Earn a scholarship from Facebook and Udacity, and learn how to build, train, and deploy state-of-the-art deep learning models with PyTorch. Apply today!
Today, we’re pleased to share details of our newest offering to rapidly expand students’ machine learning skills —the PyTorch Scholarship Challenge from Facebook.
PyTorch is an open source deep learning framework that’s quickly become popular with AI researchers for its ease of use, clean Pythonic API, and flexibility. With the preview release of PyTorch 1.0, developers can now seamlessly move from exploration to production deployment using a single, unified framework.
This new scholarship program, announced today at the PyTorch Developer Conference, offers participants the opportunity to acquire cutting-edge skills in deep learning using PyTorch, and earn a full scholarship to Udacity’s Deep Learning Nanodegree program.
Gil Akos knew the idea was a good one. He knew the product was something the world needed. But he didn’t have the skills yet to make it a reality. Four Nanodegree programs later, and Astra is live!
Gil Akos had an incredible idea for a new app. He wanted to empower individuals to make better decisions about their money through the power of technology. Gil believed that through smart financial modeling, based on deep learning, he could give people the power to make smarter moves with their money.
There was just one problem. Gil’s background was in architecture, not artificial intelligence. While his self-taught programming experience could get him so far, he lacked the skills to turn his app into reality. So Gil set out to master the skills and tools he needed. He found Udacity, and enrolled in the Machine Learning Engineer Nanodegree program. Now, two years later, his new iOS app—Astra—has officially launched, and Gil has graduated from four (and a half!) Nanodegree programs that he took to learn the skills he needed to bring his app idea to life.
We spoke to Gil to hear about his story.
The 2018 AI Challenger Global AI Contest is being co-organized by Sinovation Ventures—along with Sougo, Meituan-Dianping, and Meitu—and it features 10+ brand-new datasets, and a $450,000 USD prize.
In September of 2017, as an exclusive global education partner, Udacity had the pleasure of announcing details of a new AI competition and open data initiative organized by Sinovation Ventures. The event was entitled the AI Challenger Global AI Contest, and as Udacity COO Clarissa Chen noted in her contest announcement, “Artificial Intelligence has hit fever pitch in China. From cutting-edge tech powerhouses to established large corporations, everyone is setting up new AI Labs, hiring new talent, and announcing new platforms. “AI First” is the order of the day in China, and the excitement around this transformational technology is incredible.”
Success in 2017
The energy around AI across China was indeed incredible, and the subsequent success of the competition was clear evidence that it had been a perfect time to launch this kind of effort. Approximately 9000 teams from more than 60 different countries participated in the AI Challenger Global AI Contest, in 2017, and as reported by China Daily at the conclusion of the competition:
“AI Challenger released over 300,000 image-based as well as over 10 million language-based data entries to talents working on new technologies that will help AI applications ‘see’ and ‘read’ our world better.”
The 2018 AI Challenger Global AI Contest
China continues to represent a remarkable nexus for global AI innovation, and the 2018 AI Challenger Global AI Contest returns bigger, broader, and with more data than ever. As with last year, Udacity is especially pleased to be an exclusive global education partner, and we are excited to extend our ongoing collaboration with Sinovation Ventures to include new courses developed specifically to support contest entrants.
The Machine Learning community is busy sharing new research and insights on Twitter, and this is an extensive list of recommended AI researchers and pioneers to follow.
Artificial intelligence is advancing at a rocket’s pace, and every year the field looks fundamentally different than the year before. It’s often difficult to keep up with all the news and exciting results. The best way I’ve found is to follow the machine learning community on Twitter. This community lives on Twitter, sharing new research and insights, opinions, jokes, and just generally supporting each other. Keeping track of advancements in AI is not only fun but will also help in interviews by demonstrating to hiring managers your investment in the field.
To get you started following the machine learning community, here’s a fairly extensive list of AI researchers and pioneers I’m following.
Udacity’s AI Programming with Python Nanodegree program was updated this week, with additional Python lessons, a new project where students will learn how to use a pre-trained image classifier, and a new visual calculus lesson. As these changes go live, we’d love to introduce you to the program’s instructor and Curriculum Lead, Ortal Arel.
Hi Ortal! Can you start by telling us a little about your background? What did you do before becoming Udacity’s Curriculum Lead for the AI Programming with Python Nanodegree program?
I’ve always had a passion for math, and teaching science and engineering. I studied electrical and computer engineering as an undergraduate, where I became interested in pursuing graduate studies in intelligent algorithms for embedded systems—such as mobile devices. I received my PhD, then I began teaching undergraduate and graduate courses in signals and systems, logic design, and applied cryptography at the University of Tennessee.
Was there a particular moment that really sparked your passion for AI, when you knew it was what you wanted to do with your career?
My initial fascination with the world of AI started with my research in embedded systems. I worked on the design and analysis of intelligent algorithms for high speed digital architectures. Learning more about intelligent algorithms led to a passion for Machine Learning (ML) and Deep Learning. I’ve been increasingly amazed with all the ML applications being applied to help better society, like those used in healthcare systems, in agriculture, in automated driving, and more. Prior to the emergence of machine learning as a discipline for addressing challenging data science problems, signal processing was the prevailing approach. Given the many mathematical similarities between the analytical tools behind signal processing and machine learning, I was able to fairly quickly learn about the recent advances in ML and AI.
As a lifelong learner, your journey of discovery is ongoing. To ensure you’re able to make the best choices to achieve your unique life and career goals, our Nanodegree programs are organized into schools that offer clear roadmaps to success.
Is there such a thing as too much choice? When it comes to learning, we don’t think so. But, having a wide range of learning options at your disposal can make charting your own path to career success complicated. Each of us brings a unique set of skills and experience to the table, each of us has our own unique sense of work values, and each of us aims to represent a unique value to prospective employers. But even with a clear end goal in sight, it can be challenging to determine what exactly you need to do to reach your career goals.
With 30 different Nanodegree programs available, Udacity offers a wide range of learning opportunities. But how do you know what to take, and when to take it?
That’s where Udacity’s “Schools” can help!
Willian Ver Valem Paiva has come a long way—both geographically, and in his career. He’s gone from Brazil to France, via Ireland, and he’s recently landed an incredible new role as a Lead Artificial Intelligence Engineer at a start-up in Bordeaux.
Growing up in Sao Paulo, Willian spent hours working out how to program his computer. It became his passion, and he knew he wanted a career working with computers. When he started looking at universities, he found he simply couldn’t afford the expensive fees to pursue a computer science program in Brazil. What he found instead was a company that would sponsor him to study engineering. It wasn’t the subject he loved, but at least it was a degree program. Within a few years however, he realized he’d made the wrong choice, so he did something very bold. He emptied his savings, left his program, and got on a plane.
This is the story of what happened next.