71–80 of 100 posts
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…
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…
Yes, that guy can become a world class practitioner.
He was talked down to junior and paid as little as possible because that's what companies do.
Earlier quoted context omitted.
What are some examples of a trendy, complex web stack? Or, what are some examples of a non-trendy, non-complex web stack?
Adding pub-sub/queue/message broker in your app when a normal when a normal connection to SQL db from your web app is more than sufficient. There are a lot of companies that have kafka in their stack when they actually don't need it. This is one thing i have seen a lot. On front-end side adding redux when a normal react-app would be just fine.
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…
How rare is it to find a new problem domain these days? Most if not all problems tackled with deep learning have already been done in one form or the other. Deep learning is not yet much of an academic discipline, but accessible to anyone willing to put in the hours of study and practice.
Earlier quoted context omitted.
Adding pub-sub/queue/message broker in your app when a normal when a normal connection to SQL db from your web app is more than sufficient. There are a lot of companies that have kafka in their stack when they actually don't need it. This is one thing i have seen a lot. On front-end side adding redux when a normal react-app would be just fine.
What do you think draws people to these technologies?
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…
> 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'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 int…
Earlier quoted context omitted.
Nothing is unlearnable - it's never question of "if it can be done" but question of "how much effort is needed". Read few blogs, books and take Coursera classes etc are all good but the real thing is working on multiple real and new problems. Anyone who has read books/blogs etc, give them a new problem, for example, predicting pedestrian intent for crossing a road from set of video frames. See how far they get.
So are you saying there are no best practices or patterns that are discovered over time that allows newcomers to progress in the field faster than the early pioneers?
While I don't entirely agree with this assumption, if you are in business it's probably a "safer" assumption to make.
This is obvious, yet an easy mistake. Behind it is applying the same values to a business as to a PhD - not only in topic selection, but also in execution. It's related to the conundrum that solving a problem that real people have now does not always require a technically great soluton. Even a revolutionary business does not necessarily require a technically great solution.
Academic success requires academic values and skills; business success requires business values and skills.
Of course, if you want to create a technologically revolutionary business, you'll need both.