Machine Learning Crash Course • Hacker News Board

Re: Machine Learning Crash Course

#71

Post by andyjohnson0 » Thu, Mar 01, 2018, 8:22 PM UTC

This looks like a well put-together course, and a good way to learn TensorFlow. Keras and TensorFlow are top of my list of technologies to explore in the very near future.

Is anyone here doing Andrew Ng's Machine Learning course [1]? I'm about half-way through and really enjoying it. I'm particularly appreciating that the programming exercises are done in MatLab/Octave, so I feel that I'm really understanding the fundamentals without an API getting in the way, and developing some good intuition. Obviously frameworks are the way to go for production ML work, but I wonder whether ML people here think this bottom-up approach is advisable or could it be misleading when I move on to Keras/TensorFlow/whatever?

[1] https://www.coursera.org/learn/machine-learning

Edit: brevity

Re: Machine Learning Crash Course

#72

Post by grad_ml » Thu, Mar 01, 2018, 8:23 PM UTC

xamuel wrote: post #64

minimaxir wrote: post #12

Looking through the topics covered, the standard AI-course caveats ( https://news.ycombinator.com/item?id=16247629 ) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about…

The other day I met with someone who was visiting my city to attend a big ML conference. In the course of our discussion, it transpired this person did not know the Halting Problem. He'd "heard of" Turing machines, but nothing more than "hearing" of them. Gatekeepers shouldn't keep gates just for gatekeeping sake. But if so-called ML experts don't even know undergraduate computer science, that should really give you…

"turing machines" are cs 101?

Re: Machine Learning Crash Course

#73

Post by xamuel » Thu, Mar 01, 2018, 8:25 PM UTC

eanzenberg wrote: post #66

xamuel wrote: post #64

Earlier quoted context omitted.

The other day I met with someone who was visiting my city to attend a big ML conference. In the course of our discussion, it transpired this person did not know the Halting Problem. He'd "heard of" Turing machines, but nothing more than "hearing" of them. Gatekeepers shouldn't keep gates just for gatekeeping sake. But if so-called ML experts don't even know undergraduate computer science, that should really give you…

Why? Most of that cruft is abstracted away, computation only gets cheaper over time (a world class AI rig cost ~30k, a decent one for 2k) and most applications of ML run on commodity hardware.

For one thing, it suggests that they are actually technicians, not the scientists they're selling themselves as.

That's fine if you want a technician (and if they're charging technician's rates).

Re: Machine Learning Crash Course

#74

Post by xamuel » Thu, Mar 01, 2018, 8:27 PM UTC

grad_ml wrote: post #72

xamuel wrote: post #64

Earlier quoted context omitted.

The other day I met with someone who was visiting my city to attend a big ML conference. In the course of our discussion, it transpired this person did not know the Halting Problem. He'd "heard of" Turing machines, but nothing more than "hearing" of them. Gatekeepers shouldn't keep gates just for gatekeeping sake. But if so-called ML experts don't even know undergraduate computer science, that should really give you…

"turing machines" are cs 101?

Amended that to "undergraduate computer science"

Re: Machine Learning Crash Course

#75

Post by fnl » Thu, Mar 01, 2018, 8:30 PM UTC

kmax12 wrote: post #13

Great to see they have a nice introductory section to feature engineering! Feature engineering is often the most impactful thing you can do to improve quality of models and a place where I often see beginners (and experts for that matter) get stuck. Google walks through how to work with json files and categorical variables https://developers.google.com/machine-learning/crash-course/... . If anyone is looking to get m…

Your comment got me interested in this course. However, all I could find about feature engineering there is what you linked to, directly.

Given that entire scientific careers, books, and conferences are built around the topic of feature engineering, and at least IMO good ML tools live or die with good feature engineering (in its broadest sense, for you deep learning fanatics :-)) that doesn't seem like more than the bare minimum I'd expect from any ML "crash-course" that is to be taken serious (and I wouldn't expect an ounce less from Google... :-)).

Am I missing something, maybe?

In any case, nice work of your own, and thanks for sharing it!

Re: Machine Learning Crash Course

#76

Post by zitterbewegung » Thu, Mar 01, 2018, 8:31 PM UTC

I like this move from google. Sure it is targeted for you to use Tensorflow but more courseware and MOOCs help everyone. I love doing self study and Tensorflow's tutorials are top notch. Since I can also use Tensorflow on my own hardware and anywhere else I really love better docs and MOOCs in general. What I really want to do is understand enough Tensorflow to reproduce other people's experiments in their papers on github and I think this would be one of the best ways to do this. Of course, this may eat into a bunch of companies that have paid programs for ML but its Google's prerogative to make ML cheaper and easier to deploy and learn so I am all for that.

