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Sometimes in deep learning, architecture design and hyperparameter tuning pose substantial challenges. Using Auto-Keras, none of these is needed: We start a search procedure and extract the best-performing model. This post presents Auto-Keras in action on the well-known MNIST dataset.
Auto-Keras: Tuning-free deep learning from R Read More »
The final release of TensorFlow v1.3 is now available. This release marks the initial availability of several canned estimators including DNNClassifier and DNNRegressor.
TensorFlow v1.3 Released Read More »
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.
TensorFlow Estimators Read More »
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 focus on enabling fast experimentation.
The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R.
tfruns: Tools for TensorFlow Training Runs Read More »
We are excited to announce the availability of the cloudml package, which provides an R interface to Google Cloud Machine Learning Engine. CloudML provides a number of services including on-demand access to training on GPUs and hyperparameter tuning to optimize key attributes of model architectures.
R Interface to Google CloudML Read More »
A new major release of lime has landed on CRAN. lime is an R port of the Python library of the same name by Marco Ribeiro that allows the user to pry open black box machine learning models and explain their outcomes on a per-observation basis
lime v0.4: The Kitten Picture Edition Read More »
