The pace of change in fields such as Deep Learning and AI is incredible, and professionals at every level of experience have to maintain a commitment to ongoing learning if they’re to remain successful. To rest on one’s laurels is to fall behind, and this is as true of senior executives as it is of new-entrants.
Denis Sutherland is VP of Strategy and Technology at a company working on high-tech satellites, and he has more than two decades of experience in the telecoms sector. By all measures he’s a very successful tech professional, yet when it came time to lead his company’s new technical strategy, he didn’t hesitate to return to the classroom. He knew he needed a more robust understanding of AI, so he enrolled in Udacity’s Deep Learning Nanodegree program.
We spoke with Denis to learn more about his experience in the program, and how it enabled him to perform his own role better. We were also eager to get his thoughts on the value of a Nanodegree credential, as someone with responsibility for recruiting technical people himself.
My cat, in the style of Hokusai!
Image processing is one of the most exciting applications of Artificial Intelligence and Deep Learning. Through it, you can train a computer to see and interpret images similar to the way humans perceive images.
In this article, you’ll learn how to use a deep learning model to transfer painting styles with TensorFlow, a machine learning software library first developed by Google. This is a project straight from our Deep Learning Nanodegree program.
When the Udacity Deep Learning Nanodegree Foundation program launched in January of 2017, it was the first comprehensive program of its kind, offering an unrivaled opportunity to master one of the world’s most transformational technologies. Since then, more than 10,000 students have enrolled. Together, these students represent the future. They are the next generation of deep learning experts who will define how these technologies change our world for the better.
The energy and dynamism in the deep learning space is incredible, and we are challenged every day to continue delivering the best learning experience on the planet. As the field of deep learning grows in both complexity and opportunity, so too must our program deepen and intensify.
This is why we are so excited today to announce incredible new additions to our Deep Learning Nanodegree Foundation program!
Editor’s Note: This post is written by Zackarey Thoutt. He’s a Udacity Nanodegree program graduate. You may recognize his name, as he’s been all over the media lately. Vice’s Motherboard magazine put it like this: Neural Network Wrote the Next ‘Game of Thrones’ Book Because George R.R. Martin Hasn’t. Zack created that neural network, and in this post, he shares how it all came to be.
As I was getting ready to graduate from Udacity’s Deep Learning Foundations Nanodegree program, I began to wonder what to do next. I needed a new project to keep my skills sharp. One episode into Season 7 of Game of Thrones (I’m a huge fan!), the idea hit me—why not train a network to write new chapters for the book we’re all waiting for?
Udacity is very excited to announce a new competition, and a new partnership!
We are proud to be the exclusive education partner—with e-commerce giant Alibaba—for a new challenge hosted by Alibaba and IJCAI2017, a top conference in AI space, and the main international gathering of researchers in the field.
Deep Learning is fast emerging as one of the most important technologies of our time. As Artificial Intelligence continues to impact our lives in remarkable and beneficial ways, Deep Learning is powering the progress.
This is why we are so excited today to introduce Siraj Raval’s Deep Learning Nanodegree Foundation Program!
“Machine Learning is everywhere.” This is a phrase we see often these days, and it’s pretty close to a genuine truism. Netflix, Amazon, Siri, Pandora, the list goes on. But it’s not just entertainment and media. It’s also everything from the post office to healthcare to traffic to security. Really close analysis suggests that, for a great many of us, virtually every moment of our lives is touched at some point by Machine Learning.
Is this a good thing?