Re: Machine Learning Crash Course

#77

Post by bkanber » Thu, Mar 01, 2018, 8:32 PM UTC

andyjohnson0 wrote: post #71

This looks like a well put-together course, and a good way to learn TensorFlow. Keras and TensorFlow are top of my list of technologies to explore in the very near future. Is anyone here doing Andrew Ng's Machine Learning course [1]? I'm about half-way through and really enjoying it. I'm particularly appreciating that the programming exercises are done in MatLab/Octave, so I feel that I'm really understanding the fun…

I teach ML and am currently writing my 2nd book on it.

I always advocate learning the fundamentals. Machine learning is math, and neural networks in particular rely on linear algebra and vector calculus. (You can build a NN without using linear algebra directly, likely it'll be slower and besides, the concept still relies on linear algebra).

Frameworks abstract away a lot of the mathiness, which is a net good for society (ie, exposing lots of developers to neural networks), but I consider that a net-negative for the individual developer.

When working on anything but trivial toy problems, you should make sure you understand your problem domain and implementation thoroughly. Is the activation function you've chosen ideal for your problem domain? If not, choose a better one. If no better one exists, you can invent it; but you'll also need to know how to design the backpropagation algorithm for that new activation function (which requires some vector calculus).

Learning the math, as you have, helps you tune your algorithm based on actual knowledge rather than guesswork. I don't think it will be misleading when you move on to a framework. The frameworks are built on the same math.

That said -- if all you're looking to do is play around, then you don't need the math as much.

Re: Machine Learning Crash Course

#78

Post by bkanber » Thu, Mar 01, 2018, 8:40 PM UTC

bjourne wrote: post #53

I want to ask people who know ML well if the hype is warranted? Billions of courses, web sites, job applications and HN posts. The subject seem to have taken off massively in the last two years. I mean image and speech recognition is pretty cool (when it works!), but hardly that earth shattering, is it?

The hype is and isn't warranted.

ML is a much broader field than just neural networks. The hype for ML, in general, I think is warranted. We hit an inflection point when AWS launched and scalable processing power became cheap. It became cheap to process tons of data and generate insights. I don't have hard numbers on this, but probably 90-95% of machine learning used in practice is NOT neural networks, and have accuracies in the 90%+ arena. So ML in general -- sure, hype warranted.

Neural networks are the new hot topic, and the hype isn't fully warranted yet. TensorFlow made them very popular in the developer community; this is a good thing because it's spurring more investment and research in ANNs. But for any given problem, odds are that a neural network is not the best (ie, most accurate or cheapest) way to solve it. Neural networks do have specific problem domains where they are the state of the art, but for most other problem domains there exists a better solution. So I'd say that neural networks are a little over-hyped right now, but with a new generation of developers learning about and experimenting with ANNs, that will change in a few years. I think we're about to see an explosion of ANN usefulness over the next few years.

TLDR: ML is very useful but is more than neural networks; neural networks need a little more progress to catch up to the tensorflow hype.

Re: Machine Learning Crash Course

#79

Post by bkanber » Thu, Mar 01, 2018, 8:42 PM UTC

Abundnce10 wrote: post #32

I have a new project at work: I need to take in a free form text of recipe ingredients (e.g. "1/2 cup diced onions", "two potatoes, cut into 1-inch cubes", etc.) and build a program that identifies the ingredient (e.g. onion, potato), as well as the quantity (e.g. 0.5 cup, 2.0 units). Would machine learning be an applicable approach to solving this? Right now I'm just planning on using an NLP library to parse out the…

This is an NLP problem if all you're trying to do is extract nouns.

Re: Machine Learning Crash Course

#80

Post by ariwilson » Thu, Mar 01, 2018, 8:46 PM UTC

eanzenberg wrote: post #61

ariwilson wrote: post #56

Earlier quoted context omitted.

Life skills? Communicative skills? What?

IMO essentially ML experts don't work in a bubble and may interface with potentially anyone at a company; C-level, engineering, product, marketing, ops, etc etc. What other tech-employee needs that flexibility? So, I grouped communication / life skills into being able to understand, read, interpret and ultimately provide value to potentially any team. Just having the technical skills will only get you so far.

IMO software engineering experts (leaders) need to do the same thing.