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How to Learn Machine Learning and Deep Learning: A Guide for Software Engineers

59 points by renanmoura 6y ago

clircle 6y ago
Do NOT trust any advice that claims you can learn math by watching a YouTube video. Math is not a spectator sport!
- denimnerd42 6y ago
  what do you mean you watch the video and then you do your homework you just learned math via youtube video (and your homework)
SCUSKU 6y ago
Bit of a nitpick, but it should be "Artificial Intelligence piques my interest" as opposed to "picks".
- renanmoura 6y ago
  thank's for the nitpicking, fixed to "piques". :)
melling 6y ago
I started working through the ISLR book then found someone else to do it with. http://faculty.marshall.usc.edu/gareth-james/ISL/ http://faculty.marshall.usc.edu/gareth-james/ISL/ You can download the PDF and there are videos too: https://www.r-bloggers.com/2014/09/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/ https://www.r-bloggers.com/2014/09/in-depth-introduction-to-... Doing the the questions and labs is helpful with a partner. Machine Language Tokyo has several good sessions. Not many times that work for me, unfortunately. https://www.meetup.com/Machine-Learning-Tokyo https://www.meetup.com/Machine-Learning-Tokyo
vletal 6y ago
While interviewing candidates I have observed an influx of candidates having a strong bias towards neural networks in the recent years - i.e. not understanding concepts such as overfitting in general and telling me how dropout would fix that. We are a mid-size AI/ML consultancy company. We have not yet have a project where we'd actually been given enough data and resources from a customer to be even able to learn a NN from scratch. Vast majority of the problems we tackle are easily solvable with traditional ML methods which these fresh candidates consider obsolete and boring. This tutorial pretends to be mildly aware of that, yet the first course it offers is Andrew Ng's ML, which is structured in a way that it culminates in building a NN, a brief mention of RF and then it jumps straight into DNN. A disclaimer that this is hardly a thing a person would actually do for living should be included at the beginning of every tutorial such as this one.
- renanmoura 6y ago
  Hey, author here. I wrote this back in January and I am aware of what you are saying about people rushing too fast into DNN. The article is an answer to some people asking me for general references. But I agree with the disclaimer in general and will incorporate it into the article. BTW, I started with Ng's course simply because of the way he teaches in a very clear way, and not scaring people out, it is a very good intro and most people would feel confident in moving forward to other resources after his course.
TrackerFF 6y ago
A bit OT: I've always wondered - you see a lot of relatively young engineers and devs doing work in what seems to be cutting edge DL work, and some of these are even self-thought, with sometimes only a few years of experience (at least if their open github accounts and blog resumes are any good gauge of experience). On the other hand - the only (really) talented people I personally know, that are working on Deep Learning problems, all have heavy and very solid academic backgrounds from Math / Physics / CS / etc., and are either finishing up their Ph.D's, or were done the past few years. Most started studying (or working on) ML before the Deep Learning craze took off. All in all, these guys have at least a decade of solid experience - but in return, know a metric ton of classic ML / stats / grad. level math / etc. by heart. Obviously, in probably most cases, one can't compress 10 years of hard work experience into 1-2 years, even though that's what a lot of these "roadmaps" seem to promise. Makes me wonder if the internet-world of deep learning is filled with savants that are able to catch up with decades of research and best-practices in only couple of years, or if these are people that are really, really specialized in some niche application of DL.
- klipklop 6y ago
  Seems to me humans are really good at 'catching up' based on what we perceive as possible. For example when a new world record is set in a sport often the idea of such a performance is possible raises the bar for the entire field rapidly. Within months multiple competitors are nipping at the heels of the record. I also see this in video game speed running. When a new breakthrough record is set suddenly the competitors begin to catch up to the new record now that they see it is indeed possible. Even new people playing the game don't take 10 years to catch up skill wise when they can learn from previous attempts from the 'old masters.' It seems like even in the arena of AI breakthroughs as soon as some paper gets published others quickly catch up. I do agree there is no replacement for 10 years of research and work, but future people studying in the field can and do catch up.
- scsilver 6y ago
  Trailblazers will always make slower progress than those following in their footsteps. It took researchers how long to develop calculus and we become effective at wielding it in 3 classes
ilaksh 6y ago
Ng's Deep Learning Specialization is awesome but I feel after this second course I need to stop and create my own exercises to build and apply NNs from scratch because I am learning how to convert math notation to vectorized Python code as much as anything else. I mean I am learning some core concepts along the way which I definitely appreciate and the reason I am continuing, but to become proficient I need to apply them without the hand-holding. Because those Coursera assignments have evolved to be computer-graded and easily approachable and so they actually do most of the thinking for you. Also I feel the modern approach is not vectorized Python but something like Keras or Tensorflow 2 or anything that is maybe higher level or will allow me to keep the computation on the GPU/TPU and not going back and forth to the CPU like I assume numpy must be doing.

