ab 8: Deep Learning Overview For this exercise we will continue working with the Titanic data from the previous labs. Set-up 1. Load the Titanic and Titanic_hidden datasets Lab Steps 1. Create a training/test set using the Titanic data Note: If you’re using the technique shown in class: the matrix model will exclude 1 and Name 2. Set up a deep neural network with the following: two hidden layers with 30 units and ReLU activation and an output layer with a single unit and sigmoid activation 3. Compile the model with binary_crossentropy loss and the accuracy metric 4. Fit the model on the training set, evaluate it with the test set 5. Get the titanic_hidden in a form Keras can use: you’ll can set it up with code similar to this: new_data_x <- titanic_hidden %>% model.matrix(object = Survived ~ . -1 -Name) %>% scale() new_data_y <- titanic_hidden$Survived 6. Test the model’s accuracy with the hidden data it’s never seen before solve it using Python
Lab 8: Deep Learning
Overview
For this exercise we will continue working with the Titanic data from the previous labs.
Set-up
1. Load the Titanic and Titanic_hidden datasets
Lab Steps
1. Create a training/test set using the Titanic data Note: If you’re using the technique shown in class:
the matrix model will exclude 1 and Name
2. Set up a deep neural network with the following: two hidden layers with 30 units and ReLU activation
and an output layer with a single unit and sigmoid activation
3. Compile the model with binary_crossentropy loss and the accuracy metric
4. Fit the model on the training set, evaluate it with the test set
5. Get the titanic_hidden in a form Keras can use: you’ll can set it up with code similar to this:
new_data_x <- titanic_hidden %>% model.matrix(object = Survived ~ . -1 -Name) %>% scale()
new_data_y <- titanic_hidden$Survived
6. Test the model’s accuracy with the hidden data it’s never seen before
solve it using Python
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