Coding the History of Deep Learning
blog.floydhub.com
Coding the History of Deep Learning
1–10 of 52 posts
Re: Coding the History of Deep Learning
#2Spot on. I struggled with the mainstream deep learning/machine learning MOOCs. I felt like they were to math heavy. However, I'm struggling on how to learn deep learning. I get polarized advice on it. Some argue that you need a degree or certificates from established MOOCs, others keep recommending me to do Kaggle challenges.
Has anyone managed to land a decent deep learning job without formal CS/machine learning training? How did you approach it?
Re: Coding the History of Deep Learning
#3Spot on. I struggled with the mainstream deep learning/machine learning MOOCs. I felt like they were to math heavy. However, I'm struggling on how to learn deep learning. I get polarized advice on it. Some argue that you need a degree or certificates from established MOOCs, others keep recommending me to do Kaggle challenges. Has anyone managed to land a decent deep learning job without formal CS/machine learning tra…
I felt like they were to math heavy. However, I'm struggling on how to learn deep learning.
I do think a lot can be done on the presentation of the material, and certainly don't think much of credentialism.
Honestly, in your shoes I would look for a position where you can learn from people internally, rather than try and qualify yourself first. Even if you do a bunch of online learning and toy problems, you are going to flail about if you don't have a strong mentor in your first position.
What related/supportive skills do you have to bring to a group that is doing ML ?
edit: I should add that you don't really have to understand much these days to integrate (some) ML into a system, but you aren't going to get very far into modeling or understanding issues without some background. You can only get so far with black boxes.
Re: Coding the History of Deep Learning
#4If you want a more nuanced research on the history on deep learning in neural networks, here is an excellent historical survey paper: https://arxiv.org/abs/1404.7828
Re: Coding the History of Deep Learning
#5Spot on. I struggled with the mainstream deep learning/machine learning MOOCs. I felt like they were to math heavy. However, I'm struggling on how to learn deep learning. I get polarized advice on it. Some argue that you need a degree or certificates from established MOOCs, others keep recommending me to do Kaggle challenges. Has anyone managed to land a decent deep learning job without formal CS/machine learning tra…
This is something I've also struggled with. I find it hard to read deep learning papers because I need to translate each math notation, thus struggling to get the bigger picture. I'm fond of the bottom-up approach, e.g. I started by mastering C and wrote my own libraries. But for deep learning I lean towards the opposite, starting with high-level libraries. When I want to understand the theory I search for simple python code that I can implement from scratch. This way I can understand the logic, without having to understand all the math behind it. I've mostly focused on doing Kaggle type of problems and used MOOCs when I get stuck. I've had little interest from larger companies, but I've managed to get a few offers from startups. Startups often have a couple of people with PhD-level knowledge but are also looking programmers that can code the models.
Re: Coding the History of Deep Learning
#6This seems like a great introduction to the history. I have a problem with it, though.
In the first example, the method compute_error_for_line_given_points is called with values 1, 2, [[3,6],[6,9],[12,18]]. Where did those values come from?
Later in that same example, there is an "Error = 4^2 + (-1)^2 + 6^2". Where did those values come from?
Later, there's another form: "Error = x^5 - 2x^3 -2" What about these?
There seem to be magic formulae everywhere, with no real explanation in the article about where they came from. Without that, I have no way of actually understanding this.
Am I missing something fundamental here?
Re: Coding the History of Deep Learning
#7This seems like a great introduction to the history. I have a problem with it, though. In the first example, the method compute_error_for_line_given_points is called with values 1, 2, [[3,6],[6,9],[12,18]]. Where did those values come from? Later in that same example, there is an "Error = 4^2 + (-1)^2 + 6^2". Where did those values come from? Later, there's another form: "Error = x^5 - 2x^3 -2" What about these? Ther…
Good point. They are all example data. The "[[3,6],[6,9],[12,18]]" can be thought of as the coordinates of a comet, and 2 is your predicted correlation, the slope, followed by 1 your predicted constant, the y-intercept. In this case, you want to change 2 and 1 to find the combination that results in the lowest error. It the same with "Error = 4^2 + (-1)^2 + 6^2", it's an example of an error function. Does that make sense?
Re: Coding the History of Deep Learning
#8Would love to see mention of several of the main contributors to deep learning, such as Geoffrey Hinton, the “father” of deep learning, Andrew Ng and Demis Hassabis in future posts.
Re: Coding the History of Deep Learning
#9Spot on. I struggled with the mainstream deep learning/machine learning MOOCs. I felt like they were to math heavy. However, I'm struggling on how to learn deep learning. I get polarized advice on it. Some argue that you need a degree or certificates from established MOOCs, others keep recommending me to do Kaggle challenges. Has anyone managed to land a decent deep learning job without formal CS/machine learning tra…
This is something I've also struggled with. I find it hard to read deep learning papers because I need to translate each math notation, thus struggling to get the bigger picture. I'm fond of the bottom-up approach, e.g. I started by mastering C and wrote my own libraries. But for deep learning I lean towards the opposite, starting with high-level libraries. When I want to understand the theory I search for simple pyt…
There's a difference between people who can implement models and those that can create them -- startups could use people who do the former, and many don't actually need the latter.
Re: Coding the History of Deep Learning
#10Least squares, gradient descent and linear regression separately? I get that he wants to point out the profundity and universality of the ideas encompassed in those techniques (& models; least squares and gradient descent are rightly thought of as (numerical) techniques, whereas the linear regression models is a, well, model) but that is like saying that arithmetic is fundamental to deep learning. Essentially, this "history" only takes you to 1947 and Minsky.