Google Asia Pacific
70 Pasir Panjang Road, #03-71, Mapletree Business City, · 117371
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.
Talks will start at 7:00pm and end at around 9:00pm, at which point people normally come up to the front for a bit of a chat with each other, and the speakers.
As always, we're actively looking for more speakers for future events - both '30 minutes long-form', and lightning talks. For the lightning talks, we welcome folks to come and talk about something cool they've done with TensorFlow and/or Deep Learning for 5-10mins (so, if you have slides, then #max=10). We believe that the key ingredient for the success of a Lightning Talk is simply the cool/interesting factor. It doesn't matter whether you're an expert or and enthusiastic beginner: Given the responses we have had, we're sure there are lots of people who would be interested to hear what you've been playing with. Please suggest yourself here :