• Deep Learning Course Demo: The Unreasonable Effectiveness of Deep Learning
    Hosted by NYC Data Science Academy (nycdatascience.com). The talk will serve as an overview of Jon Krohn's upcoming Deep Learning course (http://nycdatascience.com/courses/deep-learning/) at the NYC Data Science Academy, running from October 20th to December 1st. We’re now offering 30% tuition deduction for all women who participate in this Deep Learning course. Use code “DLFORWOMEN” at checkout. Deep Learning algorithms have become unprecedentedly transformative across industry verticals and are now the state-of-the-art across applications as diverse as image classification, natural language processing, generation of visual art, and game-playing. Almost unknown a few years ago, Deep Learning today powers countless everyday services from Tesla's Autopilot to Siri's voice recognition, and from Google Inbox's suggested replies to superhuman ability at the elaborate boardgames. Via analogy to biological neurons and the human brain, this talk is an introduction to Deep Learning. It features interactive demos and example code from the leading open-source Deep Learning library, TensorFlow, and its high-level API, Keras. Agenda: 6:00 - 6:30 pm - Food, Drinks & Mingling 6:30 - 6:40 pm - Edward Weiss, a student on the previous Deep Learning class cohort, will present on his capstone project and his experience taking the course 6:40 - 7:30 pm - Course Demo by Jon Krohn 7:30-8:00 pm - Q&A & Mingling Speaker: Jon Krohn (https://www.jonkrohn.com/) is Chief Data Scientist at the machine-learning startup untapt. He presents an acclaimed series of tutorials on artificial neural networks, including Deep Learning with TensorFlow LiveLessons (https://www.safaribooksonline.com/library/view/deep-learning-with/9780134770826/). Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. His forthcoming book, Deep Learning Illustrated, is being published on Pearson's Addison-Wesley imprint and will be distributed in 2018. About NYC Data Science Academy: Founded in 2014, the NYC Data Science Academy offers the highest quality in data science and data engineering training. Their top-rated and comprehensive curriculum has been developed by industry pioneers using experience from consulting, corporate and individual training and is approved and licensed by the NYS Department of Education. NYC Data Science Academy offers a variety of services including full-time bootcamps, part-time courses, corporate training, consulting, and career services. For more information visit http://nycdatascience.com (http://nycdatascience.com/)

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

  • Detecting Misconduct and Malfeasance within Financial Institutions
    We're excited to partner with ACM NY (www.meetup.com/ACM-NY) and Dataiku Meetup Group (https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY) to co-host this event. Schedule: 6:00pm: Pizza + Beer + networking 6:15pm: Dr. Panos Ipeirotis, Professor at NYU 7:15pm: Alexander Wolf, Data Scientist at Dataiku Abstracts: Detecting Misconduct and Malfeasance within Financial Institutions by Dr. Ipeirotis: Misbehavior in the online world manifests itself in several forms, and often depends on the domain at hand. In the financial domain, firms have the regulatory obligation to self-monitor the activities of their employees (e.g., emails, chats, phone calls), in order to detect any form of misconduct. Some forms of misconduct are illegal activities (e.g., insider trading, bribery) while others are various forms of policy violations (e.g., following improper security practices, or inappropriate language use). Traditionally, and due to ease of understanding and implementation, firms deployed relatively archaic, rule-based systems for employee surveillance. Such rule-based systems generate a large number of false positive alerts, and are hard to adapt in changing environments. More recent techniques aimed at solving the problem by simply transitioning from simple rule-based techniques to statistical machine learning approaches, trying to treat the problem of misconduct detection as a single-document classification problem. We discuss why approaches that try to identify misconduct within single documents are destined to fail, and we present a set of approaches that focus on actors, connections among actors, and on cases of misconduct. Furthermore, we highlight the importance of having a "human in the loop'' approach, where humans are both guided and guide the system at the same time, in order to detect malfeasance faster, and also adapt to changing environments; we also show how humans can play an important role for detecting shortcomings of existing machine-learning-based malfeasance-detection systems, and how humans can be incentivized to detect such shortcomings. Our multifaceted approach has been used in real environments within both big, multinational and smaller financial institutions; we discuss practical constraints and lessons learned by operating in such non-tech, highly regulated environments. Transformers in NLP- Building a Neural Machine Translator by Alexander Wolf: In the past, Natural Language Processing has been dominated by Recurrent Models, but now a new architecture called the Transformer has been shown to dominate NLP in many domains. The revolutionary new model uses no recurrence but attention only and develops state of the art accuracy in a fraction of the training time compared to other Deep Learning models. Alex has built a translator using this architecture and will give introduction plus deep dive of how it works along, will explain how it can overcome pitfalls of RNN/LSTM models and will present a history of NLP/ Translation systems. Bios: Panos Ipeirotis is a Professor and George A. Kellner Faculty Fellow at the Department of Information, Operations, and Management Sciences at Leonard N. Stern School of Business of New York University. He received his Ph.D. degree in Computer Science from Columbia University in 2004. He has received nine "Best Paper" awards and nominations, a CAREER award from the National Science Foundation, and is the recipient of the 2015 Lagrange Prize in Complex Systems, for his contributions in the field of social media, user-generated content, and crowdsourcing. Alex is a Data Scientist at Dataiku, working with clients around the world to organize their data infrastructures and deploy data-driven products into production. Prior, he worked on software and business development in the tech industry and studied Computer Science and Statistics at Dartmouth College. He's passionate about the latest developments in Deep Learning/Tech and enriches Dataiku's NLP features.

