• Building Reliable Data Lakes - Technical Workshop on Delta Lake

    [masked]pm: Networking & Food[masked]pm: Workshop by Data Bricks Presenter: Soham Bhatt, Solution Architect, Databricks, Inc. Workshop Agenda: Delta Lake is an open source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs. Delta Lake sits on top of Apache Spark. The format and the compute layer helps to simplify building big data pipelines and increase the overall efficiency of your pipelines. This workshop will provide a primer on Delta Lake and how to use Delta Lake to ensure consistency with ACID transactions. The workshop will use public lending data as an example and will walk through a notebook to showcase technology capabilities and features of Delta Lake. More info on Delta: https://databricks.com/product/databricks-delta

    4
  • DataOps for AI: How Startups Do Data The Right Way

    [masked]: Networking & Food[masked]: Main Talk (Brad Ito) If you have been trying to harness the power of data science and machine learning — but, like many teams, struggling to produce results — there’s a secret you are missing out on. All of those models and sophisticated insights require lots of good data, and the best way to get good data quickly is by using DataOps. What is DataOps? It’s a way of thinking about how an organization deals with data. It’s a set of tools to automate processes and empower individuals. And it’s a new DataOps Engineer role designed to make that thinking real by managing and building those tools. About the speaker: Brad Ito (https://www.linkedin.com/in/brad-ito/) is the CTO and Co-Founder of Retina AI, a data science company focused on predicting customer purchase behavior. He previously let the technical teams at three other digital marketing startups, has a degree in physics from MIT, and enjoys learning and building new ways for technology to service businesses, and through them, people!

    2
  • Ontologies, NLP and Deep Learning

    The University of Texas at Dallas

    [masked]pm: Networking & Food[masked]pm: Workshop by Chris Davis, Lymba and Faculty, UT Dallas Workshop Agenda: 1) Pros and Cons of various approaches to NLP 2) Role of Ontologies 3) Case Studies 4) Demo 5) Q&A ECSW Room 1.315, UT Dallas Park for free in PS4 parking structure (green zone) after 6pm. See below for parking info on UTD campus. https://utdallas.edu/visitors/visitor-parking/ Here's a map: https://www.utdallas.edu/services/download/Parking_Map.pdf A FREE one-day parking pass can be obtained at the visitor center (star on the PDF map). This allows visitors to park in any green labeled parking space on campus, including green spaces in the adjacent parking garage to ECSW building where the event is being held. PAID parking is available on the ground floor of parking garage PS4. There is an automated kiosk that takes all standard credit cards for a few dollars and does not require stopping by the visitor center. Sponsored by: Lymba.

    10
  • Sentiment Analysis with LSTMs using Tensorflow & Keras

    [masked]pm: Networking & Food[masked]pm: Workshop by Carlos Lara, technical founder and CEO of Poincaré Group. Workshop Agenda: 1) The Power of RNNs 2) RNN Architectures 3) LSTMs vs Vanilla RNNs 4) Learnable Parameters and Hyperparameters 5) Sentiment Analysis Exercise with TensorFlow's High-Level API Keras 6) Q&A Please bring your laptop with Tensorflow, Keras and Jupyter notebooks. We will announce additional information soon.

    8
  • Hands-on Workshop: Getting Started with Recurrent Neural Networks and LSTMs

    [masked]pm: Networking & Food[masked]pm: Workshop by Neelabh Pant Neelabh Pant is a data scientist at walmart in Plano. Neelabh earned his PhD from UT Arlington and is a frequent speaker at AI and ML events. His hands-on workshop will cover the following: 1) Introductions to RNNs and their applications 2) Long Short Term Memory Primer 3) Model development, Architecture RNNs are networks whose connections between neurons include loops, well-suited for processing sequences of inputs, which makes them highly effective in a wide range of applications, from handwriting, to texts, to speech recognition. We will begin by understanding the domain of the dataset, understand and summarize the problem. Right after that we will model a benchmark and finally build RNNs and LSTMs and compare the performance. This event is primarily for data scientists and engineers who are looking to get started with RNNs. Please bring your laptop with Tensorflow, Keras and Jupyter notebooks. We will announce additional information soon.

