ODSC Webinar Calendar

ODSC’s free webinar series serves to educate our community on the languages, tools, and topics of AI and Data Science

AI Infrastructure and Supporting the Rise of Data Science

September 18th, 2019
1 pm – 2 pm EST
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09/18/2019 10:00 AM
America/Los_Angeles
AI Infrastructure and Supporting the Rise of Data Science

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ODSC Webinar

Darrin P. Johnson
Global Director of Solution Architectures at NVIDIA

Matt Miller
Director of Product Marketing at WekaIO

Greg Holick
Director of Technology Alliances at Western Digital

AI Infrastructure and Supporting the Rise of Data Science

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.

Darrin P. Johnson

Darrin is the Global Director of Solution Architecture for Enterprise 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

Matt Miller is the Director of Product Marketing for WekaIO, responsible for marketing strategy and positioning. 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

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 West 2019 Warm-Up: Machine Learning

September 25th, 2019
1 pm – 2 pm EST
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09/25/2019 10:00 AM
America/Los_Angeles
ODSC West 2019 Warm-Up: Machine Learning

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ODSC Webinar

Philip Tester
Director of Business Development at CloudFactory

Introducing the Data Production Line for Machine Learning

Data operations are the new AI factory model. Data is your raw material, and you have to move it through multiple processing and review steps before it’s ready for machine learning (ML). Developing high-performing ML models requires a strategic combination of people, tools, and process working together to structure massive data with high quality. In this segment, CloudFactory will share best practices for your data production line.

Philip Tester

Philip Tester is Director of Business Development at CloudFactory, a global workforce provider for AI data labeling. Philip leads the company’s partnerships and integrations strategy, and his team helps AI innovators find solutions for tough data-production problems.

Crissman Loomis
AI Engineer at Preferred Networks

Machine Learning in Chainer Python

Chainer is a neural network framework written almost entirely in Python. Chainer was the first framework to provide the “define-by-run” neural network definition, which allows for dynamic changes in the network. Define-by-run simplifies the debugging process since Chainer provides an imperative API in Python. This means error messages provide normal error documentation with Python. Since Chainer was created from the start in Python, the code is inspectable with Python tools, and can be customized if required. Newer neural network models can require new algorithms, and having a framework written in Python means that the code can be altered as required. Chainer supports computation either on CPUs or GPUs. GPU computation is enabled in Chainer by CuPy, a numerical computation library which supports Numpy-compatible operations between arrays. This enables easy switching between CPU or GPU, as required by the coding environment. 

Crissman Loomis

Since his mathematics degree, Crissman has devoted himself to the study of languages, including Spanish, Javascript, German, Python, and Japanese. Previously, Crissman worked on open source projects for automation of game playing systems, including MMORPGs, web-based games, and Pokemon. After finding the limits of rule-based systems, he worked on Deep Learning programs at Preferred Networks, the company that created the AI Python framework Chainer.


Human Machine Learning

October 2, 2019
1 pm – 2 pm EST
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10/02/2019 10:00 AM
America/Los_Angeles
Human Machine Learning

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ODSC Webinar

Matt Cowell
CEO at QuantHub

Nathan Black
Chief Data Scientist at QuantHub

Human Machine Learning

Could we, as humans, improve the way we learn by applying techniques machines use to learn? From early in the field of AI, researchers have been looking to cognitive psychology for inspiration on how to teach machines to learn. The effectiveness of this approach is evidenced by recent advances in, and growing prevalence of, deep learning. However, we’ve reached a point where machine learning methodologies are deviating from the way humans learn and gaining impressive efficiencies as a result. For humans, this issue is amplified by current findings in cognitive psychology which strongly suggests that many of our long-standing learning methods have been largely misguided. In this webinar, we explore the challenges with common studying practices, contrasting those with methods that machines successfully use to learn, and drawing parallels to recent cognitive psychology research.

Matt Cowell

Matt serves as the CEO at QuantHub, spearheading the drive to help companies overcome the extreme analytics talent shortage and build exceptional data science and engineering teams. Matt has a passion for developing authentic relationships with customers to truly understand what drives them, and then crafting creative solutions to their most critical problems. Prior to joining QuantHub, Matt spent the last 15 years running product and tech at PE-backed companies, including building a product and engineering organization at Daxko to deliver 10x revenue growth, 7 acquisitions, and 3 enormously successful recapitalizations in just 10 years. While at Daxko, Matt led the team to deliver the first machine learning/AI solution to the market, predicting customer membership churn and also propensity to donate.

