Deep learning is the latest method of machine learning, and has been steadily gaining interest from engineers for the last few years. Yes, a certain level of knowledge is expected before entering this field. But there’s no set path to starting a career in the deep learning realm. What does that mean for engineers? They might be closer to landing a deep learning job than they think.
So where do you start if you want a job in deep learning engineering?
1. Find Out Who’s Hiring, and What They’re Looking For
A good place to start is by looking at existing roles that companies are hiring for. This will give you an initial understanding of the sort of skills and experience that companies actually look for in deep learning engineers. It’ll also let you know the types of companies you could find yourself working for. There are plenty of sites you can refer to for available jobs, including the big job boards like Indeed and Glassdoor, and also more specialized sites like Hacker News’ monthly ‘Who’s Hiring’ and the startups-only AngelList. You’ll find that plenty of companies are hiring deep learning engineers, and all with their own requirements for education, experience and skills.
Many believe that all deep learning engineers boast PhDs with experience that takes decades to accumulate. While this may be true for some of these engineers, it’s not a standard that everyone in the deep learning industry has to strive for. A look at those job boards can tell you that much.
What should you be striving for, then?
2. Get Educated
Work out what you already know… or what you might need to brush up on.
For starters, deep learning engineers need strong mathematical skills. Calculus, probability and linear algebra are highly useful in this field, and necessary in understanding deep learning theory. The most popular deep learning libraries are Python and R, so programming experience in either language is useful.
Resources are everywhere these days, and there are plenty of ways for you to refresh your programming or math skills through online courses, books, podcasts etc. However you like to learn, there’s a platform to help you. With these skills, you’ll be ready to tackle deep learning itself. Convenient, online courses can introduce you to deep learning, or to help you practice it. Coursera currently has over 250 courses on deep learning, and Udacity offers both free and ‘Nanodegree’ versions of their deep learning program. If online courses aren’t your thing, there are both practical and theoretical books to help you get up to speed on all things deep learning.
Udacity’s free and Nanodegree offerings for Deep Learning
Going down this path of self-teaching or online learning requires accountability. Consider starting a blog, or jotting down some notes to keep you organized on your learning journey. When you’re ready to start job searching, these records become evidence of your knowledge and skills to future employers.
3. Build Your Portfolio
With these sorts of resources available, it’s possible to enter the deep learning career field without the years of higher education you may think is required. As for experience, engineers who have worked on machine learning projects, big or small, look great on your résumé. If you need to bulk up your machine learning experience, then starting a personal project on a platform like Kaggle allows you to experiment and learn whilst also creating something that showcases your talents to employers or recruiters.
Combining a solid work portfolio with a thorough understanding of deep learning puts you in a great position when you’re ready to start looking for jobs. With the sorts of resources available today, it’s more convenient than ever to start learning and practicing for a career in deep learning.
Does this sound like the job for you? If you’re ready to get a job in deep learning engineering, submit your resume to Elevano today!
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source https://www.elevano.com/how-to-get-deep-learning-engineer-jobs/
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