Multi Armed Bandits - Reinforcement Learning Explained!
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- čas přidán 27. 08. 2024
- Let's talk about a reinforcement learning strategy called Multi Armed Bandits to k-armed bandits.
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Imagine you have a bunch of colorful candy machines lined up in front of you, and you want to figure out which one gives you the most candy when you put in a coin. Each candy machine is like an "arm" of a bandit.
Now, you don't know which candy machine is the best because they all look pretty cool. You only have a limited number of coins to use, and you want to get as many candies as possible. This is where the problem comes in.
The multi-arm bandit problem is like a game where you have to decide which candy machine to use for each coin you have. You want to try different machines to find out which one gives you the most candy, but you also don't want to waste too many coins on the bad machines.
So, it's a bit of a tricky puzzle. You need to decide when to stick with a machine that has given you lots of candy in the past and when to try a new one in the hope of getting even more candy. It's all about making smart choices to get the most candy with your limited coins!
Sincerely,
ChatGPT
This is one of the best explanations on CZcams. Better than the whiteboarding BS that introduces unnecessary complexities.
First comment 😇
Thank you, sir, for sharing valuable information through your CZcams channel. Once again, I have a request: please create a series on how to apply Transformers to time series tasks such as anomaly detection, forecasting, or classification. Working on just one of these tasks would be sufficient for us. I have followed numerous articles, short notes, and videos regarding the application of Transformers to time series data, but it is still not clear to me. I am a beginner on this Transformer journey, and there are no useful videos available on CZcams overall.
Would there be any benefit in using a more advanced Q-learning algorithm like DQN or even D3QN?
Hey bro your really awesome, you are giving such a high quality content and I want some help from, I have some question about nlp career, could you please clear that...
I am so much obsessed with NLP, so I learned lost stuffs like - GRU, LSTM, Attention, Transformers, I want to go very deeper, could you please tell me what are the advanced and challenge stuff should I learn in order to become expert in the NLP, can you give me a NLP expert road map
(1) get interested (2) Read research (3) start doing projects end to end. I think you have done 1 and some of 2 for now. So I would recommend focus on a project. You can always keep reading the latest and greatest in NLP but the fundamentals don’t change much. I would start by taking a problem that really interests you In NLP, then do your research to see the tools to accomplish this. And then gather data and code out your solution. In this process, you will be surprised of the struggles and how much you learn. Good luck!
Thank you bro
@@CodeEmporium Other thing, I really want to understand these architecture very well, because many of the people today just use some code from the internet and watch some tutorials and then they create their models,but they don't know about what they are really doing, but I really want to understand them like how they really predict the next word of the Sentence, yh I know you have the lots of video based on that 😅, but I want to go even deeper, could you give me any information about how to master these model - transformer - in level of knowing alphabet
Can you recommend any resources , I taken andrew ng spec, research paper are little bit tuff to read, is there any resources available that goes very in depth in NLP or language models
RL is just first person real life
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