Nathan Black

Coming Soon!


Previous Webinars


Check out our previous AI talks at learnai.odsc.com below


Telling Human Stories With Data

Free recording will be available here


Alan Rutter
Founder of Fire Plus Algebra

Telling Human Stories With Data

Robust data analysis underpins every business decision, public sector project and non-profit initiative. But data in its raw form often fails to convince crucial lay audiences – either due to its complexity, or due to suspicion and mistrust. And you can’t help guide the world in the right direction if you alienate key decision-makers or the public.

This talk, delivered by journalist and data visualization specialist Alan Rutter, will cover an audience-centered approach to visualizing data. It will introduce tried-and-tested techniques for communicating data-driven stories effectively to people from a broad range of backgrounds, and deal with some of the common problems that practitioners encounter.

Alan Rutter

Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Time Out,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.


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

Free recording will be available here


Sathya Chandran, PhD
Security Research Scientist at DataVisor

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

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.

Sathya Chandran, PhD

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.


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

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Anais Dotis-Georgiou
Developer Advocate at InfluxData

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

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.


ODSC India 2019 Warm-Up: Deep Real Learnathon

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Aditya Bhattacharya
Data and Cloud Platform Engineer, West Pharmaceuticals

Joy Mustafi
Director and Principal Researcher, Salesforce

Person Identification via Multi-Modal Interface with Combination of Speech and Image Data

Having multiple modalities in a system gives more affordance to users and can contribute to a more robust system. Having more also allows for greater accessibility for users who work more effectively with certain modalities. Multiple modalities can be used as backup when certain forms of communication are not possible. This is especially true in the case of redundant modalities in which two or more modalities are used to communicate the same information. Certain combinations of modalities can add to the expression of a computer-human or human-computer interaction because the modalities each may be more effective at expressing one form or aspect of information than others. For example, MUST researchers are working on a personalized humanoid built and equipped with various types of input devices and sensors to allow them to receive information from humans, which are interchangeable and a standardized method of communication with the computer, affording practical adjustments to the user, providing a richer interaction depending on the context, and implementing robust system with features like; keyboard; pointing device; touchscreen; computer vision; speech recognition; motion, orientation etc.

Aditya Bhattacharya

Aditya is currently working as Data and Cloud Platform Engineer in West Pharmaceuticals in Bangalore, India. Being an ex-Microsoft employee, Aditya is an enthusiastic learner, who loves to explore new technologies and tries to grasp the in-depth knowledge of the concepts used in them. Aditya has roughly 4 years of experience in domains like Internet of Things (IoT), Machine Learning, Robotics and Cloud Computing. Currently, he is working on a research project to compare performance of Deep Learning Algorithms Variational Auto Encoder (VAE) and Deep Convolutional Generative Adversarial Networks (DCGAN) for generating pencil sketch images.

Joy Mustafi

Joy Mustafi is Director and Principal Researcher at Salesforce, primarily responsible for leading the Salesforce Einstein and other intelligent cloud platform in India. Joy has overall seventeen years of experience in corporate, research and academic world. Had worked as Principal Applied Scientist at Microsoft – Artificial Intelligence and Research, Data Science and Machine Learning. Was with IBM for a decade as Data Scientist, and involved in the Business Analytics and Optimization, Watson Solutions, IT Operations Analytics etc. Got the Research Fellowship Award in Computer and Communication Sciences from Indian Statistical Institute. Collaborated with the ecosystem by visiting around twenty-five leading universities in India as visiting faculty, guest speaker, advisor, mentor, project supervisor, panelist, academic board member, curricula moderator, paper setter and evaluator, judge of events like hackathon etc. Supporting around fifteen start-ups and non-profit forums being in the board or as consultant for data sciences.

Vivek Singhal
Co-Founder & Chief Data Scientist at CellStrat

Shreyas Jagannath
AI Researcher at CellStrat

Training Autonomous Driving Systems to Visualize the Road ahead for Decision Control

We will train the audience on how to develop advanced image segmentation with FCN/DeepLab algorithms which can help visualize the driving scenarios accurately, so as to allow the autonomous driving system to take appropriate action considering the obstacle views.

