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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