8:30 am - 9:00 am
Registration & Networking
9:00 am - 9:30 am
Traditionally, Renat Khasanshyn will open TesnorFlow Mega Meetup
9:30 am - 10:15 am
Building Answer Bot in cloud, using TensorFlow by Avkash Chauhan, H2O
With the recent advances into neural networks capabilities to process text and audio data we are very close creating a natural human assistant. TensorFlow from Google is one of the most popular neural network library, and using Keras you can simplify TensorFlow usage. TensorFlow brings amazing capabilities into natural language processing (NLP) and using deep learning, we are expecting bots to become even more smarter, closer to human experience. In this technical discussion, we will explore NLP methods in TensorFlow with Keras to create answer bot, ready to answers specific technical questions. You will learn how to use TensorFlow to train an answer bot, with specific technical questions and use various AWS services to deploy answer bot in cloud.
10:15 am - 10:45 am
Machine Learning on VMware vSphere 6 with NVIDIA GPUs by Lan Vu and Uday Kurkure, VMware
Efficient deployment of GPU-based machine learning, especially deep learning, in cloud environments is an important focus of research and development. As the leader in cloud infrastructure software, VMware provides multiple solutions that optimize performance and enhance flexibility for machine learning workloads. We'll present the results of our research on machine learning with NVIDIA GPUs on VMware's vSphere platform. You will learn different ways to deploy GPU-based workloads developed with popular machine learning frameworks like TensorFlow in a virtualized environment using VMware DirectPath I/O and NVIDIA GRID vGPU solutions. We'll discuss how to mix workloads to maximize resource utilization and deployment flexibility by running machine learning together with other workloads on the same server. Finally, we'll present the performance characteristics of machine learning with GPUs for multiple use cases and at different scales in virtualized cloud data centers.
10:45 am - 11:30 am
Dive Deeper in Finance with TensorFlow by Daniel Egloff, QuantAlea
The talk starts with an overview of promising Deep Learning applications in Finance. We then focus on deep (variational) recurrent autoencoders show how they can learn hidden representations of unlabeled data and generate new data. This opens interesting new applications in anomaly detection, risk analysis, price prediction and algorithmic trading. We explore some of these use cases with real FX data and show how to build the models using frameworks such as Tensorflow.
11:30 am - 12:00 pm
Fireside chat #1. TensorFlow in the Financial Services Industry: Theory and Practice
This Fireside chat will elaborate on using TensorFlow in finance.
- Sanjay Agarwal, Chief Data Scientist at Discern Analytics;
- Charanjeet Ajmani, Founder and CEO of Up IQ;
- Ali Loghmani, Founder of Planner; - Geeta Chauhan, Consulting CTO at SVSG
12:00 pm - 12:40 pm
12:40 pm - 1:30 pm
Practical Reinforcement Learning with TensorFlow by Illia Polosukhin, XIX.ai
The description will be added shortly
1:30 pm - 2:15 pm
Logical Graphs - Control Flow Operations in TensorFlow by Sam Abrahams, Metis
Sometimes your models need to perform different computations depending on intermediate results or random chance. However, placing this sort of logic in the Python layer adds extra complexity and overhead to your code. TensorFlow provides a number of operations to help create graphs with built-in logical branching structure. In this talk, we'll look at what benefits these operations provide and how to make use of them.
2:15 pm - 3:00 pm
TensorFlow visualization with Guild AI by Garrett Smith, Guild AI
Guild AI is an open source project released under the Apache 2.0
license that streamlines TensorFlow development and provides a dynamic
visual interface for monitoring and evaluating model trainings. Guild
AI reduces complex operations to simple commands, provides
immediate feedback on training progress, and lets you compare model
performance at-a-glance. In this presentation, Garrett Smith, project
lead for Guild AI, will walk through Guild's features and architecture
and demonstrate how Guild fits within TensorFlow developer and team
3:00 pm - 3:30 pm
3:30 pm - 4:00 pm
Fireside chat #2. TensorFlow in Manufacturing and Industry 4.0: Theory and Practice
This Fireside chat will elaborate on using TensorFlow in manufacturing industry.
- Pradeep Nagaraju, Software Engineer at Splunk; - James Sebastian Henrikson, Machine Learning and Data Analysis at Lawrence Livermore National Laboratory (LLNL); Dr. Karthik Kappaganthu, Senior Scientist at Johnson Controls International, Innovation Garage; Nolan Browne, Managing Partner at ADL Ventures
4:00 pm - 4:40 pm
How to run TensorFlow cheaper in the cloud using Elastic GPUs by Subbu Rama, Bitfusion
TensorFlow is great for deep learning development and training. Combined with GPUs, it makes for fast dev and fast execution, but doesn’t make it easy to switch from a laptop or cloud execution context with CPUs to GPUs and back, to keep development and training costs low. We’ll look at best practices on doing deep learning model development with Jupyter and TensorFlow, and then show how to work with CPUs and GPUs more easily by using Elastic GPUs and quick-switching between custom kernels. At Bitfusion, we’ve developed custom kernels coupled with network-attached Elastic GPUs to make it quick and easy to switch from CPUs to GPUs and back again with only a couple clicks, and something that can be used locally or in the cloud.
4:40 pm - 5:00 pm
Toward and ML-Assisted Tumor Board by Dave Singhal and Raghavan Kripakaran, Light Field Interactive
5:00 pm - 5:30 pm