Learning Outcomes:

  • Understand advanced image segmentation algorithms such as FCN and DeepLab.
  • Learn how the pre-trained image segmentation models related to FCN and DeepLab can be utilized by an autonomous driving system to take decisions on the road.
  • How to use pre-trained models of FCN/DeepLab for inference on new images.

Vivek Singhal

A leading Data Scientist and researcher with expertise in AI and Machine Learning. Also, a Startup Advisor and Industry Consultant having spent many years in USA and India in the high-tech industry. Co-Founder at CellStrat, India’s leading Artificial Intelligence startup. Serial Entrepreneurial experience having Co-Founded or advised several startups in prior years including Healthiply.in (AI-driven online health startup), LocVille.com (online furniture and decor) and SalesGlobe (sales CRM). Long experience in telecom and digital industries in Strategy, Mobile Apps/Web, Data Analytics, Systems Integration and Enterprise Mobility, in leading MNCs like IBM, AT&T, Schlumberger, HCL-HP etc.

Shreyas Jagannath

An AI Researcher doing research in experimental AI and theoretical AI and also an active entrepreneur with a mission of AI for social good. Also have an affinity towards AR/VR, Driverless cars, Cognitive sciences.


Kubeflow, MLFlow and beyond - augmenting ML delivery

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Stepan Pushkarev
CTO in Provectus

Kubeflow, MLFlow and beyond - augmenting ML delivery

Reproducible ML pipelines in research and production with monitoring insights from live inference clusters could enable and accelerate the delivery of AI solutions for enterprises. There is a growing ecosystem of tools that augment researchers and machine learning engineers in their day to day operations. Still, there are big gaps in the machine learning workflow when it comes to training dataset versioning, training performance and metadata tracking, integration testing, inferencing quality monitoring, bias detection, concept drift detection and other aspects that prevent the adoption of AI in organizations of all sizes.

In this webinar, we’ll design a reference machine learning workflow. We’ll review open source tools that contribute to this workflow and are applicable to build reproducible automation of it.

Takeaways:

– A deeper view on traps and pitfalls on each stage of ML lifecycle.

– Reference implementation and automation of ML Workflow.

Prerequisite knowledge:

– Understanding of core Data Science methods, frameworks and libraries.

– An image of what Docker and Kubernetes are.

Presenter bio

Stepan Pushkarev is a CTO of Provectus. His background is in the engineering of data platforms. He spent the last couple of years building continuous delivery and monitoring tools for machine learning applications as well as designing streaming data platforms. He works closely with data scientists to make them productive and successful in their daily operations.


ODSC India 2019 Warm-Up

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Kavita D. Chiplunkar
Data Science Head, Infinite-Sum Modelling Inc.

Nirav Shah
Founder of OnPoint Insights

Building a Scorecard using Python

This webinar will tell you the importance of Credit Scorecards in Banking /Financial Institutions , how they are used to measure the credit worthiness of a customer and how Machine Learning Algorithms are helping built better scorecards than traditional algorithms.We plan to briefly discuss the key data elements that would be required to build such scorecards.We will talk at high level about various steps in building a scorecard .We will also share a brief snapshot of what to expect out of our session at ODSC and how this session can benefit Data Science Enthusiasts and Banking professionals.

Kavita D. Chiplunkar

Kavita is an Analytics leader with 12 + years of core hands on experience having an excellent track record on Presales, Partner Management, Analytics Delivery and Team management across domains in World Class Organizations. Currently, she is heading the Data Science function at Infinite Sum Modeling. She is a Chemical Engineer by education followed by a Masters (Eco) from IGIDR. She is a seasoned analytics professional with work experiences across companies like Fair Isaac, Experian, Accenture, Infosys and Vodafone. Her vast experience in domains like Banking, Insurance, Telecom, Fraud and Risk Management give her the right kind of diversification. She has published papers in areas of Financial Econometrics and Social Media Analytics. She has been an esteemed speaker at various national seminars on Analytics.

Nirav Shah

Nirav Shah is the Founder of OnPoint Insights, a data analytics, software services and staff augmentation consultancy based in Boston. He has 15 years of industry experience – mainly in consulting on data analytics, big data modeling, control systems, process analytics and software tools, off-line and real-time data solutions, and training customers in data analytics,dashboards and data visualization. He is an expert in Dashboards and Visualization using Tableau and other Multivariate Data Analytics software.

