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.
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.
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.
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.
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.
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.
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.
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.
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.
The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R.continue reading.
The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R.continue reading.
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.
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.
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.
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.
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.
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.
Plentiful high-quality data is the key to great machine learning models. But good data doesn’t grow on trees, and that …Continue reading →continue reading.
As a follow-up to my Primer On Universal Function Approximation with Deep Learning, I’ve created a project on Github that …Continue reading →continue reading.
Arthur C. Clarke famously stated that “any sufficiently advanced technology is indistinguishable from magic.” No current technology embodies this statement …Continue reading →continue reading.