After a few months of covering new features in TensorFlow and cutting-edge papers, we are returning to some more basics of Deep Learning this month. We will look at dealing with structured tabular data and some of the techniques around doing that in TensorFlow.
Planned Talks :
"Using Feature Columns with tf.Keras" - Sam Witteveen
Using tabular data is often tricky and painful in TensorFlow and Keras. In TensorFlow 1.12 Feature Columns have been introduced for handling data from data frames such as Pandas. Sam will show how to use them to create new features with tf.data for feeding into Keras layers.
'Embed all the things" - Martin Andrews
Embeddings are extremely powerful and their power goes beyond just using them for text. In this talk, Martin will show using embeddings for a variety of objects including embeddings for graph models.
"MLBlocks demo" - Rishabh Anand and Sarvasv Kulpati
MLBlocks - a company that won Ideasinc 2018 and won $10000 in seed money from NTU that makes machine learning more accessible to companies. MLBlocks contains tool, that enables people to create and deploy image models in the cloud without touching a line of code.