Amit Doshi
Senior Application Engineer at MathWorks

Integrating Digital Twin and AI for Smarter Engineering Decisions

With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science. However, there are many complexities associated with modeling plant assets, training predictive models for them, and deploying these models at scale for near real-time decision support. This talk will discuss these complexities in the context of building an example system.

Highlights:

  1. Concept of Digital twin, real world applications using DT and overview of ways to build one.
  2. Building blocks of developing predictive algorithms, techniques for identifying key condition indicators as well RUL methods.
  3. How Digital Twin fits into AI workflow and helps improving robustness of AI model.

Amit Doshi

Amit Doshi works as a Senior Application Engineer at MathWorks in the area of technical computing. He is responsible for driving and managing the technology evaluation stage of the sales process. He focuses primarily on data science and predictive analytics.  Amit has over 12 years of experience working across the industry. Over the years, he has worked on data analytics, experimental test setup development, workflow automation, and system simulations. He previously worked at Suzlon Energy Limited in Pune and Germany, Texas Instruments in Germany, and IIT Bombay. Amit holds a bachelor’s degree in mechanical engineering and a master’s degree in mechatronics.

Ramanathan Ramakrishnamoorthy
Director & Co-Founder of Zentropy Technologies

Gurram Poorna Prudhvi
Machine Learning Engineer at mroads

Time Series analysis in Python

Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics helps us to get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.

Ramanathan Ramakrishnamoorthy

Co-Founder, Director & Head of Research & Development at Zentropy Technologies. Before finding Zentropy, Ram worked with a leading hedge fund as a Project Manager responsible for building tools and technologies required by the middle and the back office. He was instrumental in delivering some of the most mission-critical strategic projects that helped in the overall business of the firm.

Gurram Poorna Prudhvi

Prudhvi is working as a machine learning engineer at mroads. He is interested in NLP research, Opensource, Public Speaking, and Python. In his free time he explores and tries to understand different dimensions of life. He is also a core team member of Hyderabad Python Community.


ODSC India 2019 Warm-Up: Machine Learning & Deep Learning

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Dr. C.S.Jyothirmayee
Sr. Scientist at Novozymes South Asia Pvt Ltd

Usha Rengaraju
Principal Data Scientist at Mysuru Consulting Group

Vijayalakshmi Mahadevan
Faculty Scientist at Institute of Bioinformatics and Applied Biotechnology (IBAB)

Deep learning powered Genomic Research

The event disease happens when there is a slip in the finely orchestrated dance between physiology, environment and genes. Treatment with chemicals (natural, synthetic or combination) solved some diseases but others persisted and got propagated along the generations. Molecular basis of disease became prime center of studies to understand and to analyze root cause. Cancer also showed a way that origin of disease, detection, prognosis and treatment along with cure was not so uncomplicated process. Treatment of diseases had to be done case by case basis (no one size fits).
With the advent of next generation sequencing, high through put analysis, enhanced computing power and new aspirations with neural network to address this conundrum of complicated genetic elements (structure and function of various genes in our systems). This requires the genomic material extraction, their sequencing (automated system) and analysis to map the strings of As, Ts, Gs, and Cs which yields genomic dataset. These datasets are too large for traditional and applied statistical techniques. Consequently, the important signals are often incredibly small along with blaring technical noise. This further requires far more sophisticated analysis techniques. Artificial intelligence and deep learning gives us the power to draw clinically useful information from the genetic datasets obtained by sequencing.

Dr. C.S.Jyothirmayee

As Senior Technology Innovation Specialist,work on exploring innovative technologies in the field of biology. Before Novozymes, worked on comparative genomics of H. Pylori, mutational analysis of cataract protein and developing human model for cancer studies at prestigious national laboratories at CDFD, CCMB (Hyderabad) and NCCS, Pune respectively.

Additionally, I am a registered patent agent. Combining my domain knowledge in Biological science and application oriented patent analytics (PatInformatics) and  work one three areas:
a. Using Patent & Literature data for deriving technology evolution insights for future project planning
b. Pitching new ideas and exploring their feasibility
c. Networking with new ventures and exploring new areas for organization opportunities.

