13 results
- Go to itemThis workflow shows an overview of outlier detection techniques for credit card fraud detection. The performance of the technique…3
- Go to itemRead Keras model. Read deployment data, which are normalized into range [0,1]. Apply the Keras model to the deployment data, calc…1
- Go to itemPartition numeric input data into a training, test, and validation set. Normalize the data into range [0,1]. Build a Keras autoen…1
- Go to itemThis workflow shows an overview of credit card fraud detection techniques. The performances of the techniques are evaluated on th…1
- Go to itemThis workflow shows an overview of outlier detection techniques for credit card fraud detection. The performance of the technique…0
- Go to itemExercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…0
- Go to itemExercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…0
- Go to itemExercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to train an autoencoder model to detect …0
- Go to itemExercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to train an autoencoder model to detect …0
- Go to itemThis workflow replicates the exercises of session 2, combining training and deployment using Integrated deployment. The purple bo…0
- Go to itemThis workflow executes the model generated by the Integrated deployment to get a prediction of fraudolent transaction.0