Restricted Boltzmann Machines (RBM) - A friendly introduction
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- เผยแพร่เมื่อ 27 พ.ค. 2024
- CORRECTION: The score for BE is 6 and for BD is -1.
A simple introduction to Restricted Boltzmann Machines (RBM) and their training process, using a real-life example with people and pets.
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Introduction: (0:00)
Mystery: (0:17)
Scores: (4:39)
Probabilities: (7:30)
Training (11:09)
Contrastive Divergence: (13:37)
Small Problem: (15:33)
Gibbs Sampling: (16:33)
Updating Weights: (20:56)
Sampling Problems: (22:58)
Independent Sampling: (24:27)
Picking Random Samples with Conditions: (28:30)
Picking Completely Random Samples: (31:05)
Summary: (35:03)
Conclusion: (35:57) - วิทยาศาสตร์และเทคโนโลยี
Learning with examples first is always better than starting with math, Now when I read the math behind RBM it makes more sense since I have something to relate too!
Thank you for this wonderful presentation :)
Exactly, you are right
this is the only ML tutorial i’ve seen that makes everything so clear. cannot emphasize the importance of this as a beginner, thank you so much!!
This is the best tutorial that I came across from introduction to RBM. Looking forward to more such AI tutorials from you.
I just have some doubt about some possible scenarios that you explained at the beginning of the video. For example, in your explanations, you explained that the score for the scenario BD is equal to -1. But, then in the table, we see that the score for this scenario is not -1, instead, it is -2. And this inconsistency happens for some other scenarios as well. Thank you very much for your clear explanations.
Chapeau to how much effort you put into this tutorial. ML-Grads like me are dependent on such videos. Love from Germany!
Your tutorials are addictive. So clearly explained, that you think of making use of it in real world !!
This video is a game-changer, I had no idea how RBM's worked before. Thanks!
I love your videos and methods of teaching relatively difficult stuff in easy way. Keep up the great job!
Great Work! You're video is the first one I've watched to properly explain RBM to people who dont really know much about math.
Just want to leave a comment so that more people could learn from your amazing videos! Many thanks for the wonderful and fun creation!!!
you explain concepts so eloquently -- thank you for these explanations
Explanation with visual representation, the best tutorial I have ever seen. RBM is very well explained !!
Explained very well with practical example. Now I understand how Gibbs sampling is used in RBM. Thank you very much.
This clip clarified a lot about RBMs for me. Perfect combination of simple examples and the math behind it which makes it easy to understand. I don't get why literature on this or similar topics aimed to teach has to only focus on the math part/formulas which is much more time consuming to understand and in the end doesn't provide as good intuition as your approach of teaching a subject. Thanks for the video!
Nice. Topic I like most (in ai) posted on my birthday. I have been following you for a some months, good content, keep up the good work!
I read RBM's wikipedia page twice but was still confused. The video clarifies everything. Thanks!
Whoa....I was warned these were super hard to understand but that was so well explained. You are amazing
Amazing with the mathematical meaning behind it. Thank you so much❤
wow this was one of the best tutorials i've seen about RBM
high quality teaching... way such a complex math was expalined is amazing...
First experience researching RBMs. I can say, my mind is throughly blown 🤯
Thanks for this beautiful presentation on RBM now it will be easy to digest the paper 🙏
GREAYYYYTTTT!!!! WORK WOW dude this is the best explanation i have come across
Thank you so much for this video, it was very easy to understand!
I am just amazed by seeing this tutorial
Thanks a lot for this video. The example is great to follow!
This is such a good explanation, and so creative! Thank you!
Thanks Mia!!! :)
This is a suberb explanation, thank u so much. Please keep the videos coming
enjoyed learning RBM (excellent work)
Please never stop making these videos!
Thank you so much for your kind contribution!! Definitely, I’ll continue making videos, if you have suggestions for topics, please let me know. Cheers!
thank you for saving my life
Wow, this is really intuitive, thanks you!
Thank you for the wonderful explanation...
the best tutorial on this topic.
Thank you for your video! It is really helpful:)
Nice video, very easy to follow through
Hi and thanks a lot for this video! It is an excellent source to get into RBMs. I have a question regarding the energy function, though. I always see this particualr form of the energy and never a derivation or an argument for it. Is it the only possible form? Or can one come up with any sort of functions, as long as the sum over all possible configurations doesn't lead to zero ( so that we can still divide by the partition function )?
Thank you for your great video, but how did the correlation between D and AC, E and B, come about? Why does this specific correlation form?
Excellent explanation, thank you a lot
Thank you for the clear explanation, the only thing that could be better is the sound quality ;)
Thanks for the video. Wondering what's the difference with hidden Markov model
Great explanation.. 2^300 :-), that's why we need quantum computing. QC will solve SAT kind of problem in a beautiful way.
Very informative, thank you!
Very well explained, thanks :)
Wow, thank you.. really good explanation..
HAHAHA, coming in here from the video about Generative Adversarial Network and realize need to understand this concept in order to understand GAN, the recc Algo really guessed my thoughts right...
crazily good man, congrats!
great content. I am trying to write review paper on Restricted Boltzmann machine for Chemometrics.