Usha Rengaraju

I am a polymath and unicorn data scientist with strong foundations in Economics, Finance, Business Foundations, Business Analytics and Psychology. I specialize in Probabilistic Graphical Models, Machine Learning and Deep Learning. I have completed Financial Engineering and Risk Management program from Columbia University with top honors, micromasters in Marketing Analytics from UC Berkeley and statistical analysis in Life Sciences specialization from Harvard. I am chapter lead/Co-Organizer of Women in Machine Learning and Data Science Bengaluru Chapter and Core oganizing team member at WIDS Bengaluru .I have around 6 years of technical experience working in various companies like Infosys, Temenos, NeoEYED and Mysuru Consulting Group. I am part of dedicated group of experts and enthusiasts who explore Coursera courses before they open to the public, an ambassador at AIMed (an initiative which brings together physicians and AI experts), part time Data science instructor, mentor at GLAD (gladmentorship.com), mentor at JobsForHer and volunteer at Statistics without Borders. I developed the course curriculum for Probabilistic Graphical Models @ Upgrad which is taught by Professor Srinivasa Raghavan from IIIT Bangalore.

Vijayalakshmi Mahadevan

With a background in Physics and Electronics from the Bharathidasan University,Trichy, Dr.Vijayalakshmi Mahadevan completed her Ph.D. from the National Centre for Biological Sciences- Tata Institute of Fundamental Research( NCBS-TIFR), Bangalore. She was an Assistant Professor in the School of Electrical and Electronics Engineering at SASTRA Deemed University in Thanjavur and a TCS Chair Professor of Bioinformatics and Associate Dean of the School of Chemical & Biotechnology.She was the Group Lead of the Chromatin and Epigenetics group also headed the Department of Bioinformatics from 2008 to 2016 besides being affiliated to the Centre for Nanotechnology and Advanced Biomaterials (CeNTAB) at SASTRA.

Dr.Vijayalakshmi was also a Research Mentor in the National Network for Mathematical and Computational Biology (NNMCB), India from 2013 and was a Research Mentor – Research Science Initiative (RSI) of the IIT Madras, Chennai Mathematical Institute, SASTRA University, Thanjavur, PSBB Group of Schools, Chennai and Centre for Excellence in Education, McLean,USA to promote scientific research among school children.

Dipanjan Sarkar
Principal Data Scientist at Red Hat

Anuj Gupta
S
cientist at Intuit

A Hands-on Introduction to Natural Language Processing

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying NLP to solve real- world problems. We leverage Python 3 and the latest and best state-of- the-art frameworks including NLTK, Gensim, SpaCy, Scikit-Learn, TextBlob, Keras and TensorFlow to showcase our examples. You will be able to learn a fair bit of machine learning as well as deep learning in the context of NLP during this bootcamp.

The intent of this workshop is to make you a hero in NLP so that you can start applying NLP to solve real-world problems. We start from zero and follow a comprehensive and structured approach to make you learn all the essentials in NLP. We will be covering the following aspects during the course of this workshop with hands-on examples and projects!

Dipanjan Sarkar

Dipanjan (DJ) Sarkar is a Data Scientist at Red Hat, a published author, and a consultant and trainer. He has consulted and worked with several startups as well as Fortune 500 companies like Intel. He primarily works on leveraging data science, advanced analytics, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He is also an avid supporter of self-learning and massive open online courses. He has recently ventured into the world of open-source products to improve the productivity of developers across the world.
Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, statistical methods and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. He also acts as a key contributor and Editor for Towards Data Science, a leading online journal focusing on Artificial Intelligence and Data Science. Dipanjan has also authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing, and Deep Learning.
Dipanjan’s interests include learning about new technology, financial markets, disruptive start-ups, data science, artificial intelligence and deep learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://medium.com/@dipanzan.sarkar and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.

Anuj Gupta

I am part of Intuit AI team. Prior to this, I was heading ML efforts for Huawei Technologies, Freshworks, Chennai and Airwoot, Delhi. I did my masters in theoretical computer science from IIIT Hyderabad and I dropped out of my Phd from IIT Delhi to work with startups. 

