Tensorflow Dev Dummit 2017

 

Keynote

9:35-10:10am
Jeff Dean, Rajat Monga, and Megan Kacholia deliver 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

XLA: TensorFlow, Compiled!

10:10-10:55am
Speed is everything for effective machine learning, and XLA was developed to reduce training and inference time. In this talk, Chris Leary and Todd Wang describe how TensorFlow can make use of XLA, JIT, AOT, and other compilation techniques to minimize execution time and maximize computing resources.

Hands-on TensorBoard

11:00-11:20am
Join Dandelion Mane in this talk as they demonstrate all the amazing things you can do with TensorBoard. You’ll learn how to visualize your TensorFlow graphs, monitor training performance, and explore how your models represent your data.

TensorFlow High-Level API

11:20-11:35am
TensorFlow allows you to define models using both low- as well as high-level abstractions. In this talk, Martin Wicke will introduce Layers, Estimators, and Canned Estimators for defining models, and show the roadmap for their availability in core TensorFlow.

Integrating Keras & TensorFlow: The Keras Workflow, Expanded

11:35-11:50am
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.

TensorFlow at DeepMind

12:10-12:30pm
In this talk, Daniel Visentin from the DeepMind Applied team talks about DeepMind and TensorFlow. He’ll explain the importance of choosing a platform, the team’s choice to migrate to TensorFlow, and give a number of examples of how DeepMind uses TensorFlow.

Skin Cancer Image Classification

12:30-12:40pm
Join Brett Kuprel, and see how TensorFlow was used by the artificial intelligence lab and medical school of Stanford to classify skin cancer images. He’ll describe the project steps: from acquiring a dataset, training a deep network, and evaluating of the results. To wrap up, Brett will give his take on the future of skin cancer image classification.

Mobile and Embedded TensorFlow

12:40-1:10pm
Did you know that TensorFlow models can be deployed in iOS and Android apps, and even run on Raspberry Pi? In this talk Pete Warden will go through everything you need to know to make this happen, and provide some golden technical pro-tips on the way.

Distributed TensorFlow

2:10-2:40pm
TensorFlow gives you the flexibility to scale up to hundreds of GPUs, train models with a huge number of parameters, and customize every last detail of the training process. In this talk, Derek Murray will give you a bottom-up introduction to Distributed TensorFlow, showing all the tools available for harnessing this power.

TensorFlow Ecosystem: Integrating TensorFlow with Your Infrastructure

2:40-3:10pm
Generating input data, running distributed TensorFlow training, and serving models all involve other infrastructure components. Jonathan Hseu will describe integration for each of these steps.

Serving Models in Production with TensorFlow Serving

3:10-3:30pm
Serving is the process of applying a trained model in your application. In this talk, Noah Fiedel describes TensorFlow Serving, a flexible, high-performance ML serving system designed for production environments.

ML Toolkit

3:30-3:45pm
Tensorflow is an extremely powerful framework, yet has been missing packaged solutions that work out-of-the-box. In this talk, Ashish Agarwal will introduce a toolkit of algorithms that takes a step in that direction.

Sequence Models and the RNN API

4:05-4:35pm
In this talk, Eugene Brevdo will discuss the creation of flexible and high-performance sequence-to-sequence models. He’ll cover reading and batching sequence data, the RNN API, fully dynamic calculation, fused RNN cells for optimizations for special cases, and dynamic decoding.

Wide & Deep Learning: Memorization + Generalization with TensorFlow

4:35-4:50pm
Wide models are great for memorization, deep models are great for generalization — why not combine them to create even better models? In this talk, Heng-Tze Cheng will explain Wide and Deep networks and give examples of how they can be used.

Magenta: Music and Art Generation

4:50-5:00pm
Using TensorFlow for Music and Art Generation — that’s what Magenta is all about. Join Douglas Eck who will discuss art and music generation with deep nets and reinforcement learning. He’ll also talk about how artists and musicians fit in to the effort. Be prepared to see and hear inspired ML models.

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