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
Machine Learning and Deep Learning Day (07th February, 2019)
Most organizations are able to adopt RPA relatively easily. However, moving up the value chain to machine learning, cognitive automation, NLP and other advanced technologies will require a systemic strategy. My white paper reveals 7 ways to easily embrace Machine Learning after implementing RPA.
This talk will demonstrate how computer vision techniques were used to check quality of work done by technicians during an install or repair job, which subsequently resulted in preventing repeat dispatches, in-turn saving millions of dollars for AT&T.
Organizations are transitioning to high performance computing (HPC) infrastructure for their data science practice. However, infrastructure transition is only half the puzzle. The other half of the puzzle is to transition their teams. The transition has be carefully managed by understanding why HPC is different and establish new hiring practices, ongoing training, and organization support so that the organization is productive on HPC environment.
AI and Machine Learning are being the prime focus area for most of the organizations to improve their business outcome. Many Data Science programs has been initiated but failed during implementation - only 20% of the programs are being successful. As data science needs different business strategy and change in current IT culture, in this session we will be sharing the key strategies, approach and experience to enable successful implementation of d
ata science programs with real industry case studies and examples.
Data Driven Science is reshaping the world and is opening new opportunities across industries. In recent years, Life Science companies started embracing digital information and applying data science to optimize the time from “Lab to Life”. Big Data and Machine Learning (ML) technologies provide an opportunity for Life Science firms to unlock answers to a host of high-value questions such as the true effectiveness of treatments by integrated data driven analysis of clinical trial and omics Data. This talk introduces omics data, NoSQL database, concepts of machine learning and provides insight on ML algorithms classified as Supervised, Unsupervised, Artificial Neural Network and Deep learning. Further we explore some Machine Learning Implementations in Lifesciences Industry. Finally, will show how to apply machine learning on integrated data for identifying the hidden patterns and better treatment decisions using Python libraries.
Broad scale use of Artificial Intelligence (AI) is in the early stages—few companies are ready to harness its power in a way that truly replicates human reasoning. As companies endeavor to use AI to deliver best-in-class services, it is important to consider the impact these technologies have on individuals and society. As we strive to deliver customer-centered experiences we need to understand how the data we collect can affect people and what unintended consequences stem from broad-scale AI adoption. How can companies deliver curated experiences and protect consumers against potential threats? (publication coming by end of 2019)
Over the last two decades, marketing to the consumer has already been redefined. Digital
Marketing and avenues to know your consumer and develop the messaging that catches their
attention is currently being redefined all over again... using Artificial Intelligence and Machine
Learning. Business-to-Consumer (B2C) businesses will soon learn that building a competitive
advantage will start with Data... analyzing huge amounts of data of consumers searching,
buying and using their products, but also about their daily habits, wants and preferences.
Many of these insights will help businesses not only rethink their marketing approaches and
messages, but even redesign their products or services to create a stronger value proposition
and a lasting influence on consumer choices. However, as businesses implement AI/ ML, they
will also need to deal proactively with the impact of their decisions on the society and their
businesses, especially the people it affects.
Approximately 70% of commercial freight is moved around the US in trucks - "If you bought it, a truck brought it". As a fundamental backbone of the American way of life, the trucking industry faces several challenges from safety issues to an acute shortage of skilled drivers. Learn how data mining and machine learning techniques are leveraged to provide several end-to-end solutions to mitigate risks and make better business decisions.
With so many profound developments in technology, such as Cloud Computing, IoT …etc we are seeing a complete change in how we collect, process, and make decision based on data. We are not tied to transactional, or digital data any more. Voice, video, and images can be used to make business decisions, everyday physical things can be connected onto the network… With the growth of the technology comes an imperative, we need to embrace the complexity and define strategy around how to collect, process, analyze, and visualize data as well as how to use it to teach computers and machines make decision without human interruption. During my presentation I am going to talk about best practices to create a data driven culture. I will discuss how to move from Data to Analytics to Machine Learning to AI, and skill sets required for each.
Ebru Evliyaoglu Akyuz has 18+ years experience in Machine Learning and Data Science. She joined Microsoft in February 2018 as an Artificial Intelligence Solution Architect. She helps companies drive innovation and achieve more in the area of Artificial Intelligence and Machine Learning. She empowers and educates senior customer executives, developers, and architects on AI technologies and solutions. Her areas of expertise are Cognitive Services, Azure Machine Learning, Deep Learning.
Previously, she was Director of Digital Analytics at CNN. She was responsible for establishing data strategy and enabling advanced analytics for all CNN Digital properties. She also held various positions at companies such as Cox , and UPS focusing on Digital Analytics and Data Science.
