51–60 of 100 posts
I don't want to diminish the success that this company is having being run in their own way, but give me a break with these blog posts. VC is a "trend to avoid"? Avoid "Hypergrowth"? I guess companies like Google, Facebook, Twitter, Amazon, Stripe, Airbnb, Dropbox, Pinterest and a hundred others all really messed up. I mean I could go point by point and give examples that are the opposite of these trends, but that is…
I don't want to diminish the success that this company is having being run in their own way, but give me a break with these blog posts. VC is a "trend to avoid"? Avoid "Hypergrowth"? I guess companies like Google, Facebook, Twitter, Amazon, Stripe, Airbnb, Dropbox, Pinterest and a hundred others all really messed up. I mean I could go point by point and give examples that are the opposite of these trends, but that is…
I'm not going to say that VC isn't ever or usually the right move, but this comment is the height of survivorship bias. Not every business idea should be a VC-funded startup and trying to shoehorn every team and every business model into that path is a mistake. Whether to take VC capital is situational and depends on the team, market, product idea and all other factors of that specific startup. The businesses you listed were all the proverbial winning lottery tickets. There's plenty of losing lottery tickets too, and many of those had great ideas that would have been better served with a bootstrapped model that looked for slower, more sustainable growth and increased founder control.
A great startup is like a great food recipe. You have to mix the right ingredients in the right amounts to make it taste good. Almost every ingredient works in some dish, but many ingredients don't work in combination. Dishes can be appetizers, mains or desserts, and you'll want to add different ingredients depending on which one you're making. VC is like sugar. It can make your startup fat in a hurry, but if you're growing very quickly, all those calories can keep you from starving. It tastes great in almost anything, but it's put into way to much of what we eat today and causes a lot of premature deaths.
The catchiest (and wrong) assertion is this: "even for highly technical aspects like deep learning, fast.ai has shown that people with 1-year of coding experience can become world-class deep learning practitioners" Yes, any Joe can train deep network with dozen lines of Keras. Sure, your startup can use off-the self models and tweak it a bit... That doesn't make you "world-class deep learning practitioner". If you ar…
I thought the definition of 'startup' is a company aiming for massive growth. If not you are a regular business.
So maybe the message is "You don't have to be a startup"
The catchiest (and wrong) assertion is this: "even for highly technical aspects like deep learning, fast.ai has shown that people with 1-year of coding experience can become world-class deep learning practitioners" Yes, any Joe can train deep network with dozen lines of Keras. Sure, your startup can use off-the self models and tweak it a bit... That doesn't make you "world-class deep learning practitioner". If you ar…
Still, it's nothing unlearnable. You read a couple good books and blogs, do some experiments, and you're about 80% of the way there.
I don't want to diminish the success that this company is having being run in their own way, but give me a break with these blog posts. VC is a "trend to avoid"? Avoid "Hypergrowth"? I guess companies like Google, Facebook, Twitter, Amazon, Stripe, Airbnb, Dropbox, Pinterest and a hundred others all really messed up. I mean I could go point by point and give examples that are the opposite of these trends, but that is…
Counterpoint: Blog posts like this one are useful, because the VC-funded companies you named are so successful at dominating our thoughts, conversations, and news feeds (HN included) that many founders never see any examples of people following an alternative path. It can be inspiring to a lot of people to simply hear about the possibility of building a small, revenue-generating business or side project. I've been ru…
The catchiest (and wrong) assertion is this: "even for highly technical aspects like deep learning, fast.ai has shown that people with 1-year of coding experience can become world-class deep learning practitioners" Yes, any Joe can train deep network with dozen lines of Keras. Sure, your startup can use off-the self models and tweak it a bit... That doesn't make you "world-class deep learning practitioner". If you ar…
Reading HN and following key people gives you the impression that this is an incredibly popular field - it is. In reality, though, it's a small field and lots of companies still don't use it, intend to use it, or have any idea how to use it. Alexnet was only 5 years ago. Faster-RCNN was only 2 years ago. Compared to, say, all programmers, statistically nobody is doing this.
Debugging a model is hard, but it's unlikely your efforts will be significantly more or less productive than a leading researcher aside from obvious things like noticing that the learning rate is too high or low. There's so much hand waving, even in top papers, that mostly we've tried stuff that looked like a good idea, and it turned out to be correct.
The thing is that a lot deep learning tasks don't require a fancy model. You can take VGG/Inception/ResNet and bodge them to fit a lot of real-world scenarios. Then the problems are mostly solved by standard machine learning intuition - making sure you have an appropriate train/test/validation set, a good loss function, etc.
Edit -
Here's an example: Halcon, a popular commercial image processing library has recently included a deep learning package due to repeated customer demand. It's not out yet, but it's most likely just going to be a wrapper for train/test using a basic image classification or object detection network. That's what the market wants. They don't care about GANs, they want something which will tell them if the image represents a valid product during QA 99% of the time (and often QA failures are pretty obvious - e.g. big crack in a bottle, or a deformed label).
You could build that kind of model with very little expertise and you'd beat most of the available image processing packages easily, with practically no work. The point is that an off-the-shelf model trained on company-specific data is likely to be state of the art on that problem (because who else has the dataset?) You don't care about 1000 classes in Imagenet, you want good product/bad product.
I don't want to diminish the success that this company is having being run in their own way, but give me a break with these blog posts. VC is a "trend to avoid"? Avoid "Hypergrowth"? I guess companies like Google, Facebook, Twitter, Amazon, Stripe, Airbnb, Dropbox, Pinterest and a hundred others all really messed up. I mean I could go point by point and give examples that are the opposite of these trends, but that is…
I don't want to diminish the success that this company is having being run in their own way, but give me a break with these blog posts. VC is a "trend to avoid"? Avoid "Hypergrowth"? I guess companies like Google, Facebook, Twitter, Amazon, Stripe, Airbnb, Dropbox, Pinterest and a hundred others all really messed up. I mean I could go point by point and give examples that are the opposite of these trends, but that is…
For founders and employees, you can't work for 50 different high risk, high return companies at the same time, so that might not be what they want.