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Overview of the event

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn from data, without being explicitly programmed.

The "deep" in "deep learning" refers to the number of layers through which the data is transformed. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to its original goals. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation.



Facts & Figures About The Event

7 Speakers
7 Topics
50 Tickets

Conference Schedule

Machine Learning and Deep Learning Day (12th September, 2019)
X Topic Abstract

Assisted or Supervised models of machine learning are a great boon to automate many mundane tasks in Enterprise world. Supervised model combined with hypothesis models helps in creating “situations” and in predicting possible outcomes to a given situation, thereby making “situations” intelligent.
But the question in user’s mind is how can I believe/trust what’s proposed is correct?
It’s here explainability becomes critical in ML. Inherently Decision Trees, Random Forest and features like boosting does help in showcasing significant factors and does trace the path of decision. LIME (Local Interpretable Model-Agnostic Explanations) does play a good role here. But for a user that’s just 50%. The remaining 50% is why the other “possibly close match” was not selected. It’s here the concept of anti-model is used.
The anti-model addresses the question of why the other “possibly close match” was not selected say in a classification scenario by building anti-models to the original models and its inference models to “explain” the decision taken. Multiple anti-models are constructed and validated with original model.
We will look at this ML approach, its benefits, applicability and potential success SAP has obtained in this space.

X Topic Abstract

EY XBRL service professionals provide focused advice, project management services and qualified XBRL resources to assist entities with compliance. The work involves analysis of annual reports from the companies that include financial statements, disclosures from Directors and reports of the Auditors. We are developing AI solutions to help with these processes. The large volume of tagged reports can be used to train deep learning models for NLP. In this talk, I will present an overview of the solutions that we built to extract information from these documents.


X Topic Abstract

I will cover the following:

1. Walk thru of key challenges in Broadcast and media organization and how ML help to address the challenges.
2. Computer vision use cases in hospitality industry
3. Case study of a international cruise companies leveraging computer vision and short demos


X Topic Abstract

Social Media has become a huge aggregator of consumer perception of brands. Whether it is the sharing of product experiences, resolving product-related queries or joining engaging discussions, the information posted by consumers on social platforms is invaluable to brands. However, extracting this information, which is largely in the form of text and images is cumbersome to say the least. A robust pipeline to process the large volumes of text is necessary to generate meaningful insights. The objective of this presentation is to walk through the NLP techniques used to build the pipeline, and how the same has been used to tackle use-cases like content-gap analysis, product recommendations, competitor survey etc

X Topic Abstract

For any AI transformation journey, the first question which needs to be asked is “Is your organisation transforming into a butterfly or is it training to be the faster caterpillar”. Many organisations follows the faster caterpillar route, thinking of short term wins. Any business that deploys AI needs a large volume of relevant, well-organized data.
In any AI transformation journey, data literacy is the core business skill. For this talk, I plan to walk thru the journey we had in kick-starting a AI transformation journey for a world’s leading consultancy company. During this talk, I will be sharing the best practices around strategic data acquisition, unified data warehouses, data labelling strategy, what to expect out of a data cleanup strategy. As we understand the overall data literacy, I will also provide a path towards fuelling AI transformation journey using the data literacy, explaining thru analysis, predictions and automations.

X Topic Abstract

Central topic for the talk is Analytics driven spoken & written interactions
High level flow of the discussion will be centered around Business interaction & technology related to the following: Text & speech related analytics; Text to speech and vice versa; Closed loop integration with business apps & customer interactions
Will be focusing for each of the topic the Business processes as well as BUs which will be impacted by the above mentioned capabilities: Notable being Customer service centers; IT organizations; 3rd party & vendor interactions; Billing & Payment gateways.

X Topic Abstract

When building a Neural Network , all one gets to hear about is what kind of Deep Learning architect to use. But for building a production level model at scale, the challenges are much more than that. There are several other crossroads to go through such as: what compute capacity do you need? How will you host your model? How will you tune your hyperparameters? What's the best way to process your unstructured data? And many more.

