• Webinar: Introduction to Deep Learning Models for Computer Vision

    We want to invite you to participate in the FREE ODSC Webinar! Date: October 10 Time: 1 pm - 2 pm EST To access this webinar, please register using the link below: https://attendee.gotowebinar.com/register/8666469799183337485 In this Webinar, we will discuss the application of DL models using DLPy focusing on Computer Vision. DLPy is a high-level Python API designed to provide an efficient way to apply Deep Learning functionalities using friendly Keras-like APIs to solve Computer Vision, Natural Language Processing, Forecasting, and Speech Processing problems. We explain how DLPy can be applied to data preparation, data processing, model building, assessment and deployment. This will be a preview of our more in-depth presentation, specifically focused around Multi-Task Deep Learning For Image Tagging, during ODSC Europe in London this November. Talk will be delivered by: - Haidar Altaie, Data Scientist at SAS UK&I. He joined SAS in September 2018 after graduating with a Mathematics and Statistics degree, and is now passionate to integrate Advanced Analytics, Machine Learning, Forecasting and Computer Vision techniques across various industries to enable customer to solve complex real life problem. - Spiros Potamitis, Data Scientist at SAS. He is a leading software and services provider in advanced analytics. Having acquired an MSc in Information Management from the University of Manchester, Spiros is specialising in the application and implementation of analytics to drive business outcomes. Prior of joining SAS, Spiros has acquired a wealth of predictive modelling experience while working in advanced analytics positions in Credit Risk, Customer Insights and CRM.

  • An overview of machine learning interpretability

    CIC Cambridge

    Speaker: Mehrnoosh Sameki, Technical Program Manager at Microsoft https://www.linkedin.com/in/mehrnoosh-sameki-a2a02245/ Topic: An overview of machine learning interpretability Schedule: 6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments 6:30pm - 7:20pm - Talk 7:20pm - 7:30pm - Q&A 7:30pm - 8:00pm - Networking Bio: Mehrnoosh Sameki is a technical program manager at Microsoft responsible for leading the product efforts on machine learning interpretability and fairness within the Azure Machine Learning platform. Previously, she was a data scientist at Rue Gilt Groupe, incorporating data science and machine learning in retail space to drive revenue and enhance personalized shopping experiences of customers. She earned her Ph.D. degree in computer science at Boston University. Abstract: With the recent popularity of machine learning algorithms such as neural networks and ensemble methods, etc., machine learning models become more like a ‘black box’, harder to understand and interpret. To gain the user’s trust, there is a strong need to develop tools and methodologies to help the user to understand and explain how predictions are made. Data scientists also need to have the necessary insights to learn how the model can be improved. Much research has gone into model interpretability and recently several open sources tools, including LIME, SHAP, and GAMs, etc., have been published on GitHub. In this talk, we present popular state-of-the-art interpretability algorithms and introduce a Machine Learning Interpretability toolkit which incorporates the cutting-edge technologies in the domain of AI transparency. Using this toolkit, data scientists can explain machine learning models using state-of-art technologies in an easy-to-use and scalable fashion. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london

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  • SK-Burn: Tips for new data scientists

    CIC Cambridge

    Kaggle Days Meetup in Boston #1 Speaker: Raymond Grossman, Machine Learning Engineer at Kensho Technologies https://www.linkedin.com/in/raymond-grossman-bb4664114/ Topic: SK-Burn: Tips for new data scientists Schedule: 6:00pm - 6:30pm - ODSC and LogicAI Intro, Pizza & Refreshments 6:30pm - 7:20pm - Talk 7:20pm - 7:30pm - Q&A 7:30pm - 8:00pm - Networking Bio: Raymond Grossman has been an avid machine and deep learning practitioner since graduating from Princeton Mathematics in 2016. He specializes in natural language processing and speech at Kensho Technologies under S&P Global, where he works as an ML Engineer. He also is an accomplished Kaggler, recently achieving overall rank 14 out of over 100,000 competitors globally under the moniker "To Train Them Is My Cause". His work on Kaggle includes winning Google's Toxic Comment Classification Challenge (1st/4551). Outside of work, Raymond enjoys playing the violin and bouldering. Abstract: Information about how different SOTA models or technologies work is readily available, but information on how to apply those models quickly and effectively is hard to come by. Covering everything from modeling tips and tricks to engineering pipelines and workflows, this talk attempts to bridge that information gap by using SK-learn to demonstrate the impact of engineering decisions on modeling pipelines. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london

