Word Embeddings with Keras
Word embedding is a method used to map words of a vocabulary to dense vectors of real numbers where semantically similar words are mapped to nearby points. In this example...continue reading.
Category: Keras
Word Embeddings with Keras
Word embedding is a method used to map words of a vocabulary to dense vectors of real numbers where semantically similar words are mapped to nearby points. In this example...continue reading.
Time Series Forecasting with Recurrent Neural Networks
In this post, we’ll review three advanced techniques for improving the performance and generalization power of recurrent neural networks. We’ll demonstrate all three concepts on a temperature-forecasting problem, where you...continue reading.
Time Series Forecasting with Recurrent Neural Networks
In this post, we’ll review three advanced techniques for improving the performance and generalization power of recurrent neural networks. We’ll demonstrate all three concepts on a temperature-forecasting problem, where you...continue reading.
Image Classification on Small Datasets with Keras
Having to train an image-classification model using very little data is a common situation, in this article we review three techniques for tackling this problem including feature extraction and fine...continue reading.
Image Classification on Small Datasets with Keras
Having to train an image-classification model using very little data is a common situation, in this article we review three techniques for tackling this problem including feature extraction and fine...continue reading.
Deep Learning for Text Classification with Keras
Two-class classification, or binary classification, may be the most widely applied kind of machine-learning problem. In this excerpt from the book Deep Learning with R, you’ll learn to classify movie...continue reading.
Deep Learning for Text Classification with Keras
Two-class classification, or binary classification, may be the most widely applied kind of machine-learning problem. In this excerpt from the book Deep Learning with R, you’ll learn to classify movie...continue reading.
tfruns: Tools for TensorFlow Training Runs
The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R.continue reading.
tfruns: Tools for TensorFlow Training Runs
The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R.continue reading.
Keras for R
We are excited to announce that the keras package is now available on CRAN. The package provides an R interface to Keras, a high-level neural networks API developed with a...continue reading.
Keras for R
We are excited to announce that the keras package is now available on CRAN. The package provides an R interface to Keras, a high-level neural networks API developed with a...continue reading.
TensorFlow Estimators
The tfestimators package is an R interface to TensorFlow Estimators, a high-level API that provides implementations of many different model types including linear models and deep neural networks.continue reading.
TensorFlow Estimators
The tfestimators package is an R interface to TensorFlow Estimators, a high-level API that provides implementations of many different model types including linear models and deep neural networks.continue reading.
TensorFlow v1.3 Released
The final release of TensorFlow v1.3 is now available. This release marks the initial availability of several canned estimators including DNNClassifier and DNNRegressor.continue reading.
TensorFlow v1.3 Released
The final release of TensorFlow v1.3 is now available. This release marks the initial availability of several canned estimators including DNNClassifier and DNNRegressor.continue reading.
A Simple Convolutional Neural Network for The Binary Outcome
Since CNN(Convolutional Neural Networks) have achieved a tremendous success in various challenging applications, e.g. image or digit recognitions, one might wonder how to employ CNNs in classification problems with binary...continue reading.
Autoencoder for Dimensionality Reduction
We often use ICA or PCA to extract features from the high-dimensional data. The autoencoder is another interesting algorithm to achieve the same purpose in the context of Deep Learning....continue reading.
An Example of Merge Layer in Keras
The power of a DNN does not only come from its depth but also come from its flexibility of accommodating complex network structures. For instance, the DNN shown below consists...continue reading.
Dropout Regularization in Deep Neural Networks
The deep neural network (DNN) is a very powerful neural work with multiple hidden layers and is able to capture the highly complex relationship between the response and predictors. However,...continue reading.
