query: Deep learning model with both continuous and categorical features
positive_passages.docid: 17160
positive_passages.text: How to recode categorical variable into numerical variable when using SVM or Neural Network
negative_passages.docid: 73225
negative_passages.text: local minima vs saddle points in deep learning
negative_passages.docid: 6914
negative_passages.text: Why are neural networks becoming deeper, but not wider?
negative_passages.docid: 5026
negative_passages.text: Can I use multiple regression when I have mixed categorical and continuous predictors?
negative_passages.docid: 153601
negative_passages.text: difference between neural network and deep learning
negative_passages.docid: 95969
negative_passages.text: How to choose the split in Random forest for categorical predictors (features)?
negative_passages.docid: 145300
negative_passages.text: A list of cost functions used in neural networks, alongside applications
negative_passages.docid: 24230
negative_passages.text: Proof that CRF models and logistic models are convex functions
negative_passages.docid: 111321
negative_passages.text: When should we discretize/bin continuous independent variables/features and when should not?
negative_passages.docid: 79882
negative_passages.text: Explanation of Spikes in training loss vs. iterations with Adam Optimizer
negative_passages.docid: 109966
negative_passages.text: How does the Adam method of stochastic gradient descent work?
negative_passages.docid: 125509
negative_passages.text: Scikit Learn Missing Data - Categorical values
negative_passages.docid: 22203
negative_passages.text: Data normalization and standardization in neural networks
negative_passages.docid: 155274
negative_passages.text: Multicollinearity between categorical and continuous variable
negative_passages.docid: 15759
negative_passages.text: Adding Regressors to Convolutional Neural Network
negative_passages.docid: 79824
negative_passages.text: Varied Activation Functions in Neural Networks
negative_passages.docid: 13412
negative_passages.text: Motivating sigmoid output units in neural networks starting with unnormalized log probabilities linear in $z=w^Th+b$ and $\phi(z)$