World class is subjective. The term here is practitioner and the simple answer is yes: you will be world class because so few people can do it. World class researcher? No.
Reading HN and following key people gives you the impression that this is an incredibly popular field - it is. In reality, though, it's a small field and lots of companies still don't use it, intend to use it, or have any idea how to use it. Alexnet was only 5 years ago. Faster-RCNN was only 2 years ago. Compared to, say, all programmers, statistically nobody is doing this.
Debugging a model is hard, but it's unlikely your efforts will be significantly more or less productive than a leading researcher aside from obvious things like noticing that the learning rate is too high or low. There's so much hand waving, even in top papers, that mostly we've tried stuff that looked like a good idea, and it turned out to be correct.
The thing is that a lot deep learning tasks don't require a fancy model. You can take VGG/Inception/ResNet and bodge them to fit a lot of real-world scenarios. Then the problems are mostly solved by standard machine learning intuition - making sure you have an appropriate train/test/validation set, a good loss function, etc.
Edit -
Here's an example: Halcon, a popular commercial image processing library has recently included a deep learning package due to repeated customer demand. It's not out yet, but it's most likely just going to be a wrapper for train/test using a basic image classification or object detection network. That's what the market wants. They don't care about GANs, they want something which will tell them if the image represents a valid product during QA 99% of the time (and often QA failures are pretty obvious - e.g. big crack in a bottle, or a deformed label).
You could build that kind of model with very little expertise and you'd beat most of the available image processing packages easily, with practically no work. The point is that an off-the-shelf model trained on company-specific data is likely to be state of the art on that problem (because who else has the dataset?) You don't care about 1000 classes in Imagenet, you want good product/bad product.
Reading HN and following key people gives you the impression that this is an incredibly popular field - it is. In reality, though, it's a small field and lots of companies still don't use it, intend to use it, or have any idea how to use it. Alexnet was only 5 years ago. Faster-RCNN was only 2 years ago. Compared to, say, all programmers, statistically nobody is doing this.
Debugging a model is hard, but it's unlikely your efforts will be significantly more or less productive than a leading researcher aside from obvious things like noticing that the learning rate is too high or low. There's so much hand waving, even in top papers, that mostly we've tried stuff that looked like a good idea, and it turned out to be correct.
The thing is that a lot deep learning tasks don't require a fancy model. You can take VGG/Inception/ResNet and bodge them to fit a lot of real-world scenarios. Then the problems are mostly solved by standard machine learning intuition - making sure you have an appropriate train/test/validation set, a good loss function, etc.
Edit -
Here's an example: Halcon, a popular commercial image processing library has recently included a deep learning package due to repeated customer demand. It's not out yet, but it's most likely just going to be a wrapper for train/test using a basic image classification or object detection network. That's what the market wants. They don't care about GANs, they want something which will tell them if the image represents a valid product during QA 99% of the time (and often QA failures are pretty obvious - e.g. big crack in a bottle, or a deformed label).
You could build that kind of model with very little expertise and you'd beat most of the available image processing packages easily, with practically no work. The point is that an off-the-shelf model trained on company-specific data is likely to be state of the art on that problem (because who else has the dataset?) You don't care about 1000 classes in Imagenet, you want good product/bad product.