This workflow demonstrates how to do sentiment analysis by fine-tuning Google's BERT network. The idea is straight forward: A sma…

This is a simple example workflow for multivariant time series analysis using an LSTM based recurrent neural network and implemen…

This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used netwo…

This workflow trains a simple convolutional neural network (CNN) on the MNIST dataset using Keras. The enclosed pictures are from…

This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used netwo…

Read and preprocess an image dataset to train a GAN (Generative Adversarial Network) that produces similar images. The Discrimina…

This is a simple example workflow for the deployment of a multivariant time series, LSTM based, recurrent neural network. It is b…

Here we use word embedding instead of hot encoding, using a Word2Vec Learner node. The hidden layer size is set to 10, therefore …

This workflow creates and trains a Unet for segmenting cell images. The trained network is used to predict the segmentation of un…

This workflow shows how to train an XGBoost based image classifier that uses a pretrained convolutional neural network to extract…

Exploring the latent space of an autoencoder for dimensional reduction

This workflow uses Tensorflow2 to create and train a Unet for segmenting cell images. The trained network is used to predict the …

Compares the Keras Layer Nodes against the DL Python nodes. Builds four models which attempt to predict future sales. Two models …

This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used netwo…

This workflow trains and applies an LSTM network to predict energy demand using lagged values of a time series as input. In the E…

This workflow shows the different options of training and executing a network using TF2 on the example of an autoencoder: Option …

This workflow applies a trained autoencoder model to detect fraudulent transactions.

This workflow uses a Kaggle Dataset including 14K customer tweets towards six US airlines (https://www.kaggle.com/crowdflower/twi…

This workflow trains an autoendcoder model to detect fraudulent transactions.

Uses a character level encoder-decoder network of LSTMs. The encoder network reads the input sentence character by character and …