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

  • [Partner Event]Peeking into the On-Demand Economy
    Details We're excited to partner with ACM NY (www.meetup.com/ACM-NY) for a talk opening up the black box of the on-demand economy by Ming Yin, Post-doctoral Researcher at Microsoft Research. Co-hosted with Dataiku Meetup Group (https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY) Peeking into the On-Demand Economy: Today, an increasing number of digital and mobile technologies have emerged to match customers, in almost real time, with a potentially global pool of self-employed labor, leading to the rise of the on-demand economy, which has brought about dramatic changes in our society. It creates new business models and new dynamics of labor allocation. It enables new models of computation, that is, human-in-the-loop computing. And it leads to new forms of knowledge creation—people all over the world are contributing to scientific studies in dozens of fields, either by making scientific observations as amateur scientists or by participating in online experiments as subjects. Despite its already significant impacts, the on-demand economy has still been considered as a black-box approach to soliciting labor from a crowd of on-demand workers. Little is known about these workers and their aggregated behavior. In this talk, using the on-demand crowdsourcing platforms as an example, Ming present her attempts and findings on opening up this black box with a combination of experimental and computational approaches, with focuses on understanding who the on-demand workers are, how to model their unique working behavior, and how to improve their work experience. Ming Yin, Postdoctoral Researcher at Microsoft Research: Ming Yin is currently a postdoctoral researcher at Microsoft Research New York City. Starting in Fall 2018, she will join Purdue University as an Assistant Professor in the Department of Computer Science. Ming’s primary research interests lie in the interdisciplinary area of social computing and crowdsourcing. Her research has contributed to better understanding human behavior in social computing and crowdsourcing systems through large-scale online behavior experiments, as well as incorporating the empirical insights from the behavioral data into developing models, algorithms, and interfaces to facilitate the design towards better systems. More broadly, her research connects to the fields of applied artificial intelligence and machine learning, computational social science, human-computer interaction and behavioral economics. Ming’s work is published in top venues like WWW, CHI, AAAI and IJCAI. Ming is named as a Siebel Scholar (Class of 2017), and she has received Best Paper Honorable Mention at the ACM Conference on Human Factors in Computing Systems (CHI’16). Ming obtained her bachelor's degree from Tsinghua University, Beijing, China, in 2011, and completed her PhD at Harvard University in 2017.

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

  • [Partner Event] ML and Deep Learning Use Cases from the Trenches
    Co-hosted with Dataiku Meetup Group (https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY) To attend this event on site, please sign up at: https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY/events/249943007/ ------------------------------- Join us for a deep dive into business applications of machine learning and deep learning (intermediate level) at Dataiku and WeWork! Talk #1: Building a Translator Using a New Deep Learning Model for NLP Deep Learning in NLP has been dominated in the past years by recurrent and convolutional models. But other models emerge to improve translation quality and performance. Alex has developed a translator for his team and clients using a new neural network architecture called the Transformer. Unlike traditional translator models, this one solely focuses on attention instead of recurrence and develops powerful NLP models in a fraction of the training time. Alex is a Data Scientist at Dataiku, working with clients around the world to organize their data infrastructures and deploy data-driven products into production. He's passionate about the latest developments in deep learning. Tak #2: Powered by Machine Learning: Recommendation Systems at WeWork Team Rex at WeWork deploys intelligent applications to help our members create their life's work. One of our first products is a personalized newsfeed designed to surface the most relevant user-generated content for each member. This application is powered by a suite of recommendation models embedded within a novel multi-armed bandit-based ML platform. This framework allows for faster experimentation and more granular optimizations compared to a standard A/B testing framework. Karry Lu is a data scientist at WeWork with interests in recommendation systems, NLP and Bayesian inference. Prior lives include relapsed statistician, community organizer and failed writer. Alex Choi is a software engineer at WeWork, as well as a thought-leader, truth-seeker, and patriot.

