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01_​DL4J

This directory contains 17 workflows.

Icon14_​DeepLearningTutorial_​MNIST 

Icon01_​Basic_​Concepts_​Of_​Deeplearning4J_​Integration 

This workflow shows basic concepts of the KNIME Deeplearning4J Integration.

Icon02_​Basic_​Learner_​View_​Tutorial 

This workflow shows an example of the View of the DL4J Feedforward Leaner nodes.

Icon03_​Network_​Example_​Of_​A_​Simple_​MLP 

This workflow shows how to create an MLP with a softmax layer for classification.

Icon04_​Network_​Example_​Of_​A_​MLP_​For_​Images 

This workflow shows how to create an MLP with a softmax layer for image classification. Workflow Requirements KNIME Analytics Platform 3.4.0 KNIME […]

Icon05_​Network_​Example_​Of_​A_​Simple_​Convolutional_​Net 

This workflow shows how to create a simple convolutional network and use it for image classification.

Icon06_​Calculate_​Document_​Distance_​Using_​Word_​Vectors 

First, we read in a dataset containing sentences and assign each document a unique label. The unique label is used to create a document vector which […]

Icon07_​Simple_​Document_​Classification_​Using_​Word_​Vectors 

This example shows how to transform a document into a vector using a word vector model and using these vectors for classification. First, we read some test […]

Icon08_​Sentiment_​Classification_​Using_​Word_​Vectors 

This example shows how to perform sentiment classification using word vectors. In this example, we use IMDb reviews which have either a positive or […]

Icon09_​Simple_​Anomaly_​Detection_​Using_​A_​Convolutional_​Net 

This workflow shows how to do anomaly detection of the MNIST dataset using a convolutional network. Workflow Requirements KNIME Analytics Platform […]