TensorFlow, Keras and deep learning, without a PhD • Hacker News Board

Re: TensorFlow, Keras and deep learning, without a PhD

#151

Post by johnsonjo » Sun, Jul 19, 2020, 3:04 AM UTC

dumb1224 wrote: post #141

citilife wrote: post #120

Earlier quoted context omitted.

I am one of those without a PhD, but have taken the time to the learn the math & contribute quite a bit to this area. That being said, I also don't think deep learning itself is not really a "science". The issue I have is you can't predict if a network will learn. We're effectively testing deep learning networks the same way the Romans used to test bridges. Send a bunch of elephants over them, if it holds it's good e…

> We're effectively testing deep learning networks the same way the Romans used to test bridges. Send a bunch of elephants over them, if it holds it's good enough. Well for human level tasks maybe, but what about other areas of research where we like to discover patterns not seen by a human? Like finding links between genomic interplay with external perturbation such as radiotherapy? It'll be very lucky to have such…

Well I’m certainly no expert on this, but I would guess due to the previous comment the field is possibly too immature at the moment to have as much mathematical certainty as you might find with other methods and fields of mathematics. I recently read the beginning of the book Introduction to Mathematical philosophy by Bertrand Russell and in it he explains how at the times of the Egyptians though they invented geometry it wasn’t very formal and it was very much like the grandparent explained Machine Learning is where Romans used to test bridges they would just throw enough examples at something until they thought it worked. This didn’t mean geometry could never reach a system where they could know the surety of their theorems. The Greeks did just that by starting from basic assumptions or axioms and building a consistent and partially complete system that allowed them to prove many things that followed from there assumptions. There’s a possibility that at some future time (possibly future generations) we’ll have better mathematical tools to figure out the specifics of why neural networks and machine learning work and to the specific extent they do work.

I am also currently reading a Programmer’s Introduction to Mathematics by Jeremy Kuhn (excellent book by the way I would whole heartedly recommend to programmers who have some background in math or thinking in abstractions but who want to learn more math) and it has a quote that states learning Mathematics is a lot like walking into a series of dark rooms and feeling around and getting a feeling of what is in the room until you flip on the light switch but then you could always go to a new room and start all over. I think in that sense machine learning is a series of rooms some lit, but a majority still dark that we haven’t grasped yet.

Re: TensorFlow, Keras and deep learning, without a PhD

#152

Post by dumb1224 » Sun, Jul 19, 2020, 1:07 PM UTC

johnsonjo wrote: post #151

dumb1224 wrote: post #141

Earlier quoted context omitted.

> We're effectively testing deep learning networks the same way the Romans used to test bridges. Send a bunch of elephants over them, if it holds it's good enough. Well for human level tasks maybe, but what about other areas of research where we like to discover patterns not seen by a human? Like finding links between genomic interplay with external perturbation such as radiotherapy? It'll be very lucky to have such…

Well I’m certainly no expert on this, but I would guess due to the previous comment the field is possibly too immature at the moment to have as much mathematical certainty as you might find with other methods and fields of mathematics. I recently read the beginning of the book Introduction to Mathematical philosophy by Bertrand Russell and in it he explains how at the times of the Egyptians though they invented geome…

I think there's a difference between pure mathematics and applied math in these discussions. Exploring the dark room of math is one thing while having a whole lot of great tools and figuring out how to use them in a rain forest to build a livable dwelling is quite another. Math being the abstraction of the world needs bridges to the problems we are facing. Hence the practice of trying a few real life examples (elephants) to test whether it (a method) works.

Re: TensorFlow, Keras and deep learning, without a PhD

#153

Post by gas9S9zw3P9c » Sun, Jul 19, 2020, 3:41 PM UTC

shrimpx wrote: post #149

listenallyall wrote: post #106

Earlier quoted context omitted.

Not confusing. He's saying that watching Judge Judy for a week doesn't make you a lawyer. And when hiring, be careful because lots of people who claim to be experts are far from it.

