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TensorFlow, Keras and deep learning, without a PhD

codelabs.developers.google.com

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

#41

Post by MrBuddyCasino »

gas9S9zw3P9c wrote: post #37
s1t5 wrote: post #34

Earlier quoted context omitted.

Do you ever feel like all the noise influences the way you think about your own career? I work as a data scientist and sometimes find the hype so off-putting that I think that I should look for a role that's related to solving some optimization problems outside of ML, or a software engineering role in some completely different domain and work as a backend developer or something similar.

Definitely. I came from a software engineering background into AI 6+ years ago, and I was lucky to be there at the right time. From purely a career perspective, leaving aside that this may be your life's passion, I believe that right now is the worst time to get started in AI. These is so much noise and competition but very few actual jobs or value created. It's just a research PR machine. Just as with Data Science,…

> Good backend/infrastructure/data engineers are so much harder to find these days, and these skills IMO provide much more value than doing some kind of modeling.

After all these years of FOMO on AI, this is music to my ears.

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

#42

Post by robpal »

I attended a conference talk of a FB AI-engineer talking about her paper with backprop equations so obviously wrong my eyes hurt, and incorrect definitions of objects. It did not stop her from participating (btw. this is always unclear -- who did what) in state-of-the art research in object detection.

PhD is overrated in the deep learning context. It is more about forging the intellectual resilience and ability to pursue ideas for months/years than learning useful things/tricks/theorems.

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

#43

Post by secondcoming »

It's a pity that most Tensorflow tutorials out there seem to deal with images. We tried to use it for real-time data classification (data -> [yes | no]). Every tuturial out there seems to assume you're using Python (which is probably not an invalid assumption). Here's my 2c when trying to use Tensorflow with C++:

a) Loading SavedModels is a pain. I has to trawl the Tensorflow repo and Python wrappers to see how it worked.

b) It's incredibly slow. It added ~250ms to our latency. We had to drop it.

c) It has a C++ framework that doesn't work out-of-the-box, you have to use the C lib that wraps an old version of the C++ framework (confused? me too).

d) It's locked to C++03.

Tensorflow-Lite looked to fit the bill for us, but our model weren't convertible to it. We no longer use Tensorflow.

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

#44

Post by LorenzGauge »

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!

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

#45

Post by TrackerFF »

gas9S9zw3P9c wrote: post #31

As a researcher in the field I am not quite sure how I feel about these kind of resources. I am all for making research accessible to a wider audience and I believe that you don't need a PhD, or any degree, to do meaningful work. At the same time, the low barrier of entry and hype has resulted in a huge amount of people downloading Keras, copying a bunch of code, tuning a few parameters, and then putting their result…

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 are copy/pasting tutorials or papers, and just doing some slight changes. You're practically miles ahead of the competition, when it comes to the job search.

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

#46

Post by bumby »

s1t5 wrote: post #34
gas9S9zw3P9c wrote: post #31

As a researcher in the field I am not quite sure how I feel about these kind of resources. I am all for making research accessible to a wider audience and I believe that you don't need a PhD, or any degree, to do meaningful work. At the same time, the low barrier of entry and hype has resulted in a huge amount of people downloading Keras, copying a bunch of code, tuning a few parameters, and then putting their result…

Do you ever feel like all the noise influences the way you think about your own career? I work as a data scientist and sometimes find the hype so off-putting that I think that I should look for a role that's related to solving some optimization problems outside of ML, or a software engineering role in some completely different domain and work as a backend developer or something similar.

I’ve found that non-technical management expectations are just too swayed by the AI hype machine.

I’ve also found operations research field to be more grounded and still have plenty of research opportunities. There’s something very fulfilling about optimizing a real-world system, particularly when the answer isn’t intuitive.

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

#47

Post by pjmlp »

chongli wrote: post #36
gas9S9zw3P9c wrote: post #31

As a researcher in the field I am not quite sure how I feel about these kind of resources. I am all for making research accessible to a wider audience and I believe that you don't need a PhD, or any degree, to do meaningful work. At the same time, the low barrier of entry and hype has resulted in a huge amount of people downloading Keras, copying a bunch of code, tuning a few parameters, and then putting their result…

put AI research on their resume. This has resulted in so much noise and low quality work that it really hurts the field. That’s one way of looking at it. Perhaps another way is ask why all of these employers are hiring people who have done low quality research without understanding who they’re really hiring. It reminds me of the adverse market effects on gamers when Bitcoin miners were buying up all the GPUs a few ye…

I have seen RFPs that ask for machine learning based solutions without having any clue how they are at all applicable for their business, just because they wanted to eventually have some headlines with the updated software.

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

#48

Post by disgruntledphd2 »

lordthirdeye wrote: post #28
mpfundstein wrote: post #5

Earlier quoted context omitted.

i am doing quite some deep learning work as part of my consultancy practice. And its all hand made stuff , together with lots of trial and error while trying to replicate papers that might be relevant fkr the task at hand. so i totally agree with your statement. big corps overhype the shit out of it in order to sell and lots of n00bs fail for it. regarding your last points. even if its super easy nowadays to deploy Y…

if you don't mind me asking, what do you mean by "hand made". I'm currently wworking on a information science degree and I'm trying to focus on machine learning and data science. Could you go into a little more detail on what gives something business value?

Not the OP, but ultimately something that increases revenue or decreases costs by some measurable amount.

The best thing you can do to make yourself good at this is to practice. Get some Kaggle data and try to fit a model. Realise your data is crap, clean data, repeat.

Every useful system is hand made in the sense that there's a vast amount of set up and operational code. Mostly the model's the easy part (although it will take so much time to run).

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

#49

Post by mpfundstein »

lordthirdeye wrote: post #28
mpfundstein wrote: post #5

Earlier quoted context omitted.

i am doing quite some deep learning work as part of my consultancy practice. And its all hand made stuff , together with lots of trial and error while trying to replicate papers that might be relevant fkr the task at hand. so i totally agree with your statement. big corps overhype the shit out of it in order to sell and lots of n00bs fail for it. regarding your last points. even if its super easy nowadays to deploy Y…

if you don't mind me asking, what do you mean by "hand made". I'm currently wworking on a information science degree and I'm trying to focus on machine learning and data science. Could you go into a little more detail on what gives something business value?

hand-make as in:

- looking very carefully into the very specific challenge of your client

- figuring out how (and if) ML can help

- figuring if its still economically feasible (costs of research vs perceived(!) benefit)

- deriving a solution.

- tinkering tinkering tinkering. usually more with the data than with the models :-)

All my A.I. projects are essentially outsourced R&D projects where we deliver the brain and computing power. So far, it never was as easy like installing YOLO or any other off the shelf product.

Edit: You also need very often custom software to create custom datasets. AI models are often only tested on academic datasets but I observed empirically that their performance transfers badly to real world datasets. So you need to create your own datasets etc. This is often a non-trivial problem. So I wrote a lot of dataset creation tools in my AI practice.

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

#50

Post by mattigames »

wirrbel wrote: post #7

The whole AI/ML stuff has become so hyped up that its probably time to find another topic of interest in software engineering for me. Its a weird melange nowadays where frameworks and "academic credentials" are fused together by major tech companies and leaves me - who has deployed a dozen of classical ML models into production that are still running after couple of years - wondering what this is all about. Overall,…

Instead of saying anti-correlated is better to say "inversely correlated" (or if you mean lack of correlation then "uncorrelated")
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