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

    2 comments
  • FREE Sample Class from Bootcamp: Learn Data Science with scikit-learn
    Join the webinar via http://info.nycdatascience.com/online-info-session A wire connection is highly recommended. Test your connection here (https://admin.adobeconnect.com/common/help/en/support/meeting_test.htm). Join us on May 10th, 7:00 pm for a live online FREE Sample Class from bootcamp experience. We will be demonstrating how to use the scikit learn package in Python to assist with various machine learning tasks, a course we offer in the 12-Week Data Science Bootcamp (https://nycdatascience.com/data-science-bootcamp/). The workshop is not meant to give a mathematics overview of machine learning models. Students are not required to have coding or machine learning background to attend. A laptop with a working installation of Python 2.7/3.6 is recommended to be able to follow along with the hands on exercise covered in the workshop. This is also a great opportunity for you to have an in-depth look at what you expect to accomplish during the bootcamp. Audience members will also be welcome to field questions for our members in bootcamp as well as have questions answered about the admissions process from our Student Success Officer, Drace Zhan. ------------------------------------------ You can also apply to our Summer and Fall cohorts here (https://nycdatascience.com/data-science-bootcamp/). ------------------------------------------ The meeting agenda will be as follows: 6:45 - 7:00 pm - Early check-in, meet, and greet 7:00 - 7:10 pm - Introduction about NYC Data Science Academy and What We Do 7:10 - 7:45 pm - Sample Class: Learn Data Science with scikit-learn 7:45 - 8:00 pm - Questions from the Audience See you all there and we wish you the utmost success on your journey to becoming a Data Scientist! ------------------------------- About the NYC Data Science Academy Founded in 2014, the NYC Data Science Academy offers the highest quality in data science and data engineering training. Their top-rated and comprehensive curriculum has been developed by industry pioneers using experience from consulting, corporate and individual training and is approved and licensed by the NYS Department of Education. The program delivers a combination of lectures and real-world data challenges to its students and is designed specifically around the skills employers are seeking, including R, Python, Hadoop, Spark and much more. By the end of the program, students complete at least five real-world data science projects (http://nycdatascience.com/blog/category/student-works/) to showcase their knowledge to prospective employers. Students also participate in presentations and job interview training to ensure they are prepared for top data science positions in prestigious organizations. For more information visit http://nycdatascience.com (http://nycdatascience.com/)

    NYC Data Science Academy Live Online

    http://info.nycdatascience.com/online-info-session · New York, NY

    2 comments
  • FREE Sample Class from Bootcamp: NLP in Python
    Join the webinar via http://info.nycdatascience.com/online-info-session A wire connection is highly recommended. Test your connection here (https://admin.adobeconnect.com/common/help/en/support/meeting_test.htm). Join us on April 12th, 7:00 pm for a live online FREE Sample Class from bootcamp experience. We will be giving an overview of NLP foundations in Python, a course we offer in the 12-Week Data Science Bootcamp (https://nycdatascience.com/data-science-bootcamp/). Natural Language Processing appears black box to most people given how popular implementation tends to use models such as Neural Networks to predict various outcomes. However, even big brand name companies such as Google, Amazon, and the like still adhere to practical traditional methods. In this workshop, Drace Zhan, will walk students through the introduction of important foundations of NLP methodology as well as explore various topics within NLP such as Informational Retrieval and Word Embedding. Interested students should have Python 2.7/3.6 installed as well as be familiar with Jupyter notebook. This is also a great opportunity for you to have an in-depth look at what you expect to accomplish during the bootcamp. Audience members will also be welcome to field questions for our members in bootcamp as well as have questions answered about the admissions process from our Student Success Officer, Drace Zhan. ------------------------------------------ You can also apply to our Summer and Fall cohorts here (https://nycdatascience.com/data-science-bootcamp/). ------------------------------------------ The meeting agenda will be as follows: 6:45 - 7:00 pm - Early check-in, meet, and greet 7:00 - 7:10 pm - Introduction about NYC Data Science Academy and What We Do 7:10 - 7:45 pm - Sample Class: NLP in Python 7:45 - 8:00 pm - Questions from the Audience See you all there and we wish you the utmost success on your journey to becoming a Data Scientist! ------------------------------- About the NYC Data Science Academy Founded in 2014, the NYC Data Science Academy offers the highest quality in data science and data engineering training. Their top-rated and comprehensive curriculum has been developed by industry pioneers using experience from consulting, corporate and individual training and is approved and licensed by the NYS Department of Education. The program delivers a combination of lectures and real-world data challenges to its students and is designed specifically around the skills employers are seeking, including R, Python, Hadoop, Spark and much more. By the end of the program, students complete at least five real-world data science projects (http://nycdatascience.com/blog/category/student-works/) to showcase their knowledge to prospective employers. Students also participate in presentations and job interview training to ensure they are prepared for top data science positions in prestigious organizations. For more information visit http://nycdatascience.com (http://nycdatascience.com/)

