Deep Learning with R
summary
TensorFlow 2.1 is available with TPU support and a focus on eager execution so models are debuggable yet Autograph and tf.function convert Python into optimized graphs for fast inference. R bindings expose Keras, tf.data pipelines for GPU/TPU saturation, TensorBoard in notebooks, and a Keras Tuner R port announced the day before. SavedModel export enables deployment to mobile, browser or C++ inference; the org maintains about 92 GitHub repos of production models and tools.
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chapters (10)
- 0:00Introduction to TensorFlow
- 1:02TensorFlow 2 ecosystem
- 2:27Deep learning use cases
- 5:33Getting started with R
- 7:00Architecture and design
- 9:10Standardization and APIs
- 13:24Data pipelines with tf.data
- 15:41Deployment and tools
- 17:20Performance and collaboration
- 19:21Q&A and closing
