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Autoencoder
Keras Anomaly detection Credit card Fraud Neural network Deep learning Fraud detection Decoder Encoder
  1. Workflow
    Fraud Detection Techniques Comparison
    Credit card Fraud DBSCAN
    +6
    This workflow shows an overview of outlier detection techniques for credit card fraud detection. The performance of the technique…
    knime > Finance, Accounting, and Audit > Fraud Detection Techniques Comparison
    3
    knime
  2. Workflow
    Overview of Credit Card Fraud Detection Techniques
    Credit card Fraud DBSCAN
    +8
    This workflow shows an overview of credit card fraud detection techniques. The performances of the techniques are evaluated on th…
    knime > Finance, Accounting, and Audit > Overview of Credit Card Fraud Detection Techniques
    2
    knime
  3. Workflow
    Autoencoder MNIST MidPoint Focus
    Autoencoder Keras Neural network
    +8
    Exploring the latent space of an autoencoder for dimensional reduction
    iceman > Public > Autoencoder MNIST MidPoint Focus
    1
    iceman
  4. 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
    kathrin
  5. Workflow
    Keras Autoencoder for Fraud Detection - Training
    Autoencoder Keras Neural network
    +12
    This workflow trains an autoendcoder model to detect fraudulent transactions.
    kathrin > Codeless Deep Learning with KNIME > Chapter 5 > 01_Autoencoder_for_Fraud_Detection_Training
    1
    kathrin
  6. 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
    knime
  7. 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
    knime
  8. Workflow
    Different options to train an autoencoder using TensorFlow 2
    Autoencoder Keras Neural network
    +5
    This workflow shows the different options of training and executing a network using TF2 on the example of an autoencoder: Option …
    knime > Examples > 04_Analytics > 14_Deep_Learning > 04_TensorFlow2 > 02_Tensorflow2_Autoencoder_for_Fraud_Detection_Training
    1
    knime
  9. Workflow
    Fraud Detection Techniques Comparison
    Credit card Fraud DBSCAN
    +5
    This workflow shows an overview of outlier detection techniques for credit card fraud detection. The performance of the technique…
    marcelo_linero > Public > Fraud Detection Techniques Comparison
    0
    marcelo_linero
  10. Workflow
    Keras Autoencoder for Fraud Detection - Training
    Autoencoder Keras Neural network
    +13
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to train an autoencoder model to detect …
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Exercises > 01_Fraud_Detection_Training_Exercise
    0
    knime
  11. 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_Exercise
    0
    knime
  12. Workflow
    Keras Autoencoder for Fraud Detection - Training
    Autoencoder Keras Neural network
    +13
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to train an autoencoder model to detect …
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Solutions > 01_Fraud_Detection_Training_Solution
    0
    knime
  13. 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
    knime
  14. Workflow
    Keras Autoencoder for Fraud Detection - Integrated Deployment
    Autoencoder Keras Neural network
    +13
    This workflow replicates the exercises of session 2, combining training and deployment using Integrated deployment. The purple bo…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Supplementary workflows > Autoencoder > 01_Fraud_Detection_Integrated_Deployment
    0
    knime
  15. 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
    knime
  16. Workflow
    Http attack detection by autoencoder
    Autoencoder Keras Neural network
    +1
    Train an autoencoder to detect attacks via http connections on a computer network
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Supplementary workflows > Autoencoder > 03_Http_Attack_Detect_Solution
    0
    knime