With data science community thriving to decode the so called black-box notion attached with Machine Learning based solutions, Deep Learning poses an even bigger challenge. While building any Machine Learning or Deep Learning based solutions, domain expertise plays a huge role in the selection of features and the subsequent explaining of the model’s mechanisms. Across the research community, there has been some bit of success in feature engineering of numeric and textual data, however replicating that success for visual content is still at a nascent stages. This talk provides an in depth look at some of these challenges of improving AI’s explainability and highlights some of the possible techniques to overcome those challenges.
Getting your first job in Data Science is difficult. You’ve been applying to jobs, but they keep rejecting you. You don’t know what to do and how you could differentiate yourselves amidst the pool of candidates? In this talk, we’ll be going through different tips and techniques you could use to find that elusive Data Science jobs. They’ve worked for me and probably will work for you too!
The Fourth Industrial Revolution or Industry 4.0 considered as an opportunity as well as a
challenge offers huge potential to advance economic growth, enhance global manufacturing output
and human well-being, to safeguard the environment and to achieve the 2030 agenda for
Sustainable Development Goal (SDG) set by United Nations Industrial Development Organization
(UNIDO). At Oracle, we work towards building smart industry solutions to build a cloud assisted
smart factory (CaSF) system leveraging the potential of Autonomous Database, Analytics Cloud,
Machine Learning, Blockchain and Artificial Intelligence that would revolutionize production
processes, increase the level of efficiency, security, reliability and enhance living standards.
With Oracle’s AI Tech Stack, we build statistical models, means, and forms of intelligent
manufacturing, thus not just transforming the industry but disrupting the space.
o Impact of Analytics in Manufacturing
o With slow down in Automobile / Manufacturing how it has driven more need for Analytics
o How IOT & Cloud has helped accelerate the journey
o Is Analytics in Manufacturing only for high labor cost country
o Data Product Approach – From POCs to Scale
o Demystifying – Edge computing , IOT
o Power of Visualization – From Shop floor to Top floor
o Predictive Analytics reality
Organizations today are using complex AI algorithms and immense computational power to enable their teams and organizations to make smarter decisions. Employees across business verticals with varying skill sets and maturity should have access and be able to absorb the abundance of information and intelligence with confidence to make actionable decisions. How do we help everyone achieve more — humans and machines working together to enhance efficiency, profitability and drive tactical and strategic decisions at scale.