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Agenda
ā€¢ What is Backpropagation?
ā€¢ What is Backpropagation in a Neural network?
ā€¢ How does Backpropagation work?
ā€¢ Benefits of Backpropagation
ā€¢ Applications of Backpropagation
Which among the following is not a layer of a neural network?
D. Hidden Layer
A. Input Layer
C. Propagation
Layer
B. Output Layer
Backpropagation is an algorithm which is created to test errors which will
travel back from input nodes to output nodes.
Input Nodes Output Nodes
Neutral Network
It is similar to a Biological network that contains neuron coupled with each
other.
Input Layer Output Layer
Hidden Layer
Weight
a = Activation Function
a
w4
w2
w1
w3
w5
w6
a
w1
w2
w3
w4
w5
w6
} Loss
Calculation
Input Layer a
w1
w2
w3
w4
w5
w6
w1,w2,w3ā€¦.w6=weight
Output Layer
a
w1
w2
w3
w4
w5
w6
H1=x1Ɨw1+x2Ɨw2+b1
a
w1
w2
w3
w4
w5
w6
Update
a
w1
w2
w3
w4
w5
w6
Update
Which among the following is not a layer of a neural network?
D. Hidden Layer
A. Input Layer
C. Propagation
Layer
B. Output Layer
Benefits
Quick Versatile No extra functions
Applications
Speech Recognition Character Recognition
Signature Recognition Face Recognition
Conclusion
These are some of the highlights of the fundamentals of Big Data.
If you want to become an expert on Big Data then please visit the
Simplilearn portal. It offers courses on Big Data in collaboration
with top Universities with accreditation and valuable certificate
courses.
And also Simplilearn will give insight into the real-time application
of Big Data in collaboration with industries like IBM. To get more
opportunities in this field, enroll to our program called Data
Engineering Certification Course.

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Backpropagation in Neural Networks | Back Propagation Algorithm with Examples | Simplilearn