Sentiment Analysis with Deep Learning
This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used network learns a 128 dimensional word embedding followed by an LSTM.
Deep Learning
TensorFlow
- Read And Execute a SavedModel on MNIST
- Train MNIST classifier
- Training Tensorflow MLP
- Edit MNIST SavedModel
- Translating From Keras to TensorFlow
Keras
Machine Translation
Cats and Dogs
Generate Fairy Tales
Generate Product Names With LSTM
- Classify images using InceptionV3
- Neural Machine Translation
- Classify images using ResNet50
- Semantic Segmentation
- Wide and Deep Learning on Census Dataset
- Sentiment Analysis with Deep Learning
- Train MNIST classifier
- Sentiment Analysis with Deep Learning KNIME nodes
DL4J
DeepLearningTutorial MNIST
- Sentiment Classification Using Word Vectors
- Network Example Of A MLP For Images
- Housing Value Prediction Using Regression
- Celebrity Detection Using Alex Net
- Simple Regression Of Simple Functions
- Calculate Document Distance Using Word Vectors
- Network Example Of A Simple Convolutional Net
- Font Detection Using A Convolutional Net
- Basic Learner View Tutorial
- Basic Concepts Of Deeplearning4J Integration
- Simple Anomaly Detection Using A Convolutional Net
- Simple Document Classification Using Word Vectors
- Network Example Of A Simple MLP
Train MNIST classifier
This workflow trains a simple convolutional neural network (CNN) on the MNIST dataset via Keras.
Sentiment Analysis with Deep Learning KNIME nodes
This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used network learns a 128 dimensional word embedding followed by an LSTM.
Using DeepLearning4J to classify MNIST Digits
The workflow downloads, uncompresses and preprocesses the orignal MNIST dataset from: http://yann.lecun.com/exdb/mnist/ The two "Normalize Images" wrapped metanodes use the KNIME Streaming functionality to convert the input files into KNIME image cells that can be used by the DL4J Learner and Predictor. The "LeNet" metanode (taken from the Node Repository) is a variant of the originally described LeNet convolutional neural network.
Sentiment Classification Using Word Vectors
This example shows how to perform sentiment classification using word vectors.
Network Example Of A MLP For Images
This workflow shows how to create an MLP with a softmax layer for image classification.
Housing Value Prediction Using Regression
This workflow shows how to use an MLP to predict a target value of a small real world example dataset.
Celebrity Detection Using Alex Net
This workflow shows an example of image classification of celebrity faces using AlexNet.
Simple Regression Of Simple Functions
This workflow shows how to use an MLP for regression of simple functions.
Calculate Document Distance Using Word Vectors
This example shows how to train a Word Vector model as well as some properties of the resulting vectors.