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Hebb rule with solved example
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- เผยแพร่เมื่อ 19 ส.ค. 2024
- #neuralnetwork #softcomputing #machinelearning #datamining #algorithm
Hebb algorithm | soft computing | neural networks
Introduction:1.1 Biological neurons, McCulloch and Pitts models of neuron, Types
of activation function, Network architectures, Knowledge representation, Hebb net
1.2 Learning processes: Supervised learning, Unsupervised learning and
Reinforcement learning
1.3 Learning Rules : Hebbian Learning Rule, Perceptron Learning Rule, Delta
Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, WinnerTake-All Learning Rule
1.4 Applications and scope of Neural Networks
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2
Supervised Learning Networks :
2.1 Perception Networks - continuous & discrete, Perceptron convergence theorem,
Adaline, Madaline, Method of steepest descent, - least mean square algorithm,
Linear & non-linear separable classes & Pattern classes,
2.2 Back Propagation Network,
2.3 Radial Basis Function Network.
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Unsupervised learning network:
3.1 Fixed weights competitive nets,
3.2 Kohonen Self-organizing Feature Maps, Learning Vector Quantization,
3.3 Adaptive Resonance Theory - 1
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Associative memory networks:
4.1 Introduction, Training algorithms for Pattern Association,
4.2 Auto-associative Memory Network, Hetero-associative Memory Network,
Bidirectional Associative Memory,
4.3 Discrete Hopfield Networks.
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5
Fuzzy Logic:
5.1 Fuzzy Sets, Fuzzy Relations and Tolerance and Equivalence
5.2 Fuzzification and Defuzzification
5.3 Fuzzy Controllers
Very clear explanation. Just note that we can obtain weight values using matrix operations like Y.dot(X.transpose())
This was a relatively simple explanation of the Hebbian rule (learning?) in a neural network.
What I learned:
- Used for associating, classifying and categorizing patterns
- needs an input and an output (so not useful for unsupervised learning?)
- a way of classifying weights
Disclaimer: I'm new to this stuff :P
hebbian is actually unsupervised
@@tfltd.474 yeah , why in this video has he taken the output target?
@@storgerbenevolent5678 in question we are given the class in which "i" and "o" will belong(1 for i and -1 for o) ... we are to modify weight in such a way so that it happens. Hebb is unsupervised
Sir you teach better than most of my teachers and friends .
thanks :)
Glad sir you are back again
Upload more videos
Without you I cannot pass soft computing
Sorry it took time to upload the videos,but now videos will be uploaded frequently.
Bahut badhiya bhai! Maza aa gya
thank you so much for this wonderful class but I have a doubt when we can stop the problem
nice explanation...simple explanations...easy to understand..
Nice Explanation. But the entire video is just about solving problems, if the underlying concepts are also explained then it would have been spot on.
Thank u sir for these awesome videos.
Clear explanation
Thank god i got a better video
Best explanation
Thankuuu very very much
Great
Why you didn't mention the learning rate in any of hebb rules? How will you train any neuron even If you are not using learning rate factor to avoid saturation.
Thank you..for this tutorial
NO PROBLEM MY LOVE I M ALWAYS THERE FOR U
Awesome explanation! Liked it! Bro, can you please tell me the book name from which you have taken this problem?
Before 1k 😊 views
Thank you please upload frequently
Indian guys selalu bisa diandalkan
Thank you sir
Thanks
Why was bias not drawn in the hebb network
Thank you
Hi , nice video
I have read that Hebb learning rule uses unsupervised learning then why are we using output target here?
I can't understand why we need bias?
Hmare ma'am ne bhi yehi tutorial dekha hoga😂
Weights will be updated in every bipolar question? or we can get yin from every pattern
🤗🤗
Why hebb rule used bipolar data instead of binary data?
Because if you use 0 instead of -1, every time you want to introduce input regarding class 0 (originally -1), the multiplication of x.y will be zero and weights never update for the opposite class.
I need indonesian subtitle haha. Anyway i need tutorial of delta rule too
NO PROBLEM AULIA TAKE TUTORIAL FROM ME
apalagi ngomongnya cepat banget,
I'm new to this stuff, how to get the target value?
watch this on 2x speed for maximum hebbian confusion :D
In the Hebb network drawn, we have to draw the bias too right?
bias zero i guess... so it doesn't matter
U explained too fast,...!!but tnx anyway
I have mailed you. Can you please reply on it. .. Need your helo
Bhai thoda araam se padhafo, itni jaldi kya hai
please... slow... down...gawd