  • Webinar: AI Infrastructure and Supporting the Rise of Data Science

    We want to invite you to participate in the FREE ODSC Webinar! Date: September 18th Time: 1 pm - 2 pm EST To access this webinar, please register using the link below: https://attendee.gotowebinar.com/register/3689670053232277517 The rise of data science is often attributed to the exponential growth of data, whether structured or unstructured. While likely true, it is also true that the supporting AI infrastructure has enabled not only the growth of the data but also has become critical to extracting the value from the data explosion. The industry leaders NVIDIA, WekaIO and Western Digital will each bring their perspective to the importance of AI infrastructure to data science. Whether you are a data scientist, IT professional, or C-level decision maker you will learn how thoughtful AI infrastructure can accelerate your time to insight, time to value and increase profit for your business. You will take away techniques to overcome common challenges and barriers to successful data science in development and in production. Come ready with your questions for the panel to help accelerate your data science. Talk will be delivered by: - Darrin P. Johnson, Global Director of Solution Architectures at NVIDIA. He and his team lead all DGX, OEM, and storage reference architecture initiatives. Darrin’s experience spans 25 years of leadership in O/S, high performance systems, networking, storage, I/O and most recently AI/Deep Learning technologies with companies such as Cray, SGI, Adaptec, Sun Microsystems, Oracle and now NVIDIA. He is a certified Deep Learning trainer for NVIDIA as well. - Matt Miller, Director of Product Marketing at WekaIO. Matt has spent nearly 20 years in the storage industry in both product management and product marketing roles, for companies such as HPE, Nimble Storage, NetApp, Sun Mircosystems and Veritas. - Greg Holick, Director of Technology Alliances at Western Digital. Greg Holick is a senior technologist with over 15 years of experience in the data storage industry. Throughout his tenure, Greg has engineered software solutions, architected complex storage environments, been the product manager on private cloud solutions, and guided customers and partners on some of the most challenging storage infrastructures in the industry. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/

  • Deep Learning for Clinical Natural Language Processing

    Speaker: Dr. Sadid Hasan, Senior Scientist and Technical Lead of the AI Group at Philips Research https://www.linkedin.com/in/sadidhasan/ Topic: Deep Learning for Clinical Natural Language Processing Schedule: 6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments 6:30pm - 7:20pm - Talk 7:20pm - 7:30pm - Q&A 7:30pm - 8:00pm - Networking Bio: Dr. Sadid Hasan is a Senior Scientist and Technical Lead of the Artificial Intelligence Group at Philips Research, Cambridge, MA. His recent research focuses on various Natural Language Processing (NLP) problems related to Clinical Information Extraction, Text Classification, Natural Language Inference, Clinical Text Summarization, and Paraphrase Generation using Deep Learning. Prior to joining Philips, Sadid was a Post Doctoral Fellow at the Department of Mathematics and Computer Science, University of Lethbridge, Canada, from where he obtained his PhD. in Computer Science with a focus in NLP and Machine Learning. Sadid has over 60 peer-reviewed publications in the top NLP/Machine Learning venues, where he also regularly serves as a program committee member/area chair including ACL, IJCAI, EMNLP, NeurIPS, ICML, etc. Abstract: The ever-increasing amount of Electronic Health Record (EHR) clinical free text documents has urged the need to build novel clinical natural language processing (NLP) solutions towards optimizing the patient outcomes across the care continuum. In this talk, I will discuss some of the recent deep learning-based clinical NLP algorithms developed in the Artificial Intelligence Lab at Philips Research such as radiology report classification, clinical paraphrase generation and text simplification, and disease named entity recognition, etc. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london

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  • Dumb & Dumber vs Ocean’s 11: Tackling evolving, sophisticated fraud with AI

    We want to invite you to participate in the FREE ODSC Webinar! Date: August 29th Time: 1 pm - 2 pm EST To access this webinar, please register using the link below: https://attendee.gotowebinar.com/register/3825553196643547149 Sophisticated fraud attacks that are extensively planned, hard to detect, and highly scalable are becoming the new normal for online platforms. Learn more about the spectrum of fraud attacks – from "dumb & dumber" to "ocean's 11"– and why Unsupervised Machine Learning is the key to detecting attacks before they inflict damage. The webinar will be delivered by Sathya Chandran, PhD, Security Research Scientist at DataVisor. Sathya is an expert in applying big data and unsupervised machine learning to fraud detection, specializing in the financial, e-commerce, social, and gaming industries. Sathya holds PhD in CS from the University of South Florida and has previously worked at HP Labs and Honeywell. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/

