The rise of social learning has created a new landscape for L&D professionals to explore. This webinar will examine the foundations of Social Learning and discuss how our learners are able to adapt and thrive in this space - mastering the technology and understanding the facets of Social Leadership and ways we engage as part of online communities.Read more >
The human brain makes it look easy. What our eyes see, we decode immediately and effortlessly. But is it that simple? In truth, how we process images is staggeringly complex. Inspired in part by our remarkable neurons, deep learning is a fast-growing area in machine learning research that shows promising breakthroughs in speech, text and image recognition. It’s based on endowing a neural network with many hidden layers, enabling a computer to learn tasks, organize information and find patterns on its own.
Recently, SAS took on a classical problem in machine learning research, the MNIST database, a data set containing thousands of handwritten digit images. Learn how we did – and what it reveals about the future of deep learning.
Despite being over 50 years old, machine learning has become a hot topic for how to optimize the customer experience. But what is machine learning and how can organizations of all sizes take advantage of the technology to enhance their interactions throughout the customer lifecycle?
In this webinar, you will learn:
* What machine learning represents and how it compares to other approaches
* How machine learning works to predict outcomes
* What situations machine learning is best suited for
* How you can get ready to put machine learning to use
This session will explore how Microsoft’s finance team partners with our Decision Science Group and IT to explore new ways of leveraging data to improve business performance. The speakers will share their experiences and show you how Microsoft is using Machine Learning, Power BI and other tools to capitalize on this opportunity. They will also give recommendations on how companies in different stages of transformation with regard to Data Analytics can take the next step or even leap.Read more >
Machine Learning is all about construction and study of systems that can learn from and make predictions on data using predictive analytics. Machine learning has made possible spam filtering, search engines, voice recognition, self driving cars, optical character recognition, etc. This webinar will help you understand how machine learning works.Read more >
Virtual classrooms. Social learning. Student workgroups. By any name, getting students and faculty together across campus and virtual environments is one of the greatest challenges – and opportunities – in Education today.
Now a integrated digital learning application can deliver live online courses leveraging video collaboration, breakout rooms, and lecture recording across any device; heightened social learning by establishing course groups and study networks; enhanced student and professor engagement through virtual office hours, on-demand tutoring, and real-time coaching; and more.
See for yourself, in this brief webinar (approximately 30 minutes)
The term machine learning is frequently heard these days in connection with data science. In this talk, I’ll explain what machine learning is and how it is related to some other terms we hear in the context of data science such as predictive modelling or data mining. I’ll also cover key concepts related to machine learning such as supervised and unsupervised learning, and cover some of the commonly used machine learning approaches like regression, decision trees, clustering and artificial neural networks. Finally, with the help of an example, I’ll go over the process of using machine learning to solve a real life problem.Read more >
Machine Learning has already started to show its potential and will continue to grow in importance, while at the same time, it is often oversold as its pitfalls and costs are not sufficiently emphasised.
This talk will attempt to find a balance between these two impulses. In particular, after a brief overview of machine learning modes, Luis will look at topics such as:
-How good UX design can amplify or mitigate the failures of the ML
component of the system
-How expectation management is important (why do we accept the need to train people, but not computers?)
-Why domain knowledge is almost always necessary, but it's rarely
-Why there will still be a need for data scientists in 20 years (rather
than they, themselves, also being "automated away" by machine learning)
With the rapidly accelerating nature of attacks on network infrastructure and software systems approaches such as static block lists, manual policy configurations and other current prevention techniques have become outdated. Through the use of distributed computing, contextualization and machine learning it is possible to build tools that analyze data across multiple threat vectors allowing for the development of predictive algorithms and a greater understanding of an organizations threat landscape. We will walk through common machine learning techniques, discuss contextualization, how predictive logic works and see a demonstration of contextualized threat intelligence.Read more >