Use of AI as a means to gain competitive advantage and performance improvement is a top of nind issue for senior leadership at all large companies. However unlike digital natives, most traditional companies have not be able to leverage artificial intelligence and intelligence automation technology in a significant way that has led meaningful shareholder value creation. The speaker will present his views on how large companies can speed up their adoption of AI and IA technologies to become AI powered enterprise.
Dr. Derek Loftis at the Virginia Institute of Marine Science (VIMS), at the College of William & Mary has developed a video camera system capable of
detecting water level data. The sensor is a modified Deep Learning Camera, which filters true color imagery to identify differences in contrasting pixel
values to detect edges, and then is trained to identify the free surface edge of the water in the camera’s targeted field of view in real time. Upon calibration,
the camera translates that edge to a water level which can be averaged over time and transmitted as ASCII text values via Wi-Fi or cellular broadband at a
designated time interval. If the water level exceeds a pre-established inundation threshold, video or image outputs can optionally be sent along with
triggering automated SMS text alerts. This technology is a derivative work of his efforts in creating and serving as project manager for StormSense, a smart
cities flooding resiliency research project, initially funded in 2016 (bit.ly/2zrWtmw).
StormSense has been nationally recognized and received several awards in the past 2 years (bit.ly/2Qi1Sqd>), and learning from its triumphs and mistakes, it is well poised to release a new web camera-based water level sensing technology, the StormSense Video Inundation Monitoring System (Sensor Prototype via AWS' DeepLens Camera during 2018 Hurricane Florence:youtu.be/VUuHmz9LVA8). The camera is currently in development for commercialization, funded by a private grant through the VIMS Foundation (https://bit.ly/2LP3kgd), and a Virginia state grant through their Center for Innovative Technology (bit.ly/35h8iK9).
Dr. Derek Loftis is an Assistant Research Scientist working in the Center for Coastal Resources Management and the Virginia Commonwealth Center for Recurrent Flooding Resiliency at the Virginia Institute of Marine Science (VIMS). Dr. Loftis graduated with a Ph.D. in Marine Science in 2014 from VIMS at the College of William & Mary upon completing his dissertation research focusing on street-level flood forecasting in New York City during 2012 Hurricane Sandy. Dr. Loftis' hydrodynamic modeling research at VIMS focuses on: (1) development of numerical simulations and inundation forecasts for regions prone to flood damage, (2) validation of model accuracy using drones, sensors, citizen science, and satellite remote sensing observations, and (3) engineering resilient solutions to enhance adaptability to future flood events in the interest of protecting human life and valuable infrastructural assets. His research has recently been featured in Esri’sArcUser Magazine (3D Flood Modeling), AWS’ Blog (Sensor-Driven Automated Flood Alerting), NPR’s Science Friday, and Esri’s Blog (Citizen Science Flood Monitoring). Dr. Loftis teaches remote sensing and advanced geographic information systems classes at VIMS and William & Mary, and heis the project lead and a developer of the hydrodynamic model used inthe StormSense Project in the Greater Hampton Roads Region of Tidewater Virginia.
Description: 85% of AI/ML models never gone into production. This session covers practical issues, pain points faced during AI/ML life cycle, governance and best practices to circumvent these challenges.
Artificial intelligence (AI), machine learning and data have brought enormous opportunity to healthcare. While much of the attention has been centered on use in improving treatments and enabling physicians and providers to make decisions, these technologies have exciting potential to impact patient engagement. It ranges from improving patient outcomes to reducing costs, and driving better care. It also has the power to support providers for more efficient, effective processes. This presentation will provide attendees with a comprehensive look at the use AI and its subset technologies in patient engagement, share case study examples, and empower organizations to engage patients at a powerful new level.
Use this opportunity to improve the visibility of your organization
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
"1.21gws have created nice platform and connected with various top industry experts under one roof.
Thanks for organizing this kind of event Diksha and Nitin. Really looking forward to more of such kind of conferences in future."
"Plenty of informative and sharing of experience was very good"
Finastra software solutions
"Great presentation, None of them was boring". All Equal informative.
Director of Engineering - Machine Learning
Oracle India Pvt Ltd
"Good format! Good focus and quality of delegates"
It was a very worthwhile conference
Round Table was very helpful
Enjoyed the Interaction
All Presentations were Great. Very Informative
Presentations had good content
Who can attend Machine Learning and Deep Learning Day in Atlanta?
• Data Engineers/Developers / Scientists
• Analytics Professionals
• Startup Professionals
• President/Vice president
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 Atlanta?
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?
Do I need to register for the event?
Yes, all conference attendees must register in advance to attend the event.