So you finished your deep learning online course and realized that you don’t have the computing power or the necessary coding skills needed to practice deep learning on your own.
Does this sound familiar? Then this might be the article for you.
So, you've just finished your online deep learning course (maybe it was Andrew Ng's famous Deep Learning Specialization?)... You’ve watched videos and lectures, you’ve read the books and perhaps even some papers. You understand how AI and deep learning actually work under the hood now and you’re ready to sink your teeth into some real-life problems to really hone your skills and go from theory to practice.
If this rings a bell then you’re not alone. When I started my deep learning journey, these were exactly the kind of problems I ran into and I’m guessing many others too. Once I faced the reality outside of the curated examples of the online course I hit the wall, so to speak. There was a steep learning curve if I really wanted to work on deep learning problems using open source libraries and I needed to find access to a GPU that wasn’t expensive and didn’t require a lot of setup or maintenance work from me.
This is why I believe the Peltarion Platform is a really valuable tool to use, and a tool I’d wish I’d had myself a few years ago.
Let me tell you why.
A crucial part of learning any new skill is practice. Whether you want to increase your experience, learn by doing or try your hand at practical deep learning, I believe the platform is a great place for that because of 3 reasons.
(By the way, the platform is free to try so go ahead and play with the platform before you commit to anything).
One of my big passions is surfing, so I will talk about what deep learning can do for language, from a surfer's point of view.
If you can find your mouse pad and click on it a total of five times, you can do this.
How have entertainment providers like Spotify managed to provide customers and users with great recommendations – while others desperately lag behind? The short answer is data.