This video is amazing. You explained everything in such a simple manner. I am feeling really motivated to learn more about reinforcement learning and neural networks after watching this.
What a great series! I have a question for the experts... was it necessary to map velocity as an input? I'm guessing it's not absolutely necessary and was done to make the training faster? My guess is based on the assumption that the timing of the ball x/y changes to the inputs have an effect, but I may be wrong.
Can you playlist each one of your topics plz? I wanted to post on Twitter(X) your video topics but could only post a single video at a time. Great content by the way. Ty very much. Your perspective on some topics helped me a lot to get a more intuitive understanding.
Good idea! Here's one on generative AI: ua-cam.com/play/PLWfDJ5nla8UoR8P7AGqVw7ZPjXajUFLMo.html Here's one on reinforcement learning ua-cam.com/play/PLWfDJ5nla8UoexEaLqVMw7q3Ft0vRYscL.html Here's one on LLMs + text-to-image ua-cam.com/play/PLWfDJ5nla8UoG2mvvHs_OS0asAKC5HJeu.html
I get how the model can see moves and output up or down action. But I don't get how model tracks the score for rewards etc Can someone explain how the reward is fed into model
![The moment we stopped understanding AI [AlexNet]](http://i.ytimg.com/vi/UZDiGooFs54/mqdefault.jpg)
this is video is super underrated. In fact the whole channel is underrated.
Maybe i should follow the channel then 😅.
This was my first vid, and the explanation was really well simplified
Your Channel IS SO GREAT, I share with all my eng friends for you to get more visibility!
This video is amazing. You explained everything in such a simple manner. I am feeling really motivated to learn more about reinforcement learning and neural networks after watching this.
I don't know how I stumbled upon this video but that was very interesting and intuitive to understand. Thank you.
Very very underrated channel
Underrated, two Rs
@@benc7910 thank ya sir
Too beautiful you can watch this kind of videos all the day without get bored
Can we have the code for this
Lol😅😅😅😅😅😅
agi: 1. ai develops understanding of win-loss conditions and sets policy params (inputs & actions) accordingly. 2. ai creates (= designs & builds) training env(s). 3. ai iterates, avals & adjusts policy parameters accordingly 4. done (or validation run(s) w/ human(s))
This is really awsome! It's the best video that explains DRL in such an easy to understand way!
This is super underrated video
Your videos are great. Looking forward to more!
This was so surprisingly great :3
I really like the way you visualize what you are talking about. Thank you for putting in the effort!
I agree once you see how it all works it seems like 1s and zeros give me some feed back on r/grand unified theory or cosmo knowledge
Great video, very helpful, easy to understand.
Excellent. Congratulations ❤
Thanks a lot for this one! 😊
Excellent content!
What a great series! I have a question for the experts... was it necessary to map velocity as an input? I'm guessing it's not absolutely necessary and was done to make the training faster? My guess is based on the assumption that the timing of the ball x/y changes to the inputs have an effect, but I may be wrong.
Amazing video as always :)!
Can you playlist each one of your topics plz?
I wanted to post on Twitter(X) your video topics but could only post a single video at a time.
Great content by the way. Ty very much.
Your perspective on some topics helped me a lot to get a more intuitive understanding.
Good idea! Here's one on generative AI:
ua-cam.com/play/PLWfDJ5nla8UoR8P7AGqVw7ZPjXajUFLMo.html
Here's one on reinforcement learning
ua-cam.com/play/PLWfDJ5nla8UoexEaLqVMw7q3Ft0vRYscL.html
Here's one on LLMs + text-to-image
ua-cam.com/play/PLWfDJ5nla8UoG2mvvHs_OS0asAKC5HJeu.html
@@g5min Ty!
Superb
Excellent
Super helpful! Thank you 🙏🏽
Thank you!
I get how the model can see moves and output up or down action. But I don't get how model tracks the score for rewards etc
Can someone explain how the reward is fed into model
What is your reward function for the pong game? I did a similar pong game and I couldn't get it to learn.
thank you for this!
Thank you!!!
how many layers should such network have
Brilliant
but by what number do you change the weights like you never told us
i just have a quastion, what is that thing ? 6:20 its like a worm ?
like. i didnt take it in my math class.... im 16 years btw
i mean the one u added
that was good
Simple Reinforcement learning is extremely dangerous in certain nonstationary environments 😅
whats the name of this video game ?
Pls o want the code plsss
Can you share the source code for this project
You can follow the link to the Karpathy site at the end of the video, repeated here:
karpathy.github.io/2016/05/31/rl/
Imagine using reinforcement learning in quantitative finance 😊
ah yes, reinforcement learning. a fundamental computer graphics technology
I think that character/game-AI is pretty central to graphics
Why so negative?
@@g5minespecially AI image generation or processing nowadays