Name: Deep Dive into TensorFlow
Start: 2016-09-27T18:30:00-07:00
End: 2016-09-27T21:30:00-07:00
Location: Matterport Inc.

Your First and Last name (as on your government issued ID), email and company name are REQUIRED for this meetup, so please proceed to RSVP at EVENTBRITE.COM. (https://www.eventbrite.com/e/silicon-valley-meetup-deep-dive-into-tensorflow-tickets-27698480902)

Many thanks to Matterport (https://www.linkedin.com/redirect?url=http%3A%2F%2Fwww%2Ematterport%2Ecom&urlhash=4zzW) for hosting and sponsoring the TensorFlow meetup!

Agenda:

6:30 - Doors open. Networking. Members meet each other. Pizza and beer

7:00 - Natural Language Processing in TensorFlowby Junling Hu

7:30 - Q&A break

7:40 - How Distributed Computing Helps Solve Machine Learning Computationally Intensive Tasks by Ivan Zone

8:10 - Q&A break

8:20 - Wrap-up.

DETAILED AGENDA:

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Natural Language Processing in TensorFlow

In this talk, we will review how TensorFlow supports a wide range of Natural language processing tasks. These include: Word embedding, language modeling, part-of-speech tagging, parsing, and translation. We will go through the major TensorFlow APIs and how they be used to accomplished the NLP asks. Junling will give a quick review on recurrent neural works and show some demos on NLP tasks in action.

About the presenter:

Junling Hu, a leading expert in artificial intelligence and data science, founder of question.ai (http://question.ai/), has built natural language application based on deep learning. She is the author of an upcoming book on artificial intelligence and deep learning. Prior to her current effort, she was Director of Data Mining at Samsung, building large-scale data mining solutions for business. She has also led data science teams in PayPal and eBay, creating enterprise solutions based on recommender systems, text mining and predictive modeling.

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How Distributed Computing Helps Solve Machine Learning Computationally Intensive Tasks

We have a marketplace that TensorFlow users can use to speed up their workflow and increase productivity.
It’s easier to use and less expensive than traditional cloud solutions.
It takes advantage of our distributed network, which is made up of other Machine Learning professionals and gamers contributing their computing resources.

Untapped computing resources of people’s laptops and PCs add up to 3-4x the total size of the global computing market (expected to be over $350B in 5 years). SuchFlex built a one-click solution for gamers to contribute their idle compute resources with the network. This provides the supply-side of SuchFlex’s computing resources. On the demand-side, SuchFlex built a service for Machine Learning professionals to solve their computationally intensive tasks on the network. Our Alpha Product is a one-click solution for TensorFlow.

The Problem is:

- Machine Learning requires computationally intensive tasks
- Using your own device is time consuming and inconvenient 
- Cloud services are expensive and clumsy

The Solution is:

One-Click solution for TensorFlow software:

- Save Money
- Save Time
- Increase Productivity

About the presenter:

Ivan Zone, Founder and CEO at SuchFlex
BSc in Computer Science and a Wharton MBA. Former Quant Developer and Trader at a Manhattan-based $4B Quant Hedge Fund. Very familiar with building and trading in digital markets. Passionate about building a peer-to-peer computing market to make the benefits of distributed computing accessible to everyone.

Your First and Last name (as on your government issued ID), email and company name are REQUIRED for this meetup, so please proceed to RSVP at EVENTBRITE.COM. (https://www.eventbrite.com/e/silicon-valley-meetup-deep-dive-into-tensorflow-tickets-27698480902)

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Deep Dive into TensorFlow

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