    NYC Data Science Academy Live Online

    http://info.nycdatascience.com/online-info-session · New York, NY

  • [Partner Event] Search at GIPHY: Staying on Top of the Zeitgeist
    Co-hosted with Dataiku Meetup Group (https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY) Live stream available at: https://nycdatascience.adobeconnect.com/search-at-giphy/ ------------------------------- Hi search engine enthusiasts, We're excited to provide you with one of the brightest and trendiest examples in which search engines can make or break a product. Yael Elmatad, Lead Data Scientist at GIPHY, will explain how she and her team develop their search engine to remain at the top of the social media frenzy. Search at GIPHY: Staying on Top of the Zeitgeist While on the outside GIPHY search may seem like a standard search problem, being on the top of the zeitgeist is an increasingly viral world is a constant challenge. GIPHY has devised ways to make sure our users are getting the most relevant search via models that allow us to exploit known performant content while experimenting with potentially successful content. This model ensures relevant content is surfaced to the end user while still trying to explore the space to ensure that new and exciting content has a chance to propagate up towards the top of the page. Bio: Yael Elmatad is the Lead of the Search & Discovery Division at GIPHY. Prior to that she spent 4 years at Tapad (a Marketing Technology company based in NYC, acquired by Telenor in 2016) as a Senior Data Scientist. Yael earned her undergraduate degree in Chemistry from NYU in 2006 and graduated as Valedictorian of the College of Arts and Sciences. She earned her Ph.D. from UC Berkeley in Theoretical Physical Chemistry in 2011 focusing on the statistical physics of supercooled liquids. She then spent 2 years as an Assistant Professor/Faculty fellow in the Center for Soft Matter Research in the Physics Department of NYU where she conducted independent research and taught undergraduate Statistical Physics and Quantum Mechanics.

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

    1 comment
  • [Partner Event] Economic Forecasting with Machine Learning
    Co-hosted with Dataiku Meetup Group (https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY) Live stream available at: https://nycdatascience.adobeconnect.com/economic-forecasting-with-machine-learning/ To attend this event on site, please sign up at: https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY/events/246687115/ ------------------------------- Hi data science enthusiasts, In this Meetup, we'll delve into two use cases of data science for prediction. Jorie Koster-Hale, Data Scientist at Dataiku, will first present an award-winning project on crime prediction in Portland, Oregon. Nicolas Woloszko, Junior Economist at OECD, will then present the algorithm he and his team built to make GDP projections for G7 countries, which proved more reliable than the most well-known existing methods. Rent, Rain, and Regulations - What Predicts Crime in Portland, Oregon? Crime poses a particularly interesting data challenge. It is both geospatial and temporal, and may be affected by many different types of variables: weather, city infrastructure, population demographics, public events, and government policy. Jorie will delve into the last 6 years of crime in Portland, Oregon, pulling data from a variety of public data sets, including police reports, the US census, Foursquare, newspapers, and the weather. You will learn about how to merge, visualize, and model this type of complex data, using PostGIS, spatial mapping, time-series analyses, and machine learning. Finally, Jorie will discuss what most predicts crime - and what we can do to prevent it in the future. Economic Forecasting with Machine Learning GDP forecasting for the world’s major economies is no easy task, but introducing machine learning in the field of economic research opens up new possibilities. Nicolas and his team created a forecasting algorithm – dubbed Adaptive-GBT with Predictive Intrapolation (PI) – that is specifically tailored for macroeconomic forecasting, and draws from both existing machine learning techniques and original contributions to the field. He will present their research and discuss the 2018 forecasts, as well as to what extent it can help assess the impact of current macroeconomic policies. Speakers bios: Jorie Koster-Hale is a broadly-trained data scientist at Dataiku with expertise in healthcare data, neuroscience, and machine learning. She is an award-winning researcher and instructor. Prior to joining Dataiku, she completed her Ph.D. in Cognitive Neuroscience at Massachusetts Institute of Technology and worked as a Postdoctoral Fellow at Harvard. Jorie currently resides in Paris where she eats lots of pains au chocolat. Nicolas Woloszko is a data scientist and an economist. He joined the OECD in September 2016, where he initiated an innovative project that aims at bridging the gap between machine learning and economics. Prior to joining the OECD, Nicolas has spent time in academia and consulting. He has a dual background in economics and applied maths.

