Hub

9 results

Filter
Backpropagation
Deep learning Keras Anomaly detection Autoencoder Banking
+4
  1. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +15
    This workflow applies a trained autoencoder model to detect fraudulent transactions.
    kathrin > Codeless Deep Learning with KNIME > Chapter 5 > 02_Autoencoder_for_Fraud_Detection_Deployment
    1
  2. Workflow
    Keras Autoencoder for Fraud Detection Training
    Autoencoder Keras Neural network
    +16
    Partition numeric input data into a training, test, and validation set. Normalize the data into range [0,1]. Build a Keras autoen…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 03_Keras_Autoencoder_for_Fraud_Detection_Training
    1
  3. Workflow
    Keras Autoencoder for Fraud Detection Deployment
    Autoencoder Keras Neural network
    +16
    Read Keras model. Read deployment data, which are normalized into range [0,1]. Apply the Keras model to the deployment data, calc…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 04_Keras_Autoencoder_for_Fraud_Detection_Deployment
    1
  4. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +16
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Solutions > 02_Fraud_Detection_Deployment_Solution
    0
  5. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +16
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Exercises > 02_Fraud_Detection_Deployment
    0
  6. Workflow
    Keras Autoencoder for Fraud Detection - Integrated Deployment Call
    Autoencoder Keras Neural network
    +16
    This workflow executes the model generated by the Integrated deployment to get a prediction of fraudolent transaction.
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Supplementary workflows > Autoencoder > 02_Fraud_Detection_Call
    0
  7. Workflow
    Classifying the iris dataset with ANN 4-3-1
    BackPropagation Perceptron Neural networks
    +2
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal is to train a multilayer Perceptron with 4-8-3 layers, using…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session1 > Exercises > 01_Iris_Classification_ANN
    0
  8. Workflow
    Classifying the iris dataset with ANN 4-3-1
    BackPropagation Perceptron Neural networks
    +2
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal is to train a multilayer Perceptron with 4-8-3 layers, using…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session1 > Solutions > 01_Iris_Classification_ANN_Solution
    0
  9. Workflow
    Classifying the iris dataser with ANN 4-3-1
    The GuideBook BackPropagation Perceptron
    +3
    This workflow trains a multiplayer Perceptron with 4-8-3 layers, using BackPropagation, to classify the Iris dataset.
    knime > Academic Alliance > Guide to Intelligent Data Science > Example Workflows > Chapter9 > 02_NeuralNetwork_DeepLearning
    0