Earlier quoted context omitted.
I can't believe how can someone so accomplished believe in God.
I think it's orthogonal. There are tons of smart people who believe in God. (Knuth has already been mentioned.) If God wanted, He could make himself apparent to everyone. Clearly that isn't the case; there is room to doubt or to believe no matter how smart or accomplished you are.
Tinygrad: A simple and powerful neural network framework
141–147 of 147 posts
Re: Tinygrad: A simple and powerful neural network framework
#142Earlier quoted context omitted.
I've been on a lot of ML teams and outside of Finance and a few other sensitive topics explainability has always been irrelevant.
What happens, when your model exhibits a discriminating bias? How do you find out, what is going wrong? Knowing, what the model pays attention to can be pretty helpful.
Re: Tinygrad: A simple and powerful neural network framework
#143“Almost 9k stars” is actually 7.3k stars… But otherwise very cool project :)
Re: Tinygrad: A simple and powerful neural network framework
#144Re: Tinygrad: A simple and powerful neural network framework
#145Earlier quoted context omitted.
When I was around 15 I used to do 10 hours of x86 assembly programming, and then several hours every day after school for a month or so in a row. Parents would have to force me from the computer. I attribute it to a younger brain, NO internet and NO fun distractions. At 42 I just don't see how I did it, and I know I could never be that focused. Just sitting still for 4 hours make me feel quasy now, and I need to use…
Yea I miss youth. I'm in my 30s and all nighters are not the same anymore :(. When I was young I'd do 2-3 in a week and with a four hour nap I would recover. Now after those I lay down and I can't get up for a couple hours with all this aching in my limbs lol.
Re: Tinygrad: A simple and powerful neural network framework
#146Re: Tinygrad: A simple and powerful neural network framework
#147Earlier quoted context omitted.
I've been on a lot of ML teams and outside of Finance and a few other sensitive topics explainability has always been irrelevant.
What happens, when your model exhibits a discriminating bias? How do you find out, what is going wrong? Knowing, what the model pays attention to can be pretty helpful.