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Artificial Intelligence

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  • How to Build an Open Sourced Data Science Platform
    How to Build an Open Sourced Data Science Platform David Talby, CTO, Atigeo Recorded: Oct 26 2017 75 mins
    This talk shows how to build a scalable data science platform, using only free, commercially-friendly open source software. The end-to-end architecture covers interactive queries & visualization, machine learning & data mining, deploying models to production, and a full 24x7 operations toolset.

    Requirements include what an enterprise typically requires: Strong security (authentication, authorization, audit, encryption, multi-tenancy), active monitoring for both systems & data, backup & restore, user management (with LDAP integration), distributed deployment on commodity hardware, auto scaling, and self-healing when containers or services go down. Technologies covered include Spark, Hadoop, ElasticSearch, Kibana, Jupyter notebooks, TensorFlow, OpenScoring, Docker Swarm, and supporting tools.

    This talk is intended for practicing architects and technology leaders, who need to understand how to best leverage the open source ecosystem in this space and what it takes to integrate the available cutting-edge technologies into a cohesive, enterprise-grade and production-grade architecture.

    David Talby is Atigeo’s senior vice president of engineering, leading the R&D, product management, and operations teams. David has extensive experience in building and operating web-scale analytics and business platforms, as well as building world-class, agile, distributed teams.
  • Engaging Content Creation with Artificial Intelligence
    Engaging Content Creation with Artificial Intelligence Dhaval Bhatt, Founder and CEO of Resonate AI Recorded: Oct 19 2017 39 mins
    Content marketers struggle with creating engaging content. With social media automation tools, most of the hard-lifting on content distribution is solved. The hard thing about content marketing is not marketing, it's the creation. The age-old question still stands true: How to tell an engaging story that creates "know-like-trust" factor with your audience and results in engagement and sales.

    In this webinar, we will cover:
    1) How to identify which tone of voice is generating high engagement for your brand
    2) How to identify the type of story that resonates with your audience?
    3) How to identify the style of voice that generates high-engagement for your brand
  • Image recognition with deep learning
    Image recognition with deep learning Layla Tadjpour, Data Science Consultant, Ph.D. in Electrical Engineering from University of Southern California. Recorded: Oct 11 2017 39 mins
    In this webinar, we will learn about image recognition with deep learning. After a brief overview of what deep learning is, and why it matters, we will learn how to classify dogs from cats. That is, how to train a model to recognize dog images from cat images.

    We use Keras, an easy to use python deep learning library that sits on top of Tensorflow, and “fine-tuning”, a very important skill for any deep learning practitioner, to train a model to classify the images.

    Once we trained our model to classify dogs from cats images with high accuracy, we dig into the details of the trained model and look at its building blocks, i.e., Convolutional Neural Networks (CNN), Fully Connected Block and activation functions to develop an understanding of how the deep learning model works.
  • Data scientists: Can't live with them, can't live without them.
    Data scientists: Can't live with them, can't live without them. Wyatt Benno, CEO, DataHero Recorded: Aug 24 2017 45 mins
    There has been a flood of publicity around big data, data processing, and the role of predictive analytics in businesses of the future.
    As business operators how do we get access to these valuable business insights, even when there is not a data analyst around to walk us through their results?

    - Should your software emulate a data scientist?
    - Learn about the power of data visualizations.
    - Learn about creating value from disperse data sets.
  • Artificial Intelligence: Methods, Applications and Impacts
    Artificial Intelligence: Methods, Applications and Impacts Arinze Akutekwe, PhD Data Scientist, BAS EMEIA – Intelligent Enterprise - Analytics at Fujitsu Recorded: Aug 23 2017 49 mins
    Artificial intelligence has greatly changed the way we live since the 20th century. It involves the science and engineering of making machines intelligent and autonomous using computer programs.

    The processing power of computers has been on the exponential increase with cost of processors and storage decreasing. This has made research and developments efforts in AI areas such as deep learning, once thought to be impossible possible.

    In this webinar, we will examine current methods, application domains of specific methods, their impacts on our daily lives and try to answer questions on ethics of applying these technologies.
  • Hunting Criminals with Hybrid Analytics, Semi-supervised Learning, & Feedback
    Hunting Criminals with Hybrid Analytics, Semi-supervised Learning, & Feedback David Talby, CTO, Atigeo Recorded: Aug 23 2017 62 mins
    Fraud detection is a classic adversarial analytics challenge: As soon as an automated system successfully learns to stop one scheme, fraudsters move on to attack another way. Each scheme requires looking for different signals (i.e. features) to catch; is relatively rare (one in millions for finance or e-commerce); and may take months to investigate a single case (in healthcare or tax, for example) – making quality training data scarce.

