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Deep Learning With Keras To Predict Customer Churn

This is guest post contributed by Matt Dancho, CEO of Business Science. The post was originally published on the Business Science blog. Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R! Read more →

R Interface to Google CloudML

Overview 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: Scalable training of models built with the keras, tfestimators, and tensorflow R packages. On-demand access to training on GPUs, including the new Tesla P100 GPUs from NVIDIA®. Hyperparameter tuning to optmize key attributes of model architectures in order to maximize predictive accuracy. Read more →

Classifying duplicate questions from Quora with Keras

In this post we will use Keras to classify duplicated questions from Quora. The dataset first appeared in the Kaggle competition Quora Question Pairs and consists of approximately 400,000 pairs of questions along with a column indicating if the question pair is considered a duplicate. Our implementation is inspired by the Siamese Recurrent Architecture, with modifications to the similarity measure and the embedding layers (the original paper uses pre-trained word vectors). Read more →

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. Representing words in this vector space help algorithms achieve better performance in natural language processing tasks like syntactic parsing and sentiment analysis by grouping similar words. For example, we expect that in the embedding space “cats” and “dogs” are mapped to nearby points since they are both animals, mammals, pets, etc. Read more →

Time Series Forecasting with Recurrent Neural Networks

Time Series Forecasting with Recurrent Neural Networks In this section, we’ll review three advanced techniques for improving the performance and generalization power of recurrent neural networks. By the end of the section, you’ll know most of what there is to know about using recurrent networks with Keras. We’ll demonstrate all three concepts on a temperature-forecasting problem, where you have access to a time series of data points coming from sensors installed on the roof of a building, such as temperature, air pressure, and humidity, which you use to predict what the temperature will be 24 hours after the last data point. Read more →

Image Classification on Small Datasets with Keras

Training a convnet with a small dataset Having to train an image-classification model using very little data is a common situation, which you’ll likely encounter in practice if you ever do computer vision in a professional context. A “few” samples can mean anywhere from a few hundred to a few tens of thousands of images. As a practical example, we’ll focus on classifying images as dogs or cats, in a dataset containing 4,000 pictures of cats and dogs (2,000 cats, 2,000 dogs). Read more →

Deep Learning for Text Classification with Keras

Classifying movie reviews: a binary classification example Two-class classification, or binary classification, may be the most widely applied kind of machine-learning problem. In this example, you’ll learn to classify movie reviews as positive or negative, based on the text content of the reviews. The IMDB dataset You’ll work with the IMDB dataset: a set of 50,000 highly polarized reviews from the Internet Movie Database. They’re split into 25,000 reviews for training and 25,000 reviews for testing, each set consisting of 50% negative and 50% positive reviews. Read more →

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. Use the tfruns package to: Track the hyperparameters, metrics, output, and source code of every training run. Compare hyperparmaeters and metrics across runs to find the best performing model. Automatically generate reports to visualize individual training runs or comparisons between runs. You can install the tfruns package from GitHub as follows: Read more →

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 focus on enabling fast experimentation. Keras has the following key features: Allows the same code to run on CPU or on GPU, seamlessly. User-friendly API which makes it easy to quickly prototype deep learning models. Built-in support for convolutional networks (for computer vision), recurrent networks (for sequence processing), and any combination of both. Read more →

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. More models are coming soon such as state saving recurrent neural networks, dynamic recurrent neural networks, support vector machines, random forest, KMeans clustering, etc. TensorFlow estimators also provides a flexible framework for defining arbitrary new model types as custom estimators. The framework balances the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. Read more →