TensorFlow is a wonderful tool for rapidly implementing neural networks. In this presentation, we will learn the basics of TensorFlow and show how neural networks can be built with just a few lines of code. We will highlight some of the confusing bits of TensorFlow as a way of developing the intuition necessary to avoid common pitfalls when developing your own models. Additionally, we will discuss how to roll our own Recurrent Neural Networks. While many tutorials focus on using built in modules, this presentation will focus on writing neural networks from scratch enabling us to build flexible models when Tensorflow’s high level components can’t quite fit our needs.
About Nathan Lintz:
Nathan Lintz is a research scientist at indico Data Solutions where he is responsible for developing machine learning systems in the domains of language detection, text summarization, and emotion recognition. Outside of work, Nathan is currently writting a book on TensorFlow as an extension to his tutorial repository https://github.com/nlintz/TensorFlow-Tutorials
Link to video https://www.youtube.com/watch?v=op1QJbC2g0E&feature=youtu.be