By many measures, TensorFlow has grown over the last year to become the most popular library for training machine-learning models. TensorFlow.jl provides Julia with a simple yet feature-rich interface to TensorFlow that takes advantage of Julia's multiple dispatch, just-in-time compilation, and metaprogramming capabilities to provide unique capabilities exceeding TensorFlow's own native Python API. This talk will demonstrate TensorFlow.jl by guiding listeners through training a realistic model of image captioning , showing how to 1) construct the model with native Julia control flow and indexing, 2) visualize the model structure and parameters in a web browser during training, and 3) seamlessly save and share the trained model with Python. No prior experience with TensorFlow is assumed.
