Deep Learning

Using Keras for Deep Learning with R

We are excited to announce new developments in Keras for R. Data scientists can use the most popular and powerful deep learning frameworks to guide data-driven decisions, all within R

Introducing torch for R

RStudio Team

Fernanda Viegas & Martin Wattenberg | Data, visualization, and designing AI | RStudio (2020)

Recent progress in machine learning has raised a series of urgent questions: How can we train and debug deep learning models? How can we understand what is going on inside a neural network? And, perhaps most important, how can we design systems that serve people best? We'll show a series of examples from the People+AI Research (PAIR) initiative at Google--ranging from data visualizations for researchers, to tools for medical practitioners, to guidelines for designers--that illustrate how thinking carefully about data can lead to better tools, more effective design, and help humans and AI work together. About Fernanda: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather. About Martin: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather

Sigrid Keydana | Why TensorFlow eager execution matters | RStudio (2019)

In current deep learning with Keras and TensorFlow, when you've mastered the basics and are ready to dive into more involved applications (such as generative networks, sequence-to-sequence or attention mechanisms), you may find that surprisingly, the learning curve doesn't get much flatter. This is largely due to restrictions imposed by TensorFlow's traditional static graph paradigm. With TensorFlow Eager Execution, available since summer and announced to be the default mode in the upcoming major release, model architectures become more flexible, readable, composable, and last not least, debuggable. In this session, we'll see how with Eager, we can code sophisticated architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in a straightforward way. VIEW MATERIALS https://github.com/skeydan/rstudio_conf_2019_eager_execution

Machine Learning with R and TensorFlow

J.J. Allaire's keynote at rstudio::conf 2018 on the R interface to TensorFlow (https://tensorflow.rstudio.com), a suite of packages that provide high-level interfaces to deep learning models (Keras) and standard regression and classification models (Estimators), as well as tools for cloud training, experiment management, and production deployment. The talk also discusses deep learning more broadly (what it is, how it works, and where it might be relevant to users of R in the years ahead). Slides: https://beta.rstudioconnect.com/ml-with-tensorflow-and-r/ JJ Allaire: - https://github.com/jjallaire Twitter: @fly_upside_down https://twitter.com/fly_upside_down Related blog post: https://blog.rstudio.com/2018/02/06/tensorflow-for-r/

JJ Allaire