That’s valid, but he’s implying that Judge Judy should be taken off air because some people watch it and then pretend to be lawyers. Squelching information seems like a terrible way to eliminate a few impostors downstream.

Yes, but that's a bad example because pretending to be a lawyer is hard. A better example would be gurus spreading nutrition recommendations that are not wrong per-se, but extremely simplified. Nutrition is a complex topic and individual differences make it hard to generalize. Let's say the information are so simplified that they are likely to hurt people who blindly follow them without doing further research. So, should this information be taken off air or not? I would say yes, and perhaps you would say no. In either case, I don't think the answer is quite as clear-cut.

Re: TensorFlow, Keras and deep learning, without a PhD

#154

Post by lc0_stein » Mon, Jul 20, 2020, 3:15 AM UTC

LorenzGauge wrote: post #44

lumberjack wrote: post #39

Since everyone is talking about hype in ML, I wish there was some hype for good ole' conversional scientific computing. Yes, it's not so sexy, you have to build your own model yourself, and then the hard work is in finding and verifying a suitable numerical method and finally devising a solid implementation. It requires a vast number of different skills, anything from pure math to low level programming and it is defi…

So excited to start my Masters in Numerical/Scientific Computing for these reasons!

What are some good universities for something like this?

Re: TensorFlow, Keras and deep learning, without a PhD

#155

Post by TrackerFF » Mon, Jul 20, 2020, 5:19 AM UTC

mlthoughts2018 wrote: post #51

TrackerFF wrote: post #45

Earlier quoted context omitted.

But then again, a lot of current ML jobs are basically just that - finding some optimal architecture, tune hyperparameters, and bam! You're now a modern "AI" powered company. Heck, I've encountered plenty of ML jobs that didn't require anything more than familiarity with some known frameworks or libraries, and being able to apply known methods to real-world data / problems. So I can absolutely understand why people a…

This is really a myth. Most ML jobs require very detailed understanding of statistics because the devil is in the details. You need to understand things like multicollinearity, coding biases, missing data techniques, convergence of Markov chains, learning curves, mechanics of various higher order gradient optimization methods, how to really carefully evaluate goodness of fit in a huge range of categories of models (n…

I can just tell from my own experience, having interviewed and researched a ton of ML jobs: The majority of ML jobs today seem to be re-branded analytics jobs.

I'd say that a solid 4 in 5 of the jobs I've interviewed for, which were tagged in the ML domain, were just that. Typical [x] analytics jobs which don't really require more than stats 101, and good handling of excel. Basic scripting knowledge were often in the nice-to-know section.

Now, there might be a world difference in the typical ML jobs you see in startup hubs like SF, and the jobs you see elsewhere - but companies are, and have been for almost 10 years, been desperate to get onboard of the hype-train, and have re-banded a lot of jobs to attract those wanting to work with ML or Data Science.

Re: TensorFlow, Keras and deep learning, without a PhD

#156

Post by Breza » Mon, Jul 27, 2020, 3:19 AM UTC

godelski wrote: post #110

gas9S9zw3P9c wrote: post #92

Earlier quoted context omitted.

Thanks for sharing your learning path and congrats to your success. There are so many great resources out there that it's possible for anyone to become an expert. Unfortunately, people like you who are willing to put in the hard work seem to be the minority. All of your success is well-deserved and props to you. Just like you said, most gravitate towards the easy-to-understand videos and blogs instead of confronting…

> I wonder if your success also had something to do with the fact that you had a specific problem you were trying to solve. As a side note, and I've found that this is one of the best ways to learn. Find a hard but obtainable problem and work towards it gathering all the knowledge you need along the way. What works for me is breaking down a project into a bunch of mini projects and so it becomes a lot easier to track…

I completely agree with this. In undergrad, I majored in non-profit management. Every single course in major had a field work requirement. Grant writing class required us to work with an area charity and write them a grant application, which our professor graded as one of our assignments. Same thing for program evaluation class and the rest. In addition to learning the topics, I learned so much about how to work with real world teams.