It's a shame that they don't include a chapter on ethics the way [Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD](https://course.fast.ai/Resources/book.html) does. I hope people read the etics chapter or use the ethics section of the [associated free course](https://course.fast.ai/) to fill in that oversight on the Amazon Engineers' part.
Also it's a bit annoying that they address the fact that other resources exist but vaguely handwaved over the difference between their resources and the existing ones with "the others are not as up to date and don't have as much detail".
FWIW, my recommendation with this sort of stuff is to build something alongside of the reading you are doing.
One of the things I’m exploring is building an OpenAI Gym-like project where we build neural nets to play various games. If that’s of interest to anyone, please post below!
Sort of, yes! Not "solved" in the academic sense of actually finding the mathematically correct/ideal way to play, but rather using neural nets to learn to play and beat humans.
I'm running through Andrew Ng's "Machine Learning Specialization" course on Coursera right now. I plan to do his "Deep Learning Specialization" immediately after.
There's not a lot of formulas and math so far, which concerns me because I eventually want to understand papers.
It's a shame that they don't include a chapter on ethics the way [Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD](https://course.fast.ai/Resources/book.html) does. I hope people read the etics chapter or use the ethics section of the [associated free course](https://course.fast.ai/) to fill in that oversight on the Amazon Engineers' part.
Also it's a bit annoying that they address the fact that other resources exist but vaguely handwaved over the difference between their resources and the existing ones with "the others are not as up to date and don't have as much detail".
Source: https://programming.dev/post/7017669
If folks are looking for free deep learning resources, here are a few more that I’ve personally vetted: https://phaseai.com/resources/free-resources-ai-ml-2024
FWIW, my recommendation with this sort of stuff is to build something alongside of the reading you are doing.
One of the things I’m exploring is building an OpenAI Gym-like project where we build neural nets to play various games. If that’s of interest to anyone, please post below!
Reading + building neural nets to play games? Yes! Count me interested.
haha yes, that's it. Shoot me a note at hello <at> phaseai <dot> com and I'd love to connect!
OpenAI Gym looks incredibly interesting. Aren't those games supposed to be solved using neural nets?
Sort of, yes! Not "solved" in the academic sense of actually finding the mathematically correct/ideal way to play, but rather using neural nets to learn to play and beat humans.
I'm running through Andrew Ng's "Machine Learning Specialization" course on Coursera right now. I plan to do his "Deep Learning Specialization" immediately after.
There's not a lot of formulas and math so far, which concerns me because I eventually want to understand papers.
Can anyone recommend similar content but for classical machine learning? Would you even consider it a prerequisite for understanding Deep Learning?
IMO, this is the best course to start with. It has everything you need from theory to pratical code. Very high quality stuff.
Oh, I'm interested!