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Neural Network Tutorial | Introduction to Neural Network | Deep Learning Tutorial | Edureka
The document discusses the structure and functioning of neural networks, focusing on the concepts of neurons, layers, and weights. It explains the processes involved in calculating the weighted sum and activation functions such as sigmoid and ReLU. The document emphasizes the organization of input, hidden, and output layers with a specific example of a neural network consisting of 784 neurons and approximately 13,000 weights and biases.
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- 13. Neural Networks First Layer ofNeural Network HIDDEN LAYERS INPUTLAYERS OUTPUTLAYERS
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- 17. W1 W2 W3 Wn Wn-1 Weights W1X1 + W2X2+ W3X3 + W4X4 + . . . . . . . . . . . .WnXn Weighted Sum
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- 19. BIAS W1 W2 W3 Wn-1 Wn Bias for Inactivity (W1X1 +W2X2 + W3X3 + W4X4 + . . . . . . . . . . . .WnXn ) > B Weighted Sum σ (W1X1 + W2X2 + W3X3 + W4X4 + . . . . . . . . . . . .WnXn + B ) BIAS
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