MEETUPS & TRAINING
COMING EVENTS

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PAST EVENTS

Prepare.Ai Annual Conference!

Date: May 8, 2018

Location: Eric P. Newman Center in St. Louis, MO

Prepare.Ai Annual Conference!

The Prepare.Ai Conference is the Midwest’s premier forum to explore, understand, and influence how Artificial Intelligence will impact our lives and businesses.

We will have exciting keynotes and sessions with speakers from Amazon, IBM, WashU, SLU, IDEO, Maritz, Showtime Networks, Centene, and many others. Topics covered will be: machine learning, predictive analytics, NLP, data science, UX design, computer vision, toolsets, economics, ethics, and real-world business applications. Experts and newcomers alike will find valuable resources, insights, and networking opportunities.

Needs-based scholarships are available through the generous donations of our sponsors.

Registration fee: $299

Fast.Ai Courses – Local Study Group!

Start date: April 30, 2018

End date: June 30, 2018

Time: Weekly meetings Mondays 6:00pm

Location: CIC @ 20 S Sarah St, St. Louis, MO 63108

Fast.Ai Courses – Local Study Group!

Fast.ai is a free online course over 7 to 8 weeks on practical deeplearning with no assumptions on prior expertise. This study group intends to provide some structure and local mentorship to fast.ai cohorts. If you have been toying with the idea of kickstarting your foray into deeplearning, this could be a great avenue. What differentiates this initiative is opportunity of local mentorship and peer experience sharing.

Fast.ai is dedicated to making the power of deep learning accessible to all. Deep learning is dramatically improving medicine, education, agriculture, transport and many other fields, with the greatest potential impact in the developing world. For its full potential to be met, the technology needs to be much easier to use, more reliable, and more intuitive than it is today.

You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material.

Using Artificial Intelligence & NLP to Streamline the Workplace

Date: January 9, 2018

Time: 6:00 pm to 8:30 pm

Location: 4240 Duncan Ave. Saint Louis, MO Havana Room - 2nd Floor

Using Artificial Intelligence & NLP to Streamline the Workplace

St. Louis Machine Learning & Data Science Meetup

Artificial intelligence is poised to radically transform the modern workplace. David Karandish (CEO) and Dave Costenaro (Head of AI Design) from Ai Software will discuss trends on what is happening in AI, and specifically how AI and Natural Language Processing (NLP) can transform access to information for a company’s applications, documents, and people. The session will explore related technologies like text mining, named entity recognition, question answering, and neural nets, along with a discussion of system deployments to productize and pull it all together.

Please join us for networking and refreshments at 6:00 PM. The presentation will begin promptly at 6:30 PM.

NLP Meetup

Date: October 19, 2017

Time: 6:00 pm

Location: Monsanto North Campus 2291 Ball Dr, St Louis, MO

NLP Meetup

Session 1. Using Artificial Intelligence & Natural Language Processing (NLP) to Streamline the Workplace:
David Karandish and Dave Costenaro from Ai Software will discuss trends on what is happening with AI in the workplace, and specifically how AI and Natural Language Processing (NLP) can transform access to information for a company’s applications, documents, and people. The session will explore NLP technologies like text clustering/vectorization, named entity recognition, part of speech tagging, speech-to-text, text mining, and question answering.

Session 2. Language Translation using Deep Learning:
Google made a big leap in Language translation in 2016 by applying Deep Learning techniques. The architecture of the Neural Net was based on a Seq2Seq Long-Short Memory (LSTM) models with attention based decoders. Narayana Swamy will discuss the Google model and demo a simpler model with one encoder LSTM and one attention based decoder LSTM.