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Perhaps I may be mistaken, but this seems to be a very long road for a more shallow understanding of deep learning. I'd venture this was written by someone who

Perhaps I may be mistaken, but this seems to be a very long road for a more shallow understanding of deep learning. I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation; however, I'd venture that that is a rather inefficient way to get to deep learning proficiency. If I were to give a recommendation, it would be this -- pick a topic/project that interests you, follow the classic knowledge-bootstrapping process where you read through papers and (hopefully) have an expert or trained person walk you through the specifics, then get hands-on instantly. Especially with something like fast.ai that values empirical results over hard theory, something understandably popular in the field. From there I'd recommend branching out, but I'd use a JIT approach. The field isn't necessarily super well-founded at the moment, and while machine learning fundamentals are useful, ultimately it's a waste of time compared to the long-tail benefit of getting immediate empirical results and feedback hands-on. Just my 2c, YMMV, and anyone is totally welcome to disagree as they wish! :) Best of luck, T

jorlow 6y ago
> I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation This is the vibe I got as well. Which is fair enough (to each his/her own), but I thought I'd mention fast.ai which takes the opposite approach: > Harvard professor David Perkins, who wrote Making Learning Whole (Jossey-Bass), has much to say about teaching. The basic idea is to teach the whole game. That means that if you're teaching baseball, you first take people to a baseball game or get them to play it. You don't teach them how to wind twine to make a baseball from scratch, the physics of a parabola, or the coefficient of friction of a ball on a bat.
amaigmbh 6y ago
You are right that the "Deep Learning" section is rather shallow up to now. We are currently working on expanding it to offer a more comprehensive view of the field and expect to release this update next week. Stay tuned! :)
- amaigmbh 6y ago
  Just as we promised, we now updated and expanded the "Deep Learning" section. Check it out!
wunderwuzzi23 6y ago
I agree with your suggestion, and that is what I did to get started in machine learning as well. Solving some kind of problem and doing practical projects and playing around with models is probably the most efficient way to learn. I started with the Machine Learning course on Coursera by Andrew Ng. And while going through, I started using what I learned on a small project. I did document my journey in case it's useful or interesting for others: https://embracethered.com/blog/posts/2020/machine-learning-basics/ https://embracethered.com/blog/posts/2020/machine-learning-b...
i_love_music 6y ago
"I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation" Yeah I get the same feeling. I am hesitant to suggest it, but gives a little bit of a gate-keeping vibe. Like only once you've payed sufficient homage to the same education path that I took, shall you be granted permission to be a 'real data scientist'. That said, I think there is value in something like this more for hiring managers than practitioners... which makes sense considering the intro comments.
amaigmbh 6y ago
As for our opinion (which is just that, an opinion) why we think that the statistical foundations and knowledge about more traditional algorithms is important, it's based on the business needs and our experience. While it might seem less necessary if the goal is to "learn deep learning", it is highly relevant if your task is to "solve this business problem". Our perspective is the industrial one. And while there are certainly many complex business problems where deep learning is required, there are more cases where a traditional approach is sufficient and actually the better solution (e.g. due to the memory footprint, latency or other reasons). We routinely work on both kinds of problems on a day to day basis, but we would never go straight to deep learning approaches if simpler and faster traditional methods comprise a better solution in a given use case. So, our employees are expected to know both and to be able to judge when to apply which approach.
- tnbalsam 6y ago
  Yes, this makes sense. I'd suggest retitling the article to be something along the lines of "ML Expert Roadmap", and then continue to flesh out all avenues. As it stands, the roadmap has really nothing to do with AI at all, but your point about not just jumping to the shiny hammer certainly rings true and makes sense. I certainly think that's the right approach algorithmically, especially if you're looking to be a more generalist data shop. In a world of senseless marketing hype, I think it's a good idea to take the high road on this one. Reputation alone, even if less-buzzy words like ML are used in favor of AI, really carries a long ways. Plus, we're nearing the disenfranchisement hump, and AI's going to start taking a negative connotation with many businesses, I believe. Just shoot straight and I firmly believe it'll carry you for a long ways, there.

jorlow 6y ago

> I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation This is the vibe I got as well. Which is fair enough (to each his/her own), but I thought I'd mention fast.ai which takes the opposite approach: > Harvard professor David Perkins, who wrote Making Learning Whole (Jossey-Bass), has much to say about teaching. The basic idea is to teach the whole game. That means that if you're teaching baseball, you first take people to a baseball game or get them to play it. You don't teach them how to wind twine to make a baseball from scratch, the physics of a parabola, or the coefficient of friction of a ball on a bat.

amaigmbh 6y ago

You are right that the "Deep Learning" section is rather shallow up to now. We are currently working on expanding it to offer a more comprehensive view of the field and expect to release this update next week. Stay tuned! :)

amaigmbh 6y ago
Just as we promised, we now updated and expanded the "Deep Learning" section. Check it out!

wunderwuzzi23 6y ago

I agree with your suggestion, and that is what I did to get started in machine learning as well. Solving some kind of problem and doing practical projects and playing around with models is probably the most efficient way to learn. I started with the Machine Learning course on Coursera by Andrew Ng. And while going through, I started using what I learned on a small project. I did document my journey in case it's useful or interesting for others: https://embracethered.com/blog/posts/2020/machine-learning-basics/ https://embracethered.com/blog/posts/2020/machine-learning-b...

i_love_music 6y ago

"I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation" Yeah I get the same feeling. I am hesitant to suggest it, but gives a little bit of a gate-keeping vibe. Like only once you've payed sufficient homage to the same education path that I took, shall you be granted permission to be a 'real data scientist'. That said, I think there is value in something like this more for hiring managers than practitioners... which makes sense considering the intro comments.

amaigmbh 6y ago

As for our opinion (which is just that, an opinion) why we think that the statistical foundations and knowledge about more traditional algorithms is important, it's based on the business needs and our experience. While it might seem less necessary if the goal is to "learn deep learning", it is highly relevant if your task is to "solve this business problem". Our perspective is the industrial one. And while there are certainly many complex business problems where deep learning is required, there are more cases where a traditional approach is sufficient and actually the better solution (e.g. due to the memory footprint, latency or other reasons). We routinely work on both kinds of problems on a day to day basis, but we would never go straight to deep learning approaches if simpler and faster traditional methods comprise a better solution in a given use case. So, our employees are expected to know both and to be able to judge when to apply which approach.

tnbalsam 6y ago
Yes, this makes sense. I'd suggest retitling the article to be something along the lines of "ML Expert Roadmap", and then continue to flesh out all avenues. As it stands, the roadmap has really nothing to do with AI at all, but your point about not just jumping to the shiny hammer certainly rings true and makes sense. I certainly think that's the right approach algorithmically, especially if you're looking to be a more generalist data shop. In a world of senseless marketing hype, I think it's a good idea to take the high road on this one. Reputation alone, even if less-buzzy words like ML are used in favor of AI, really carries a long ways. Plus, we're nearing the disenfranchisement hump, and AI's going to start taking a negative connotation with many businesses, I believe. Just shoot straight and I firmly believe it'll carry you for a long ways, there.