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How to start a career in AI

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How do I start my career as a deep learning engineer? What are the key tools and frameworks used in AI? How can I learn more about AI ethics?

Everyone has questions, but the most common question about AI always comes back to: How can I get involved?

Having survived the hype of sharing basic principles for building a career in AI, a group of AI experts who gathered at NVIDIA’s GTC conference in the spring provided a great place to start.

Each panelist joined the industry from a very different location in a conversation with NVIDIA’s Louis Stewart, Head of Strategic Initiatives for the Developer Ecosystem.

Watch the session on demand.

But the speaker is Katie Kallot, former Head of Global Developer Relations and Emerging Areas at NVIDIA. David Ajoku, founder of startup He is Sheila Beladinejad, CEO of Canada Tech. And Professor Thiem Ruth of the University of Helsinki goes back to four basic principles over and over again.

1) Start with networking and mentorship

Ajoku says the first best thing to do is find someone who is where you want to be in five years.

Don’t just search online, search Twitter and LinkedIn. Look for opportunities to connect with others in your community or at professional events that go where you want to be.

“You want to find someone you look up to, someone who will take the path you want in the next five years,” Ajoku said. you have to go get it

At the same time, be generous with sharing what you know with others. “You want to find someone to teach you. By teaching you learn,” he added.

But the best place to start is knowing that it’s okay to reach out.

“When I started my career in computer science, it never crossed my mind that I should be looking for a mentor,” Beladinejad echoed other panelists.

“I’ve learned not to be shy, to seek support and ask for help whenever I get stuck on something. Always approach your professors and classmates with confidence,” she added.

2) gain experience

Carrot explained that the best way to learn is by doing.

She has a degree in Political Science and learned about technology including how to code while working in the industry.

She started out as a sales and marketing analyst before making the leap to the role of product manager.

“I had to learn everything about AI in three months, as well as how to use the product, how to code,” she said.

According to Roos, the best experiences are with people who are in the same learning process.

“Don’t do it alone. If you can, get your friends and colleagues together to start study sessions and create a curriculum,” he said. “See him twice a week, once a week. It’s a lot more fun.”

3) Soft skills development

You will also need communication skills to explain what you are learning and doing with AI as you progress through your learning.

“Practice speaking to a non-technical audience about technical topics,” says Stewart.

Ajoku recommended learning and practicing public speaking.

Ajoku took acting classes at Carnegie Mellon University. Similarly, Roos took an improvisational comedy class.

Other members of the panel learned to perform in public through dance and sports.

“The more I cross-train, the more comfortable I feel and the more I can express myself in any environment,” Stewart said.

4) Define your reasons

But the most important element is inside, said panelists.

They urged listeners to find reasons to stay motivated to travel.

For some, it’s an environmental issue. Others are driven by a desire to make technology more accessible. Or, to help make the industry more inclusive, panelists said.

“If you have a topic that you’re passionate about, it works for everyone,” Beladinejad said. “It keeps you going and helps keep you motivated.”

Whatever you do, “do it with passion,” said Stewart. “Do it with purpose.”

burning question

Throughout the conversation, thousands of virtual participants submitted over 350 questions on how to start a career in AI.

among them:

What’s the best way to learn about deep learning?

The NVIDIA Deep Learning Institute offers a wide variety of hands-on courses.

More resources are available to new and experienced developers alike through the NVIDIA Developer Program. It contains resources for those pursuing higher education and research.

Massive open online courses (MOOCs) make learning about technical subjects more accessible than ever. One panelist suggested looking for a class on Coursera taught by Professor Andrew Ng of Stanford University.

“There are tons of MOOC courses on YouTube, videos, books, etc. Finding a study buddy is also highly recommended,” wrote another user.

“Join our technical and professional network… Gain experience through volunteering, participating in Kaggle competitions, and more.”

What are the most common tools and frameworks used in machine learning and AI in the industry, and which ones are important for landing your first job or internship in the field?

As one panelist suggested, the best way to figure out which technology you want to start with is to think about what you want to do.

But another suggests that learning Python isn’t a bad place to start.

“Many of today’s AI tools are based on Python,” they wrote. “Once you master Python, you can’t go wrong.”

“Technology is evolving rapidly, so many AI developers today are learning new things all the time. And you’re ready to “learn on the job”.

What’s the best way to gain experience in the field? Do personal projects count as experience?

Student clubs, online developer communities, volunteerism, and individual projects are all great ways to gain hands-on experience, panelists wrote.

Also, be sure to include personal projects in your resume.

Is there an age limit to participate in AI?

Panelists wrote that age is not a barrier at all, whether you’re just starting out or transitioning from another field.

Create a personal portfolio so you can better showcase your skills and abilities. This is important.

Employers should be able to easily realize your potential and skills.

We want to build tech startups with some form of AI as the engine that drives solutions to solve as yet undetermined problems. Do you have any advice for entrepreneurs?

Entrepreneurs must apply to participate in NVIDIA Inception.

The program offers complimentary benefits such as technical support, go-to-market support, preferential hardware pricing, and access to VC alliances for funding.

Which programming language is best for AI?

Python is widely used in deep learning, machine learning, and data science. Programming languages ​​are at the heart of a thriving ecosystem of deep learning frameworks and developer tools. It is primarily used for training complex models and real-time inference for web-based services.

C/C++ is a popular programming language for self-driving cars and is used to deploy models for real-time inference.

However, beginners should be familiar with a variety of tools, not just Python.

The NVIDIA Deep Learning Institute’s self-paced beginner course is one of the best ways to learn.

Find out more at GTC

Hear first-hand from experts about how their careers began at NVIDIA GTC, the global AI conference, taking place September 19-22.

Register now for free and check out our sessions How to become a deep learning engineer When 5 paths to a career in AI.

Quickly learn the fundamentals of AI from NVIDIA: Getting started resources To explore the foundations of today’s hottest technology, Learning series page.