121–130 of 156 posts
Mandatory plug: do consider using PyTorch instead. It's far easier to pick up and work with. Easy things are easy, hard things are possible.
Does it train just as fast on gpus?
I am constantly puzzled by people saying that AI is overhyped and fresh grads won't have enough jobs for them. Almost every real life industry: retail, logistics, construction, farming, heavy industries, mining, medicine have just recently started to try AI. The amount of manual and suboptimal tasks that have to be automated and optimized is enormous. I am pretty sure there is more the enough work for applied DSs wit…
That being said, the business / marketing use of terms like "AI" is way out of control.
Earlier quoted context omitted.
there are many papers that seem like obvious crap that get into supposedly prestigious conferences (NeurIPS, ICML) every year. I think many people might reasonably believe that a paper in the supposed best venue in the field will be worth reading, but that is not really true. compared to machine learning, other research communities in computer science have much less nonsense. you can really feel the difference in qua…
Link to at least one “obvious crap” paper that got accepted to NeurIPS or ICML?
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'm Data Scientist and the hype is making me really jaded about the whole thing. I work in a customer facing role and it's just painful to see how many sensible and solvable problems with ML/Deep Learning get buried and destroyed under the pile of hype of solving unrealistic "sexy" problems with "AI." It was okay when Machine Learning was the hot thing, it's been awful since "AI" became the hot thing.
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…
Mandatory plug: do consider using PyTorch instead. It's far easier to pick up and work with. Easy things are easy, hard things are possible.
I read somewhere saying TF2 is good for production while Pytorch is good for research(and papers), is this true? I'm more interested in putting one of them into real products, esp standalone embedded devices.
1. PyTorch C++ API which can trivially load and execute your models exported via JIT
2. ONNX export and inference in TensorRT (highest performance inference option)
3. Or just deploy straight up PyTorch Python code - it'll run fine in "production".
One place where PyTorch is weaker than TF is mobile. TFLite is a lot more mature and has all sorts of acceleration support (GPU, DSP). So if that's what you need, at this point there's really no other good choice IMO.
Earlier quoted context omitted.
Instead of saying anti-correlated is better to say "inversely correlated" (or if you mean lack of correlation then "uncorrelated")
You mean "negatively correlated"?
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…
Earlier quoted context omitted.
> 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.
Right, I’ve been reading about machine learning for about 5 years, have read hundreds of articles about different techniques, and have often tried to explore ways it could be used. However, I’ve never found a practical use in software engineering. Every time I think I discover something that could use machine learning, I usually don’t have any data to work with or don’t have a clear definition of what the inputs and…
For example, in my PhD thesis project, I am making a series of deep learning models to make a cardiac MRI autopilot. I've build a series of deep learning networks to localize the cardiac landmarks that define the cardiac imaging planes. And our group has even made it into a clinical prototype that works within our imaging workflow.
I think the field is shifting in that ML technologies are increasingly requiring domain level knowledge in order to make a practical endpoint.
I am constantly puzzled by people saying that AI is overhyped and fresh grads won't have enough jobs for them. Almost every real life industry: retail, logistics, construction, farming, heavy industries, mining, medicine have just recently started to try AI. The amount of manual and suboptimal tasks that have to be automated and optimized is enormous. I am pretty sure there is more the enough work for applied DSs wit…