The TensorFlow Dev Summit was hosted at Google’s Headquarters in Mountain View, California. TensorFlow team and machine learning experts from around the world met for a full day of technical talks, demos, and conversations.
They had an incredible lineup of talks from the TensorFlow team. The keynote was delivered by Jeff Dean followed by technical talks and demos by the product and engineering team.
Jeff Dean, Rajat Monga, and Megan Kacholia delivered the keynote address at the inaugural TensorFlow Dev Summit. They discuss:
– The origins of TensorFlow
– Progress since TensorFlow’s open-source launch
– TensorFlow’s thriving open-source community
– TensorFlow performance and scalability
– TensorFlow applications around the world… and share some exciting announcements!
TensorFlow is a fast, flexible, and scalable open-source machine learning library for research and production. The TensorFlow community is thriving. They are thrilled to see the adoption and the pace of machine learning development by people all around the world. TensorFlow is an open-source project for everyone and we’re looking forward to building it into something more useful in collaboration with the worldwide community!
Keras has the goal to make deep learning accessible to everyone, and it’s one of the fastest growing machine learning frameworks. Join Francois Chollet, the primary author of Keras, as he demonstrates how Keras can be used in TensorFlow through a video QA example.
Keras: An API spec for building deep learning models across many platforms.
What is happening?
*Keras compatibility modele introduced in TensorFlow
*Core TensorFlow layers and Keras layers are the same objects
*Keras Model available in core TensorFlow
*Deep integration with TensorFlow features (e.g.Experiment)
What this means for Keras users?
*TensorFlow-Keras built on top of core TensorFlow features
*Easily mix-and-match pure TensorFlow & Keras functionality
*With TensorFlow, Kereas users gain access tonew features:
-Distributed training
-Cloud ML
-Hyperparameter tuning
-TF-Serving
What this means for TensorFlow users
*Gain access to Keras as a high-level API for model definition
*Without any loss of flexibility
-Deep compatibility of core TF & tf.keras
*Re-use any existing Keras codebase without stepping outside of your TF workflow
-Experiement, Cloud ML, TF-Serving
“A previously very hard problem, made accessible to anyone with basic Python scripting abilities”
