Deep-Learning in Python by Keras on top of Tensorflow Contents Forward Propagation Neural Networks Activation Function (RELU) Applying the network to many observations/rows of data Multi-Layer Neural Network Loss Function Mean Squared Error (MSE), Scaling up loss function to multiple data points Calculating Slopes Using Gradient Descent Calculating Error Using Learning Rate Making multiple updates to weights Backward Propagation Neural Networks Building Models in Keras (Specify Architecture, Compile Model, Fit Model, Prediction) Classification Models in Keras (Titanic Dataset) Using Models in Keras (Save, Reload, and add extra features for classification problems) Model Optimization via Stochastic Gradient Descent(SGD) with different Learning Rates Model Validation in Keras Using Early Stopping Monitor Model Comparison Using Matplotlib in order to check the validation score