This talks will shed light one some of these unspoken engineering challenges while building a production level Deep Learning model.

Speaker profile

Paulami Das is a seasoned Analytics and business professional with 14 years’ experience across industries. She is passionate about helping businesses tackle complex problems at scale through Machine Learning. She is currently leading the Data Science CoE at Brillio Technologies. Prior to Brillio, she has held Analytics leadership roles in companies such as JP Morgan Chase, SAS, and Cytel. She is also an alumnus of IIMA and IITK.

X Topic Abstract

Digital Transformation of Mymoneymantra
Journey of Tele-calling to Smart workstation thorugh Automation, AI and ML

X Topic Abstract

Adoption of AI has moved at different pace in different industries. Financial services has been a leader but other industries are catching up. This talk covers convergence of use cases across FS and Healthcare and what each industry can learn from its peer.

1) Onboarding - Know your customer and know your provider solutions digitize the onboarding processes while reducing risk for banks and payer organizations.
2) Conversational platforms - NLP based virtual agents are servicing robo advisory, be it explanation of financial products or of health plan benefits.
3) Anomaly Detection - AI is making anti money laundering processes more intelligent while helping payer organization prevent claims fraud.
4) Reconciliation - Reconciliation industry has spawned due to disjointed frontoffice and backoffice business processes. AI enabled automation is reducing cost of operations for the industries that generate large volumes of data.
5) Predictive analytics - Ability to predict next best action improves user experience, be it a customer or a member

Our Sponsors

Use this opportunity to improve the visibility of your organization

Instant Sponsorship
Avail instant sponsorship at just USD 2000

Instant sponsorship includes
• Branding of your company as Bronze Sponsor – Company's Logo on the event page with cross link to your website.
• One Speaking Slot (45 min -non sales talk).
• 10% discount on registration fee for any delegate from your organization.
• Full day attendance at the event with lunch
• 1 x Roll up stand / Brochure distribution at the event
• Online Interview post of your company's senior executive at our media portal

For Silver, Gold Platinum & Titanium Sponsorship opportunites, please request for Sponsorship Brochure via email at contact@1point21gws.com, naveen@1point21gws.info

Our Speakers


Srivatsan Santhanam

Senior Director

SAP


Mohit Sharma

Vice President – Digital Transformation

My Money Mantra


Yukti Vohra

Principal Data Scientist

Epsilon


Paulami Das

Principal Data Scientist

Brillio


Sathyan SethuMadhvan

AI Transformation Consultant/Leader

ThoughtWorks


Rajesh Sattanathan

Head AI & Analytics

Cognizant


Vishnu Makkapati

Associate Director

EY


Senthil Ramachandran

Senior Director

Capgemini


Kapil Mohan

Director

Optum


Somenath Chandra

ML Engineer

Capgemini

Want to become a speaker Please register here Register

Our Pricing

Group of three or more(Early Bird)

Rs. 7500 + GST

Till July 27
Group of three or more(Standard)

Rs. 8000 + GST

Till September 12
Individual(Early Bird)

Rs. 9000 + GST

Till August 12
Individual (Standard)

Rs. 10000 + GST

Till September 12


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Our Testimonial

FAQs

Who can attend Machine Learning and Deep Learning Day in Bangalore?

• Data Engineers/Developers / Scientists
• Analytics Professionals
• Startup Professionals
• Scientists/Researchers
• Professors
• President/Vice president
• Chairs/Directors

And last but not the least……….
Anyone interested in Machine Learning & thrives to make the future developed and better

Why to attend Machine Learning and Deep Learning Day in Bangalore?

Understand the state of development of Machine learning by exchanges, clearing houses, central counter parties and payment systems, and what it means for you.

What will you learn about?

Detecting where underlying problems and frictions exist in your organisation that will be alleviated by Machine learning technologies. Using Machine learning as a tool for innovation across your organisation

Are there any prerequisites to attend this program?

No

Do I need to register for the event?

Yes, all conference attendees must register in advance to attend the event.