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concepts of deep learning
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  • Course
  • Deep Learning
  • Institution
  • Deep Learning

Deep Learning – History - Key facts – Biological Neuron- Artificial Neuron – Mc Culloch Pitts Neuron, Limitations of MP Neuron – Perceptron – Multi-Layer Perceptron – Basics of Neural Networks :Neurons, Output Functions – Activation Functions – Weights and Biases

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Document information

  • March 7, 2024
  • 7
  • 2023/2024
  • Class notes
  • Nirmala
  • Deep learning concepts

Subjects

  • perceptron
  • basics of neural networks
  • activation functions
  • weights and biases
  • biological neuron artificial neuron

Written for

  • Deep Learning
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manoj2

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YBAR -
Y YBAR
Y
LEARNI
YBARCA PREDIC
GIVEN BIAS NG
LC TED
RATE
OUTPU OUTPU DIFFERE
X1 W1 X2 W2 T T NCE 0.5
0 0 0 0 0 0.1 0.1 1 1
ITERATION 1
0 0 1 0 1 0.1 0.1 1 0
1 0 0 0 1 0.1 0.1 1 0
1 0 1 0 1 0.1 0.1 1 0

YBAR -
Y YBAR
Y
LEARNI
YBARCA PREDIC
GIVEN BIAS LC TED NG
RATE
OUTPU OUTPU DIFFERE
X1 W1 X2 W2 T T NCE 0.5
0 0 0 0 0 -0.4 -0.4 0 0
ITERATION 2
0 0 1 0 1 -0.4 -0.4 0 -1
1 0 0 0 1 -0.4 -0.4 0 -1
1 0 1 0 1 -0.4 -0.4 0 -1


YBAR -
Y YBAR
Y
LEARNI
GIVEN BIAS YBARCA PREDIC NG
LC TED
RATE
OUTPU OUTPU DIFFERE
X1 W1 X2 W2 T T NCE 0.5
0 0 0 0 0 -0.4 -0.4 0 0
ITERATION 3
0 0 1 0.5 1 -0.4 0.1 1 0
1 0.5 0 0 1 -0.4 0.1 1 0
1 0.5 1 0.5 1 -0.4 0.6 1 0



once Y and Ybar are same then the iteration stops

, GIVN OUTPUT AND PREDICTEDOUTPUT ARE NOT SAME
SO GO FOR WEIGHT AND BIAS ADJUSTMENT




W=W-(LR*D*X)
B=B-LR*D



0.5

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