The main question is: what for?
Learning Machine Learning: A beginner's journey
11–20 of 56 posts
Re: Learning Machine Learning: A beginner's journey
#12I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…
I don't think Andrew Ng would agree with your assertion. His Coursera ML class assumes little more than a basic high school math education, and at the start of the course, he teaches the very small subset of linear algebra required to understand his course materials.
I think what Andrew Ng would say is that without a rigorous statistical background, you will be limited in your ability to use ML, and you will certainly be more liable to blow your foot off by using it improperly. That being said, in a subset of cases, you may be able to achieve non-trivial insights through the techniques he teaches in the course.
So how I would rephrase your assertion is that a hacker can probably get a lot more out of ML techniques if they are willing to learn the math underlying them.
Re: Learning Machine Learning: A beginner's journey
#13Any ML tutorial should start with: Its not about machines and not about learning.
It is about both. Machines AND learning.
It is about data and algorithms and if calling them machines and learning or cognition or intution or thinking or whatever makes you happy then no body can stop you from doing so.
Re: Learning Machine Learning: A beginner's journey
#14I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…
What is it not enough for? Yes, having a PhD may help if you are developing the libraries, but developers are consumers.
Re: Learning Machine Learning: A beginner's journey
#15> So I am doubling down on ML/DL. 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 knowledg…
Pretty much this. In the real world you have to deal with - Too few labeled / garbage labeled data (70000 digits? How about only 1000 complex class objects?) - Obscure bugs in custom implementation (yeah my custom layer works and gradient is correct... or wait why it diverges after 10k iteration? hmm). - Timing/RAM constraints (it should segment an image under 10ms on Jetson TX1, well, good luck with GoogLeNet)
Nailed it. Neural net execution speed is so critical for may production systems and it's very difficult to hit the sweet spot on trade offs but I never hear about these issues in wild.
Re: Learning Machine Learning: A beginner's journey
#16I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…
^ this, especially the
> Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on.
I've been coming across this on HN front page and it's worrysome to an extent.
Re: Learning Machine Learning: A beginner's journey
#17I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…
[deleted]
Re: Learning Machine Learning: A beginner's journey
#18I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…
What is it not enough for? Yes, having a PhD may help if you are developing the libraries, but developers are consumers.
ML is easy to get set up, but often difficult to debug if you don't really understand the details. Within the last week I pair reviewed a recommender system written in MLlib (ala this post http://spark.apache.org/docs/latest/mllib-collaborative-filt...), that was doing strange things, despite performing well on a test set. It turned out the metric being used on that page was not a good one for our purposes, and the algorithm had zoomed in on a degenerate solution that nailed the test score. This was clear to me after about 2 minutes by looking at the auxiliary matrices generated. The less experienced person I was helping did not how to proceed.
Re: Learning Machine Learning: A beginner's journey
#19I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…
Totally agree, I've got a solid applied calculus background from my electrical engineering undergrad degree and some DSP in my first job, but avoided learning statistics and probability because I thought it was intuitive. But after doing a MOOC course on Machine Learning, I realise that statistics and probability is more complex than anticipated.
If you don't work on your fundamentals, you end up simply memorising the algorithms and basic applications. Much like the neurons discussed for deep learning, we need to create rich relationships between our own to ensure we retain and can apply the knowledge in the long term, which starts with laying a foundation of fundamentals.
For brushing up on those fundamentals, I recommend Bertsekas & Tsitsiklis : Introduction to Probability. Theory supported with lots of examples, as well as comparing the theory to "intuition" and why it is much more effective to apply the theory.
Re: Learning Machine Learning: A beginner's journey
#20I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…
I've been interviwing for ML positions and what struck me was the general disdain for details. I had one manager claim that they're set to beat their competitors now because they're moving to GOOG's new tensorflow. Others knew little more than Tensoflow and Backprop.
Frankly I regret spending time understanding all the math, instead of working for some company, munging through their data and applying some black box stuff. The hype is quite bad IMO.