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  • Deep Learning Course Demo: The Unreasonable Effectiveness of Deep Learning
    Hosted by NYC Data Science Academy (nycdatascience.com). The talk will serve as an overview of Jon Krohn's upcoming Deep Learning course (http://nycdatascience.com/courses/deep-learning/) at the NYC Data Science Academy, running from March 3rd to April 7th. Deep Learning algorithms have become unprecedentedly transformative across industry verticals and are now the state-of-the-art across applications as diverse as image classification, natural language processing, generation of visual art, and game-playing. Almost unknown a few years ago, Deep Learning today powers countless everyday services from Tesla's Autopilot to Siri's voice recognition, and from Google Inbox's suggested replies to superhuman ability at the elaborate boardgame Go. Via analogy to biological neurons and human vision, this talk is an introduction to Deep Learning. It features interactive demos and example code from the leading open-source Deep Learning library, TensorFlow, and its high-level API, Keras. Agenda: 6:00 - 6:30 pm - Food, Drinks & Mingling 6:30 - 6:40 pm - Richard Sheng, a student on the previous Deep Learning class cohort, will present on his capstone project and his experience taking the course 6:40 - 7:30 pm - Course Demo by Jon Krohn 7:30-8:00 pm - Q&A & Mingling Speaker: Jon Krohn (https://www.jonkrohn.com/) is Chief Data Scientist at the machine-learning startup untapt. He presents an acclaimed series of tutorials on artificial neural networks, including Deep Learning with TensorFlow LiveLessons (https://www.safaribooksonline.com/library/view/deep-learning-with/9780134770826/). Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. His forthcoming book, Deep Learning Illustrated, is being published on Pearson's Addison-Wesley imprint and will be distributed in 2018. About NYC Data Science Academy: Founded in 2014, the NYC Data Science Academy offers the highest quality in data science and data engineering training. Their top-rated and comprehensive curriculum has been developed by industry pioneers using experience from consulting, corporate and individual training and is approved and licensed by the NYS Department of Education. NYC Data Science Academy offers a variety of services including full-time bootcamps, part-time courses, corporate training, consulting, and career services. For more information visit http://nycdatascience.com (http://nycdatascience.com/)

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

  • Live Online Info Session: 12-Week Data Science Bootcamp
    Join the webinar via http://info.nycdatascience.com/online-info-session A wire connection is highly recommended. Test your connection here (https://admin.adobeconnect.com/common/help/en/support/meeting_test.htm). Join us on February 8th, 7:00 pm for a live online info session. We will be giving a walkthrough of the four project deliveries and samples of how projects look as well as a quick demo of workflow/code within the projects. This is a great opportunity for you to have an in-depth look at what you expect to accomplish during the bootcamp. Audience members will also be welcome to field questions for our members in bootcamp as well as have questions answered about the admissions process from our Student Success Officer, Drace Zhan. ------------------------------------------ You can also apply to our Spring and Summer cohorts here (https://nycdatascience.com/data-science-bootcamp/). ------------------------------------------ The meeting agenda will be as follows: 6:45 - 7:00 pm - Early check-in, meet, and greet 7:00 - 7:10 pm - Introduction about NYC Data Science Academy and What We Do 7:10 - 7:45 pm - A walkthrough of projects 7:45 - 8:00 pm - Questions from the Audience See you all there and we wish you the utmost success on your journey to becoming a Data Scientist! ------------------------------- About the NYC Data Science Academy Founded in 2014, the NYC Data Science Academy offers the highest quality in data science and data engineering training. Their top-rated and comprehensive curriculum has been developed by industry pioneers using experience from consulting, corporate and individual training and is approved and licensed by the NYS Department of Education. The program delivers a combination of lectures and real-world data challenges to its students and is designed specifically around the skills employers are seeking, including R, Python, Hadoop, Spark and much more. By the end of the program, students complete at least five real-world data science projects (http://blog.nycdatascience.com/category/student-works/) to showcase their knowledge to prospective employers. Students also participate in presentations and job interview training to ensure they are prepared for top data science positions in prestigious organizations. For more information visit http://nycdatascience.com (http://nycdatascience.com/)

    NYC Data Science Academy Live Online

    http://info.nycdatascience.com/online-info-session · New York, NY