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  1. Workflow
    Sentiment Analysis
    Deep learning Keras Text classification
    +11
    This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used netwo…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 02_Keras > 08_Sentiment_Analysis_with_Deep_Learning_KNIME_nodes
    3
  2. Workflow
    Sentiment Analysis
    Deep learning Keras Text classification
    +9
    This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used netwo…
    andisa.dewi > Public > Deep Learning Workshop > Sentiment_Analysis_Deep_Learning
    2
  3. Workflow
    ECG Heartbeat Classification Dataset (PTB)
    ECG Conv1D Neural Network
    +3
    This ECG dataset is provided by Physikalisch-Technische Bundesanstalt (PTB) and is available on Kaggle (https://www.kaggle.com/sh…
    knime > Digital Healthcare > ECG Arrythmia Detection > ecg_cnn_ptb
    1
  4. Workflow
    ECG MIT-BIH Data Analysis and Modelling
    ECG Arrhythmia CNN
    +5
    This is also the ECG dataset from Kaggle, put together from PhysioNet MIT-BIH Arrhythmia (https://www.kaggle.com/shayanfazeli/hea…
    knime > Digital Healthcare > ECG Arrythmia Detection > ecg_cnn_mit
    1
  5. Workflow
    U-Net Encoder Decoder Architecture for Cell Segmentation
    Cell segmentation Cell image Neural network
    +4
    This workflow creates and trains a Unet for segmenting cell images. The trained network is used to predict the segmentation of un…
    janina > Public > UNet_Keras_CellSegmentation
    1
  6. 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
  7. 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
  8. 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
    1
  9. Workflow
    Sentiment Analysis
    Deep learning Keras Text classification
    +9
    This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used netwo…
    scottf > Public > TextMiningWebinar > Sentiment > Sentiment_Deep_Learning
    1
  10. 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
  11. 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
  12. 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
  13. Workflow
    Energy Demand Prediction with LSTM - Deployment
    Time Series Prediction Energy Usage
    +4
    This workflow applies an LSTM network to predict energy demand using lagged values of a time series as input.
    kathrin > Codeless Deep Learning with KNIME > Chapter 6 > 02_TSA_with_LSTM_Network_Deployment
    1
  14. 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
  15. Workflow
    Energy Demand Prediction with LSTM - Training
    Time Series Prediction Energy Usage
    +4
    This workflow trains and applies an LSTM network to predict energy demand using lagged values of a time series as input. In the E…
    kathrin > Codeless Deep Learning with KNIME > Chapter 6 > 01_TSA_with_LSTM_Network_Training
    1
  16. Workflow
    02_LSTM_Network
    Time Series Prediction Energy Usage
    +4
    This workflow predicts time series (energy consumption) by an LSTM network with lagged values as input. The trained model is then…
    aebartz > Public > Knime Project 471 Complete
    0
  17. Workflow
    Generate Text Using a Many-To-One LSTM Network (Training)
    Deep learning Keras Text generation
    +8
    The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive fairy tales. The brown nodes define the …
    emilio_s > Public Exercises > DL Italia > Lesson 4 > 4) 01_Text_Generation_Fairy_Tales_Training
    0
  18. Workflow
    Generate Text Using a Many-To-One LSTM Network (Training)
    Deep learning Keras Text generation
    +8
    The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive fairy tales. The brown nodes define the …
    kathrin > Codeless Deep Learning with KNIME > Chapter 7 > Generate_Fairy_Tales > 01_Text_Generation_Fairy_Tales_Training
    0
  19. 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
  20. 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
    0