Thank you, sir this really helped me
Great video! A question: when you pick a sample to increase scores, you increase all the nodes (and edges) that consist this sample. However this not only increase the probability with this particular sample but also other combinations. This seems contradict to the Gibbs sampling method. Is there something I misunderstood, or this is correct but we just tolerate this side effect as it still does the job?
Great question! You understood it correctly. As you improve the scores for a sample, it may affect the scores for other samples (this problem happens in most other ML algorithms too). The hope is that if you do this for all the samples, it starts capturing the form of the data.
Sorry, but I still didn't get this point. In your particular example, you only can see the visible layer. So, when Beto shows up, you'll end up increasing BD OR BE, cause you don't see the hidden layer (cat or dog). Besides, when other samples comes (Alisha with Cameron) it doesn't give you any compensation that increase BE and decrease BD. So, in the end, you should increase all combination related to samples (and not only those truly related with visible and hidden layers). But this is a toy example. Maybe, in real data, this relations are more complex and then such compensation truly happen. Or maybe I'm still misunderstanding something.
I know its gonna be great. Thats why I hit the like button before I even watch.
Very good video but I got struck at 14:48. Why ACE grows faster than ACD while these cases are all active at the same time?
This is MASSIVELY GOOD
Awesome explanation as always, next deep belief network??
Awesome explanation
Great video. Thanks a lot.😀
Awesome explaining. The naming of those animals is funny though.
Nice explaination!
Thank you. Very useful
Imagine paying thousands to university to teach me RBM and then finding a TH-cam video that explains it 100 times better then the professor in half the time. Couldn't be me
Excellent explanation!!
Thank you so mucu!
Amazing video!
Great explanation. What exactly meant by Energy in RBM? How do you define Energy in Layman's way?
Great question! I don't know this part very well, but if the particles have spins that align or not align, there are different energies. Look for Ising Model, and that describes it better.
how did score of BE become 7? it should be 1+4+1=6 right? But then it becomes as likely as ABCDE.
Yes you're right, it's 6. Thank you!
Thanks a lot.
Superb Content 😊😊😊😊
Hi Luis - seems there are some errors like BD should be -1 and DE should be 6 - can you clarify? but good video overall!
Yes, i have same thought
Yes you're right, they're -1 and 6. Thank you!
Hi Luis, please suggest any book for quantum computing or any youtube channel for software engineer
Hi Lalit! sorry for the super late reply. Here is my favorite quantum computing course: th-cam.com/video/VPsl_5RQe1A/w-d-xo.html
And this one for quantum machine learning: th-cam.com/video/QtWCmO_KIlg/w-d-xo.html
Enjoy!
@@SerranoAcademy Thanks :)
what are a_i and b_i in the equation 7:13?
Thank you for this! The only problem is I don't know how you can get Descartes and Euler showing up at the same time if you use your sampling algorithm
Thanks, great question! The probability of that would be really low, since the weight for any of those configurations with Descartes and Euler would have a very low weight (as some negative weights are forced to appear). But if you run it many times, you may run into that configuration.
Thanks a lot!
This is great 👍
I have one question, is the sampling random or biased towards the points with higher probabilities so far
Great question! The sampling is biased towards the combinations of points that have higher scores.
Great thanks for clarification
I guessed it tbh
Yhank you again Luis. One dubbio: shouldn't the score of BE be 1+4+1=6 instead of 7 ?
Yes you're right, it's 6. Thank you!
Sir ur a legend
Thanks a lot
Best one, salute
Always Luis is the best!
should not Beto and Euler case should have score:6 ? @ 6:19
BTW best tutorial on RBMs
Yes you're right, it's 6. Thank you!
Thanks!
Nice video, should get more exposure!
Hi. Shouldn't BE equals 6=4+1+1 ?
Yes you're right, it's 6. Thank you!
great great video
Very good video! The sound quality should be improved! the dog and cat belongs to the hidden layer! The persons mentioned have no knowledge of which pet is in the house. It is not clear to me what is the input and output of this neuronal net.
Very well explained.the example you drew for input nodes and hidden nodes is just damn cool 😁
Nice video, but there are a little mistake, the score of BE is 6 not 7.
Can you give me your slides please?
I will be very grateful for that
is RBM a kind of Generative machine?
Yes, it is
which better Autoencoder or RBM?
30:26 Shouldn´t the sum of probabilities be equal to 1? 0.73+0.31=1.04!=1
Estipenda forma de aprender matematicas.
Gracias Guillermo!
👏👏👏👏👏👏👏👍👍👍👍👍
Not knowing the animals exist, then Occam's razor applies: Aisha and Cameron are having an affair. Since on of them is married, they lie about knowing each other. =)
Lol! Good model! :)
Never seen any video better then this🦾🤯
Yhank you again Luis. One dubbio: shouldn't the score of BE be 1+4+1=6 instead of 7 ?
Yes you're right, it's 6. Thank you!