The amount of free resources now available for learning machine learning/deep learning nowadays is robust and easy to comprehend. (indeed, Andrew Ng's Coursera class is very good). And running running ML code is even easier, with libraries like Tensorflow/Theano to abstract the ML gruntwork (and Keras to abstract the abstraction!)
I suspect that there may be machine learning knowledge crash, where the basics are repeated endlessly, but there is less unique, real world application of the knowledge learned. I've seen many Internet testimonials saying how "I followed an online tutorial and now I can classify handwritten digits, AI is the future!" The meme that Kaggle competitions are a metric of practical ML skill encourages budding ML enthusiasts to look at minimizing log-loss or maximizing accuracy without considering time/cost tradeoffs, which doesn't reflect real-world constraints.
Unfortunately, many successful real world applications of ML/DL are the ones not being instructed in tutorials as they are trade secrets (this is the case with "big data" literature, to my frustration). OpenAI is a good step toward transparency in the field, but that won't stop the ML-trivializing "this program can play Pong, AI is the future!" thought pieces (https://news.ycombinator.com/item?id=13256962).