I am a regular speaker at ML conferences like Pydata, Nvidia forums, Fifth Elephant, Anthill. I have also conducted a bunch of workshop attended by machine learning practitioners. I am also the co-organizer for one of the early Deep Learning meetups in Bangalore.  I am also Editor of “Anthill-2018” – deep learning focused conference by HasGeek.


Model-based Reinforcement Learning for Atari

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Błażej Osiński
Senior Data Scientist at deepsense.ai

Model-based Reinforcement Learning for Atari

Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction – substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes.

In this webinar we will explore:

  • How video prediction models can be used to improve the sample efficiency of reinforcement learning?

  • How to create a model capable of predicting future in Atari games?

  • How to train the RL agent within “dreams” of another neural network?

Presenter bio

Błażej Osiński is a researcher at deepsense.ai working on reinforcement learning. His professional experience includes working at Google, Google Brain, Microsoft and Facebook. He was also the first software engineer at Berlin-based startup Segment of 1. Błażej holds a Masters Degree in Computer Science and Bachelors in Mathematics, both from the University of Warsaw.


OmniSci and RAPIDS: An End-to-End Open-Source Data Science Workflow

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Randy Zwitch
Senior Developer Advocate at OmniSci

OmniSci and RAPIDS: An End-to-End Open-Source Data Science Workflow

In this session, attendees will learn how the OmniSci GPU-accelerated SQL engine fits into the overall RAPIDS partner ecosystem for open-source GPU analytics. Using open bike-share data, users will learn how to ingest streaming data from Apache Kafka into OmniSci, perform descriptive statistics and feature engineering using both SQL and cuDF with Python and return the results as a GPU DataFrame. By the end of the session, attendees should feel comfortable that an entire data science workflow can be accomplished using tools from the RAPIDS eco-system, all without the data ever leaving the GPU.

Topics to be highlighted:
– What is RAPIDS? (discussion of NVIDIA open-source RAPIDS project, how it relates to Apache Arrow, etc.)
– What is OmniSci and how does it fit into the RAPIDS eco-system
– Example:
– Ingesting a data stream from Apache Kafka into OmniSci
– Using pymapd (Python) to query data from OmniSci and do basic visualizations
– Use cudf to do data cleaning and feature engineering
– Show how cudf dataframes can be passed to machine learning libraries like Tensorflow, PyTorch or xgboost.

Presenter bio

Randy Zwitch is a Senior Developer Advocate at OmniSci, enabling customers and community users alike to utilize OmniSci to its fullest potential. With broad industry experience in Energy, Digital Analytics, Banking, Telecommunications and Media, Randy brings a wealth of knowledge across verticals as well as an in-depth knowledge of open-source tools for analytics.


Going spatial: statistical learning for spatial data

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Giulia Carella
Data Scientist, CARTO

Going spatial: statistical learning for spatial data

During this webinar, Giulia Carella, Data Scientist at CARTO, will walk you through the best practices to make statistically sound decisions in the field of spatial data science. Giulia will cover the basic of the theory underlying the analysis of spatial data and she will present some of the most common methods and the associated software tools used in this domain, with a focus on the upsurge of big spatial data, for example from GPS sources.

Presenter bio

Giulia is a Data Scientist at CARTO. She hold a PhD in Statistical climatology from the University of Southampton (UK) and previously she worked as a researcher at the Le Laboratoire des Sciences du Climat et de l’Environnement (France).

Free access to ODSC talks and content is available at our

AI Learning Accelerator

ODSC EAST | Boston

– April 30th – May 3rd, 2019 –

The World’s Largest Applied Data Science Conference

ODSC EUROPE | London

– Nov 19th – 22nd, 2019 –

Europe’s Fastest Growing Data Science Community

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– Oct 29th – Nov 1st, 2019 –

The World’s Largest Applied Data Science Conference

Accelerate AI

Business Conference

The Accelerate AI conference series is where executives and business professionals meet the best and brightest innovators in AI and Data Science. The conference brings together top industry executives and CxOs that will help you understand how AI and data science can transform your business.

Accelerate AI East | Boston

– April 30th – May 1st, 2019 –

The ODSC summit on accelerating your business growth with AI

Accelerate AI Europe | London 

– Nov 19th – 20th, 2019 –

The ODSC summit on accelerating your business growth with AI

Accelerate AI West | San Francisco 

– Oct 29th – 30th, 2019 –

The ODSC summit on accelerating your business growth with AI