    This talk will cover a code walk-through, the key lessons learned while building such real-world software systems over the past few years. We'll look for fraud signals in public email datasets, using IPython and popular open-source libraries (scikit-learn, statsmodel, nltk, etc.) for data science and Apache Spark as the compute engine for scalable parallel processing.

    David will iteratively build a machine-learned hybrid model – combining features from different data sources and algorithmic approaches, to catch diverse aspects of suspect behavior:

    - Natural language processing: finding keywords in relevant context within unstructured text
    - Statistical NLP: sentiment analysis via supervised machine learning
    - Time series analysis: understanding daily/weekly cycles and changes in habitual behavior
    - Graph analysis: finding actions outside the usual or expected network of people
    - Heuristic rules: finding suspect actions based on past schemes or external datasets
    - Topic modeling: highlighting use of keywords outside an expected context
    - Anomaly detection: Fully unsupervised ranking of unusual behavior

    Apache Spark is used to run these models at scale – in batch mode for model training and with Spark Streaming for production use. We’ll discuss the data model, computation, and feedback workflows, as well as some tools and libraries built on top of the open-source components to enable faster experimentation, optimization, and productization of the models.
  • Using Predictive Analytics to Empower Customer Experience
    Using Predictive Analytics to Empower Customer Experience Patrick Rice, CEO, Lumidatum Recorded: Aug 23 2017 44 mins
    Predictive analytics & artificial intelligence are transforming the way all companies connect with customers. The customer experience is on the verge of being completely redefined with A.I.

    In this talk, you will learn what it takes for a business to deliver this new 21st-century customer conversation. Patrick will cover:

    - Collecting & Managing CX data - What I learned analyzing 3 billion records at Amazon
    - We have the data, now what? AI & the Future of Customer Experience
    - How to use Predictive Analytics to find the right customer on the right channel
    - 3 ways to drive ROI for your business by applying AI to improve the Customer Experience

    Patrick is CEO of Lumidatum which helps CMOs apply artificial intelligence to the customer experience in order to grow revenue. He has been in the data and analytics space for over a decade including running a machine learning and advanced analytics team at Amazon.
  • Putting AI into LeAdershIp
    Putting AI into LeAdershIp Prof. Dr. Michael Feindt, Founder & Chief Scientific Officer, Blue Yonder Recorded: Aug 23 2017 45 mins
    Artificial Intelligence (AI) is not a technology for the future; it’s a huge business opportunity for today. But how can your organisation become a trailblazer for AI innovation, transforming the way you work to deliver immediate – and lasting – bottom line value?

    Former CERN scientist, Prof. Dr. Michael Feindt, is one of the brightest minds in Machine Learning. Join him for a 30-minute masterclass in how to apply AI to your business.

    You’ll learn how AI can:
    •Make sense of market and customer complexity, to deliver quick and effective decisions every single day
    •Increase workforce productivity to improve output and staff morale
    •Enhance decision-making and forecasting accuracy, for operational efficiency and improved productivity
    •Be implemented into your business quickly, easily, with minimal disruption

    Michael will also share real-life examples of how international businesses are using AI as a transformation tool, from his experience as founder of market-leading AI solution provider, Blue Yonder.
  • AI for Innovation and Transformation
    AI for Innovation and Transformation Angelique Mohring, GainX Recorded: Jul 20 2017 40 mins
    Big data, to date, has been focused on external drivers such as customer economics and market intelligence. To compete, market leaders must leverage machine learning and artificial intelligence to aggressively ingest and respond to both external and internal data-centric insights. There is no exception. FSI must both innovate and transform to stay relevant.

    Today, Angelique Mohring discusses why FSIs must leverage AI to master the future by driving greater ROI on multi-million/billion dollar innovation and transformation spend.
  • Radiant, a powerful open source Shiny application for business analytics
    Radiant, a powerful open source Shiny application for business analytics Ali Marami Chief Data Scientist Recorded: Jul 11 2017 58 mins
    Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results.

    The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs. In this webinar, we review the tools available in Radiant and explain how easily you can use this tool without any setup or installation on your system.

    Radiant key features:

    • Explore: Quickly and easily summarize, visualize, and analyze your data
    • Run different models: OLS, GLM, Neural Networks, Naïve Bayes and CART.
    • Cross-platform: It runs in a browser on Windows, Mac, and Linux
    • Reproducible: Recreate results and share work with others as a state-file or an Rmarkdown report
    • Programming: Integrate Radiant's analysis functions with your own R-code
    • Context: Data and examples focus on business applications

    After this webinar you will learn:

    • Data manipulation and running different models
    • How to run advanced analytics in a browser on any device even in your tablet or iPad.


    Presenter bio:

    Ali has a Ph.D. in Finance from the University of Neuchatel in Switzerland and a BS in Electrical Engineering. He has extensive experience in financial modeling, quantitative modeling, and financial risk management in several US banks.

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