What kind of computing resources do you have?

This has terrifying implications for the future of humanity.

> Once upon a time in the Mushroom Kingdom, Sonic the Hedgehog and Mario had a wild night of partying. Sonic, in a drunken stupor, stumbled into Princess Peach's castle and stumbled upon her sleeping in her bed. Sonic, being the mischievous hedgehog that he is, decided to have some fun and began tickling her feet.

> To his surprise, Princess Peach woke up and instead of being angry, she found Sonic's antics amusing. The two of them laughed and played together for hours, eventually forming a close bond.

> Sonic, feeling bold, decided to confess his love for Princess Peach. But to his dismay, she rejected him and explained that she was already in a committed relationship with Mario.

> Heartbroken, Sonic retreated to his home in Green Hill Zone. He spent his days moping and feeling sorry for himself, until one day a portal opened up and whisked him away to another dimension.

> In this new world, Sonic discovered that he had the ability to transform into a werehog. He used his new powers to fight against evil and protect the innocent, but he could never shake the feeling of unrequited love for Princess Peach.

> Sonic continued to live in this new world, never truly happy but always doing what he could to help others. The end.

Lmaooo

There comes a point where too much automation hurts businesses as the number of people who don’t have jobs don’t have money. As a consequence businesses cannot make money.

The concept that one can truly be independent is flawed. The well being of one depends on the other and businesses are not exempt from this. This does not mean they cannot exploit but there is a limit.

What I’m trying to say is just pick a field that you like and one that is in demand.

IMO you should ideally go into a field that has at least some job opportunities (if you want to be a “dog psychologist”, you need a backup plan with that small of a market!), but you should focus on fields you are good at and that captivate you.

You don’t want to make an economic blunder and pay for a massive tuition bill and find out there are no jobs in that field, but a talented, motivated, and ambitious landscaper is going to be happier and have more opportunities in landscaping than someone whose heart wasn’t in AI/ML, or whose heart was in it but they just aren’t good at it, and they pushed through to get a degree in it just because “that job market is strong and should continue to be”.

I review resumes and do interviews with AI/ML candidates for positions, and I don’t care what degree they have, or how desperate we are for talent, if it’s clear they aren’t that good or aren’t that into their field, no job.

the cringe manifold!

You don't build your own network. You define your problem, and choose the best network for that problem. For instance, is your problem a classification problem? Then find an off the shelf classifier to use.

you get what you ask for.

+1 for using a model template rather than experimenting/crafting things by hand for your problem. Many good general-purpose architectures for classification exist and in my experience they work very well. For the classification problem you describe you will probably be fine using one of the architectures mentioned on the Keras CV page (or the equivalent place in the timm/pytorch docs). Recommend starting from a pretrained model.

The approach I usually take to solving a CV problem is to survey what architectures are recommended for the problem in the abstract (e.g. classification, segmentation, pose estimation etc), try those, then make modifications using details from the specifics of the problem if necessary.

Tbh you might not even need a deep vision model for your problem.

TensorFlow-land has tf.keras.applications

No, you’d be surprised how capable models will be and how stubborn humans ate.

Start with a small model (maybe resnet18) and start experimenting. Often its more worth your time and effort to research tricks in how to train the model (e.g. learning rate schedulers) or inprove the data quality.

Seems like a good idea, keeps you on the cutting edge so you can adapt quickly to the changing market.

It's hard to predict exactly, and requires one of two things:

a) Full-time research on new methods

b) Be the one with the breakthroughs

​

B) is hard and A) nobody I know pays for.

We're confined to jobs related to research or applying said research.

Agreed and it also seems like having some kind of ownership in something AI related is a good idea.

Your career in ML would only be safe if you were at the top of your field. Everything else in ML is a form of software engineering to implement methods or build data pipelines. Those jobs are no more safe than any other software engineering job.

The only way to really future proof is to be an owner, own lots of revenue generating assets. When the AI comes you replace your workers with AI and you keep making money. Yes I know how that sounds… I don’t like it, but that’s what it looks like the future is going to be.

All I'm saying is that Eminem is known for rhyming words that traditionally are not perceived to be rhyming. If GPT-3 can do the same, it's Eminem-level capability of rhyming, no matter how Bill from Youtube pronounces things.

I don't think you would need such a big thing like VGG even (besides, it's inefficient compared to newer architectures). The idea mostly is to extract festures with conv layers and then have an mlp to classify stuff based on the features. An AlexNet like network is the way I always start, just with adapted layer sizes. If it's already enough then no need to slap ResNet on anything.

I don't think it makes sense these days to implement a CNN architecture from scratch for a standard problem (e.g., classification), except as a learning exercise. A common set of classification networks that I use as a go-to are the EfficientNet architectures. Usually I use the timm library (for PyTorch), and instantiating the model is just 1 line of code (see its docs). You can either load it in pretrained (from ImageNet) or randomly initialized, and further fine-tune yourself. EfficientNet has versions 0-7 that give increasing performance at the cost of computation/size. If you're in TensorFlow-land I'm sure there's something analogous. Both TF and PT have model zoos in official packages too. Like torchvision.models or whatever.

There's no such guide. Just trial and error friend.

> K80

Yes, I meant that I got K80 and I was doing some CNN/BERT etc. Just checked, K80 (single unit) has similar TFLOPs to GTX1060 3GB so with other overheads in cloud (slow CPU, drive storage etc), Colab could be slower anyway.

Now I have a PC with dual GPU setup (RTX3090 + RTX3060) and have access to GPU servers at Uni, so no more Colab :)

> have a 1650 which is no slouch but colab trained in 5s what took my GPU 10 minutes.

is that a laptop GPU ?

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3090 hands down. The VRAM benefit is invaluable. However, if you are getting a 3090 at nearly 1K USD price point, then I recommend checking out the 3090Ti. The founders edition for 3090Ti in stock on Nvidia’s official store for 1100 USD + Tax.

One more consideration, if you’re using Cuda check cuda version compatibility for the 4000 series, also check cudnn compatibility as well. Sometimes newest cards are more of a pain than simple incremental value.

I am not sure about this, even my GTX1060 3 GB was kinda fast than K80 on Google Colab. Also think about storage size/speed, internet upload speed, security/restrictions of data, 12 hour limitation etc.