59 points by renanmoura 6y ago

clircle 6y ago

Do NOT trust any advice that claims you can learn math by watching a YouTube video. Math is not a spectator sport!

denimnerd42 6y ago
what do you mean you watch the video and then you do your homework you just learned math via youtube video (and your homework)

SCUSKU 6y ago

Bit of a nitpick, but it should be "Artificial Intelligence piques my interest" as opposed to "picks".

renanmoura 6y ago
thank's for the nitpicking, fixed to "piques". :)

melling 6y ago

I started working through the ISLR book then found someone else to do it with. http://faculty.marshall.usc.edu/gareth-james/ISL/ http://faculty.marshall.usc.edu/gareth-james/ISL/ You can download the PDF and there are videos too: https://www.r-bloggers.com/2014/09/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/ https://www.r-bloggers.com/2014/09/in-depth-introduction-to-... Doing the the questions and labs is helpful with a partner. Machine Language Tokyo has several good sessions. Not many times that work for me, unfortunately. https://www.meetup.com/Machine-Learning-Tokyo https://www.meetup.com/Machine-Learning-Tokyo

vletal 6y ago

While interviewing candidates I have observed an influx of candidates having a strong bias towards neural networks in the recent years - i.e. not understanding concepts such as overfitting in general and telling me how dropout would fix that. We are a mid-size AI/ML consultancy company. We have not yet have a project where we'd actually been given enough data and resources from a customer to be even able to learn a NN from scratch. Vast majority of the problems we tackle are easily solvable with traditional ML methods which these fresh candidates consider obsolete and boring. This tutorial pretends to be mildly aware of that, yet the first course it offers is Andrew Ng's ML, which is structured in a way that it culminates in building a NN, a brief mention of RF and then it jumps straight into DNN. A disclaimer that this is hardly a thing a person would actually do for living should be included at the beginning of every tutorial such as this one.

renanmoura 6y ago
Hey, author here. I wrote this back in January and I am aware of what you are saying about people rushing too fast into DNN. The article is an answer to some people asking me for general references. But I agree with the disclaimer in general and will incorporate it into the article. BTW, I started with Ng's course simply because of the way he teaches in a very clear way, and not scaring people out, it is a very good intro and most people would feel confident in moving forward to other resources after his course.

TrackerFF 6y ago

A bit OT: I've always wondered - you see a lot of relatively young engineers and devs doing work in what seems to be cutting edge DL work, and some of these are even self-thought, with sometimes only a few years of experience (at least if their open github accounts and blog resumes are any good gauge of experience). On the other hand - the only (really) talented people I personally know, that are working on Deep Learning problems, all have heavy and very solid academic backgrounds from Math / Physics / CS / etc., and are either finishing up their Ph.D's, or were done the past few years. Most started studying (or working on) ML before the Deep Learning craze took off. All in all, these guys have at least a decade of solid experience - but in return, know a metric ton of classic ML / stats / grad. level math / etc. by heart. Obviously, in probably most cases, one can't compress 10 years of hard work experience into 1-2 years, even though that's what a lot of these "roadmaps" seem to promise. Makes me wonder if the internet-world of deep learning is filled with savants that are able to catch up with decades of research and best-practices in only couple of years, or if these are people that are really, really specialized in some niche application of DL.

klipklop 6y ago
Seems to me humans are really good at 'catching up' based on what we perceive as possible. For example when a new world record is set in a sport often the idea of such a performance is possible raises the bar for the entire field rapidly. Within months multiple competitors are nipping at the heels of the record. I also see this in video game speed running. When a new breakthrough record is set suddenly the competitors begin to catch up to the new record now that they see it is indeed possible. Even new people playing the game don't take 10 years to catch up skill wise when they can learn from previous attempts from the 'old masters.' It seems like even in the arena of AI breakthroughs as soon as some paper gets published others quickly catch up. I do agree there is no replacement for 10 years of research and work, but future people studying in the field can and do catch up.
scsilver 6y ago
Trailblazers will always make slower progress than those following in their footsteps. It took researchers how long to develop calculus and we become effective at wielding it in 3 classes

ilaksh 6y ago

Ng's Deep Learning Specialization is awesome but I feel after this second course I need to stop and create my own exercises to build and apply NNs from scratch because I am learning how to convert math notation to vectorized Python code as much as anything else. I mean I am learning some core concepts along the way which I definitely appreciate and the reason I am continuing, but to become proficient I need to apply them without the hand-holding. Because those Coursera assignments have evolved to be computer-graded and easily approachable and so they actually do most of the thinking for you. Also I feel the modern approach is not vectorized Python but something like Keras or Tensorflow 2 or anything that is maybe higher level or will allow me to keep the computation on the GPU/TPU and not going back and forth to the CPU like I assume numpy must be doing.