Derivation of Back Propagation Algorithm Neural Networks Algorithm Machine Learning by Mahesh Huddar

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Derivation of Weight Equation in Back Propagation Algorithm Artificial Neural Networks Algorithm Machine Learning by Mahesh Huddar

We use the Stochastic gradient descent rule to derive the equation for updating weights in the backpropagation algorithm.
Stochastic gradient descent involves iterating through the training examples one at a time, for each training example, d descending the gradient of the error Ed with respect to this single example.

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The following concepts are discussed:
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Derivation of the backpropagation algorithm,
backpropagation neural network,
backpropagation algorithm in the neural network,
the backpropagation learning algorithm,
backpropagation algorithm in machine learning,
backpropagation in neural networks,
backpropagation algorithm,
backpropagation algorithm in machine learning,
gradient descent algorithm,
gradient descent method,
perceptron training algorithm,
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Your video helped very much in my prepration for exam, thank you so much 😊

vindyasemith
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But the activation function can be different. It will not be sigmoid always. So don’t try to remember in terms of o and (1-o)

ssvedant
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Thank you so much sir for such a nice explanation.

amritpattnaik
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do you have convolution neural network lecture also?

prashantkumar
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I don't understand when you invert the order of sum and derivative operators. Why the sum disappear after? I saw that you have x and X (capitol) are there different but why?

StefanoTruzzi
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Sir..Lecture is very nice. Could you please tell me which book you have followed for this backpropagation topic?

RJENILA
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very well explained sir.Where to find the notes for this

MEGHANAMBatchPESUniversity
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Sir can u share the material or PDF for this topic sir

paruveedh
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How can I mail you sir? I need your help.

j.ara