends Keras may use several backends: Exploits either: Theano
tensorFlow
Exploits either:
CPU
GPU

7. Resources