211–220 of 233 posts
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I think about this constantly. Not to sound like I walked uphill in both directions back in my day or something, but I remember building models in numpy without pandas. It was tedious -- and that's just a nice API wrapping ndarrays!
> Not to sound like I walked uphill in both directions back in my day Local minima?
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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 completed the andrew ng course recently, and felt that the difficulty dropped a lot in the second half of the course (for example, he stops giving homeworks). im hoping for more in his new DL courses
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my only goal in mentioning the statistics sequence at all was to give a familiar example where the standard sequences vary in depth depending on audience. a trivial point, yes, but i wanted to be concrete because it's the internet. apparently that was a terrible choice, as it was far too close to the topic at hand; my apologies for making you search so hard for a connection. i made my request because it's jarring for…
You do understand that for all the talk and new terminology and claims of "learning" in the "machine learning" (ML) in the Google OP, what is in the OP is a poor introduction to some highly polished material in "regression analysis" in 50 year old books. So, the ML stuff is adulterated old wine in new bottles with new labels. That is essentially intellectual theft and corruption, and without references essentially ac…
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> 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 industry. It is absolutely true that you do not need a graduate degree to apply AI/ML to vanilla problems. It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases…
> It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases where AI/ML fails on a deceptively-simple problem, or to tweak an AI/ML algorithm (or develop a new one) so it can solve a novel problem. How much of that is critical domain specific knowledge and how much of that is just general engineering debugging/problem solving experienc…
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Yeah, I expected this reply. The PhD is sufficient but not necessary here, right? A PhD researcher's job description is basically "learn necessary math, become a domain expert, and publish papers advancing that domain." It's difficult (but possible) to gain the same experience in industry if you don't have a graduate degree. Which company would pay you to work through Bishop or Goodfellow for a few months? Even a pri…
You can spend many months working through papers and books without a company paying you for that. That's something that I continually do and have always done, in my own time (and many different fields). Sufficient and not necessary indeed.
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Thanks for taking the time to write such a comprehensive reply - much appreciated. "ML is maths" is something that I'm getting used to now. I do have some real uses in mind for what I'm learning' both in my job and some side projects, particularly image feature recognition, and I'm looking forward so seeing how it all works in out. Thanks again!
Image feature recognition is not quite solved but I feel it's very close. It's easier, obviously, if the problem domain is very specific. In the past, like when I started on ML, the best tip was to make sure to do some edge detection with a few convolutions before feeding an image to a neural network. Now, we have convolutional neural networks that kinda do that for you automatically. Sometime in between those two da…
If you look at capsules papers, you will realize that convnets are not very good at recognizing transformations (e.g. 3d rotations) of the same object. That's probably why so many training examples are required to make them work well.
Also, if you look at errors made by state of the art models, some of them are obvious (to a human) objects, classified as something entirely different and unrelated. Which leads me to believe that object recognition is not completely solved until a model has some kind of common sense, either build in, or acquired during training.
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We have to separate AI researcher and implementation engineer. These types of crash courses help get you to the point where you can reasonably work under PhD level people and write code to test, scale, and deploy their ideas. For many current applications of ML this is acceptable because you're just stealing an idea from a paper or stealing ImageNet to recognize your problem. For anything else you really need to pay…
Will we reach a state where ML is as accessible for implementers as SQL databases? I still remember the time when databases were only for experts.
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> 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 industry. It is absolutely true that you do not need a graduate degree to apply AI/ML to vanilla problems. It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases…
> It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases where AI/ML fails on a deceptively-simple problem, or to tweak an AI/ML algorithm (or develop a new one) so it can solve a novel problem. How much of that is critical domain specific knowledge and how much of that is just general engineering debugging/problem solving experienc…
It's both, right? You pick up problem-solving techniques as a researcher or engineer; as the former, those techniques lean towards scientific problems. Your average engineer doesn't need to know about contrasting.
Again: it's possible to learn the necessary math in your spare time! I agree!! However, it's far easier to do it in a graduate program as a full-time job for 2-5+ years.