If you’re at all keyed into artificial intelligence (AI) and machine learning (ML) concepts, chances are that you’ve heard of deep learning. It’s a fascinating topic, branching off of regular ML and delving into neural networks that attempt to mimic the way the human brain works. With deep learning, machines can practice unsupervised learning and figure out how to make decisions without direct guidance from a human.
In a continuation of celebrating this year’s Women’s History Month, we would like to introduce Ayşin Taşdelen, an artificial intelligence professional and three-time Nanodegree program graduate. She has let her curiosity and desire for new skills lead her through three Nanodegree programs, new jobs, and side projects.
We recently had a chance to speak with Ayşin to hear about her motivations and interest in pursuing cutting-edge technologies.
You studied mathematics during your university years and then became a programmer, what were some of your initial career goals?
I really enjoyed my university studies, so much so, that I initially looked into becoming a full-time researcher. Leaving academia was a tough decision, I loved learning but also knew that starting a traditional career would help me financially. I decided to go the career route and follow my interest in computer science. My initial career goal was to land a job and improve my programming skills.
As your career has developed, how have you satiated your desire to learn?
Over the years, I have tried to keep up with industry articles and books about the latest computer and tech trends. As the internet surged, I started using online library subscriptions and video learning paths. Reading and watching videos were great, but they only get you so far; I never felt like I was learning enough about a subject or concept, until, Udacity.
We talk with Dan Romuald Mbanga, Global Lead of Business Development for Amazon AI, about teaching students to use SageMaker for training and deploying deep learning models.
Deep Learning is one of the most exciting technology fields in the world today, and because Udacity’s learning platform is built to allow for maximum adaptability, our Deep Learning Nanodegree program is one of our most dynamic and future-facing programs right now, as we continue to respond to advances in the field by augmenting and enhancing our curriculum.
We are very excited to share details about the latest additions to our program curriculum, which include new content and projects focused on PyTorch and SageMaker. In a recent post by Cezanne Camacho, Curriculum Lead for Udacity’s School of Artificial Intelligence, we discussed new PyTorch content, and today, we’re going to explore how we’ll be teaching students to use SageMaker for training and deploying deep learning models.
To integrate the incredible new content, we teamed up with AWS and the SageMaker team, and in the updated program, students will train and deploy a sentiment analysis model on SageMaker, then connect it to a front end through an API using other AWS services. After deploying a model, students will also learn how to update their model to account for changes in the underlying data used to train their model—an especially valuable skill in industries that continuously collect user data.
To provide a closer look into the world of SageMaker, we spoke recently with Dan Romuald Mbanga, Global Lead of Business Development for Amazon AI, and a leader of business and technical initiatives for Amazon AI platforms.
The Deep Learning Nanodegree program was one of the first Udacity programs built as a direct and immediate response to the very latest advancements in the field of AI, and as such, it was an early and unprecedented opportunity for aspiring learners to master valuable and in-demand deep learning skills. Deep learning is such a dynamic and rapidly-advancing field, and it has been a delight to see so many students learn and grow with this field. Thousands of students have graduated from the program, and many have gone on to great careers at companies like OpenAI, NASA, and more—not to mention the amazing personal projects our graduates continue to build!
As researchers learn more about deep learning, and as the technology evolves, our curriculum must advance as well. The rapid pace of change in this field means that we’re constantly updating and enhancing the content in this program, in order to consistently ensure our students always have the best educational experience possible. Staying up-to-date with a field this innovative isn’t easy, but our commitment to doing so is a big part of why this program is so special.
In this post, I’m going to share some exciting new updates to our Deep Learning Nanodegree program: the use of an additional deep learning framework: PyTorch, a new section on Model Deployment, and a new lesson on Image-to-Image transformation!
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.