  • The power of combining BI Analytics and Machine Learning

    Speaker: Daniel Gray, Senior Director of Corporate Sales Engineering at AtScale https://www.linkedin.com/in/daniel-gray-835b996/ Topic: The power of combining BI Analytics and Machine Learning Schedule: 6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments 6:30pm - 7:20pm - Talk 7:20pm - 7:30pm - Q&A 7:30pm - 8:00pm - Networking Bio: Daniel Gray is AtScale's Senior Director of Corporate Sales Engineering. Daniel has spent the last two decades in the data warehouse, big data, and machine learning space and specializes in descriptive and prescriptive analytics. Prior to Atscale, Gray worked at HP’s Advanced Technology Center for 5 years, Vertica for 7 years, and managed the Pre-Sales team at Domino Data lab. He has worked with many databases including Vertica, Netezza, Oracle, Hadoop and Spark as well as a variety of infrastructure tools including Docker, Kubernetes and Openshift. Daniel also has expertise in Python, R, and SAS. Abstract: During this session, you will learn how BI analysis can influence machine learning, how machine learning can feed BI analysis, and the benefits of combining BI analysis and machine learning. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london

  • When Holt-Winters is better than Machine Learning for Time Series Data

    Machine Learning is all the rage, but when does it make sense to use it for forecasting? How do statistical forecasting methods compare? In this presentation, Developer Advocate Anais Dotis-Georgiou will show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series forecast. Anais Dotis-Georgiou is a Developer Advocate for InfluxData with a passion for making data beautiful with the use of Data Analytics, AI, and Machine Learning. She takes the data that she collects, does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she is not behind a screen, you can find her outside drawing, stretching, boarding, or chasing after a soccer ball. Date: August 15th Time: 1 pm - 2 pm EST To access this webinar, please register using the link below: https://attendee.gotowebinar.com/register/8826913832534593803 ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/

  • Understanding Machine Learning Results to Increase their Value & Avoid Pitfalls

    Speaker: Linda M. Zeger, Ph.D., Founder / Principal Consultant at Auroral LLC https://www.linkedin.com/in/linda-zeger-87983a9a/ Topic: Understanding Machine Learning Results to Increase their Value & Avoid Pitfalls Schedule: 6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments 6:30pm - 7:20pm - Talk 7:20pm - 7:30pm - Q&A 7:30pm - 8:00pm - Networking Bio: Dr. Linda M. Zeger leads the design and execution of innovative processes and solutions to derive maximum value from data. Through projects she has led in scheduling for data delivery protocols, communication and sensor networks, and healthcare analytics, she has developed techniques to substantially improve network efficiency and reliability, guide system operations, and derive key insights, by employing statistical modeling, machine learning, and data analytics. Dr. Zeger is the founder and principal consultant of Auroral LLC, and she has also held positions at MIT Lincoln Laboratory, Lucent Technologies, Educational Testing Service, and with universities. Dr. Zeger earned a Ph.D. in physics from Harvard University. She is is the author of numerous published papers, and is an inventor on a number of patents. Abstract: With the increasing use of artificial intelligence throughout many industries, much excitement has been generated over the potential benefits that can be obtained. At the same time, questions have arisen regarding potential risks of artificial intelligence and its usage of personal data. This session will describe issues that should be considered before and while employing such a system, in order to better understand, and thereby increase, its utility, as well as to avoid possible pitfalls. The quantity and quality of data used to train a machine learning system, as well as the quality and relevancy of the data on which the system is employed, has a great effect on the value of a machine learning system. A thorough understanding of the information content, type, and quality of the input data, as well as how it was selected, obtained, and cleansed is also essential to elucidate any potential inaccuracies, biases, or limitations in the results produced by machine learning. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london

  • Drinks with Data Scientists

    The Asgard

    Join our Drinks with Data Scientists! Enjoy this great opportunity to exchange information on challenges, experiences, and goals with fellow Data Scientists. That will be an amazing networking time together. Starting at 6 pm Place: The Asgard Irish Pub & Restaurant, 350 Massachusetts Ave. (Between Central Sq. & MIT), Cambridge, Massachusetts 02139 Invite your friends, or come by yourself and make new ones! • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • India Conference Aug 7 - 10: https://india.odsc.com/ • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london

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