    9
  • Machine Learning in Autonomous Vehicles

    5445 Legacy Dr

    6 – 6:30 PM – Networking, pizza/ soft drinks 6:30 – 7:30 PM – Talk 7:30 – 8 PM - Q&A, post-talk-networking Location: 3rd Floor, Wipro Offices, 5445 Legacy Dr · Plano, TX Machine Learning in Autonomous Vehicles This talk will focus on machine learning problems in Autonomous Vehicles that are being solved at Wipro. We will analyze how machine learning can help address common problems in computer vision such as lane and pedestrian detection, static vs moving object recognition, navigation, obstacle avoidance and pothole/ hump detection in autonomous vehicles. High level details on nature of the problems, input data characteristics, regulatory concerns such as safety and how to match ML models with such problem domains will be covered. High level design of the solutions as well as considerations for choice of ML models will also be covered. Talk will cover neural networks and deep learning ML models in computer vision related to autonomous vehicles. Who should attend? ML practitioners, data scientists, business domain stakeholders from any industry domain Key questions the talk will answer? - How to analyze a given use case or problem to understand its ML related characteristics? - How to know which ML model is the best fit for a given problem? - What are the key considerations in matching ML problems and models? About the speaker - Hari Subramanian, Consulting Partner, Banking and Payments, Wipro Hari has 10+ years of experience in Fintech (emerging payments, mobile & digital banking, wallets, digital rewards, blockchain, AI/ ML, cognitive and robotic automation). He is a management consultant with 25+ years of global experience in financial services and telecommunications and has worked with several banks across the globe on fintech initiatives. He is an invited mentor for one of the leading accelerators at San Francisco. He is one of the top bloggers in Finextra and WIRED magazine (Hari's blog: http://www.harisubramanian.com/blog.html), has been interviewed by and published in Bank and Systems Technology, Banking Exchange, and Payments Source magazines and has 300+ industry leaders as followers in LinkedIn.

    5
  • Accelerate Deep Learning Skills with Kaggle Competitions

    [masked]: Networking & Food[masked]: Main Talk by John Miller - https://www.kaggle.com/jpmiller John Miller is a Principal at Benbrook Analytics and a data guru with many years of experience of participating in Kaggle competitions. John will talk about how Kaggle Competitions can help you get better with deep learning! His talk will cover the following: 1) How to best use Kaggle to learn about data science 2) How to get noticed, engage with the community and build your network 3) How to compete effectively and win!

    2
  • Artificial Intelligence in Healthcare

    13355 Noel Rd

    Artificial Intelligence is making significant inroads in healthcare, being used in identifying patients most at risk, automating routine clinical tasks and improving claims processing. We will look at how AI is impacting Healthcare including use cases in Clinical Support, Operations, and Financials. We will do a deep dive into the implementation of a Claims related AI solution, discussing the full Data Science Life Cycle including understanding the business process, data acquisition, data modeling, model deployment, and model maintenance. About the speaker: Yasir Bashir is Senior Architect with Saxony Partners. In his role, he helps customers with implementing Artificial Intelligence solutions to improve and automate business processes. https://www.linkedin.com/in/yasirbashir/ Location: One Galleria Tower Conference Room 1st Floor 13355 Noel Road Dallas, TX 75240 Here are some parking tips: - The easiest way to enter the building is through the mall. - Parking is free on the South end (Blue Parking Garage) near Old Navy, Banana Republic and The Gap. - Once you enter the mall, the entrance to Tower One is on the 3rd floor next to Champs. - Take the hallway down next to Champs and it leads you to the entry of Galleria Tower One. - Take the elevator down to the 1st floor.

    7
  • Primer on Quantum Machine Learning and AI

    CoreLogic - TX

    Quantum Computing is one of the most exciting areas of our time. It promises breakthroughs that are simply not possible with classical computing. One of the top use cases for Quantum Computing is boosting machine learning and AI algorithms. In this meetup we will have a primer of Quantum Computing and how it can be applied for ML and AI. Presenter: Doug Matzke, Ph.D. Dr Matzke earned his doctorate from UTD in Quantum Computing. He was Chairman of two PhysComp workshops on Physics and Computation and Principal Investigator SBIR Grants for Quantum/Neural Computing[masked]. Dr Matzke holds 13 patents.

    5
  • Customer Service AI Bots - A Case Study

    CoreLogic - TX

    A customer service and support bot improves customer satisfaction by providing quicker responses to customer questions and requests while reducing the operational cost. We will review a case study in automating replies for customer service requests to Hyla, Inc. The presentation will cover the problem definition, research methodology, machine learning algorithm choice, Python packages used, architecture and results. Speakers: * Chintan Shah, VP, Data Science, Analytics and Machine Learning at Hyla, Inc. Chintan Shah is the Vice President of Data Science, Analytics and Machine Learning at Hyla, Inc. He and his team lead development of HYLA Analytics platform that provides customers with actionable insights. Chintan has over 15 years of experience building and leading organizations centered on Analytics, Predictive Modeling, Business Intelligence, Big Data and Data mining. In his various roles, he developed and executed on enterprise information management, information delivery and data monetization strategies. * Richard DeVost, Data Scientist at Hyla, Inc. Richard has worked as a software engineer and consultant, an executive in a non-profit organization, and now engineers machine learning and artificial intelligence systems for Hyla, Inc. He earned a Master’s degree in Theoretical Mathematics from the University of Houston, and a PhD in Leadership Studies from Andrews University. His research interests include engineering best practices, application of quantitative and qualitative methods, organizational learning, and making work meaningful.

    3