Silicon Valley meetup: Deep Dive into TensorFlow

Altoros is a consultancy employing more than 300 developers and architects in several locations worldwide  to help Global 2000 companies digitize revenue streams and shape customer experience. Altoros offers a methodology, technology building blocks, and end-to-end solution development. Our unique specialty is turning the latest advancements in cloud-native application development, customer analytics, behavioral science, blockchain, and artificial intelligence into products, platforms, and ecosystems with a high density of sustainable, crushing competitive advantage.

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Many thanks to <A HREF="https://www.linkedin.com/redirect?url=http%3A%2F%2Fwww%2Ematterport%2Ecom&urlhash=4zzW" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Matterport</A> 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 TensorFlow by 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 <A HREF="http://question.ai/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">question.ai</A>, 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.

Matterport Inc.

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Silicon Valley meetup: Deep Dive into TensorFlow

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Silicon Valley meetup: Deep Dive into TensorFlow

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