Hebb rule with solved example

#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.
12
3
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
06
4
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

[@nikronic]

Very clear explanation. Just note that we can obtain weight values using matrix operations like Y.dot(X.transpose())

[@MarcTuinier]

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

[@tfltd.474]

hebbian is actually unsupervised

[@storgerbenevolent5678]

@@tfltd.474 yeah , why in this video has he taken the output target?

[@dep2460]

@@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

[@anuragsharma1065]

Sir you teach better than most of my teachers and friends .
thanks :)

[@dikshantjain6544]

Glad sir you are back again
Upload more videos
Without you I cannot pass soft computing

[@btechtutorialByNishantMittal]

Sorry it took time to upload the videos,but now videos will be uploaded frequently.

[@aniruddhkhare2080]

Bahut badhiya bhai! Maza aa gya

[@shellnetpeak9466]

thank you so much for this wonderful class but I have a doubt when we can stop the problem

[@knowmore3536]

nice explanation...simple explanations...easy to understand..

[@saranc7865]

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.

[@HemantSingh-si3vh]

Thank u sir for these awesome videos.

[@rabifazil7627]

Clear explanation

[@geetanjaliwadhwa128]

Thank god i got a better video

[@infouniverse1728]

Best explanation

[@amsaraza1210]

Thankuuu very very much

[@gauravbhandari1184]

Great

[@Mobidost]

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.

[@nishanisha6263]

Thank you..for this tutorial

[@vikramPahaadi99]

NO PROBLEM MY LOVE I M ALWAYS THERE FOR U

[@naveench4247]

Awesome explanation! Liked it! Bro, can you please tell me the book name from which you have taken this problem?

[@dikshantjain6544]

Before 1k 😊 views

[@fit_foodie_techie]

Thank you please upload frequently

[@randythamrin5976]

Indian guys selalu bisa diandalkan

[@simransingh6137]

Thank you sir

[@vaibhavsingh3378]

Thanks

[@Phoenix-wr6rn]

Why was bias not drawn in the hebb network

[@srinathsesaya7432]

Thank you

[@storgerbenevolent5678]

Hi , nice video
I have read that Hebb learning rule uses unsupervised learning then why are we using output target here?

[@kanangarayev6110]

I can't understand why we need bias?

[@prithambaswanigiryalkar4815]

Hmare ma'am ne bhi yehi tutorial dekha hoga😂

[@muzzamilwaqas3766]

Weights will be updated in every bipolar question? or we can get yin from every pattern

[@DeepakGupta-gu3ul]

🤗🤗

[@eshamnarula4942]

Why hebb rule used bipolar data instead of binary data?

[@nikronic]

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.

[@auliafaza866]

I need indonesian subtitle haha. Anyway i need tutorial of delta rule too

[@vikramPahaadi99]

NO PROBLEM AULIA TAKE TUTORIAL FROM ME

[@randythamrin5976]

apalagi ngomongnya cepat banget,

[@saosovannarith7736]

I'm new to this stuff, how to get the target value?

[@dewinmoonl]

watch this on 2x speed for maximum hebbian confusion :D

[@monishap1745]

In the Hebb network drawn, we have to draw the bias too right?

[@nisarggogate8952]

bias zero i guess... so it doesn't matter

[@lovely_kratos7134]

U explained too fast,...!!but tnx anyway

[@kamlesh6290]

I have mailed you. Can you please reply on it. .. Need your helo

[@manavpoddar2262]

Bhai thoda araam se padhafo, itni jaldi kya hai

[@crater7531]

please... slow... down...gawd