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Introduction To Neural Networks
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Neurons And Perceptrons
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Activation Functions
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Forward Propagation
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Loss And Cost Functions
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Backpropagation
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Optimizers
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Regularization And Normalization
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Weight Initialization
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Cnn Architectures
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Attention And Transformers Dl
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Self Supervised And Contrastive Learning
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Graph Neural Networks
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easy
Weight Initialization
A new engineer initializes all weights in a 5-layer fully connected network to 0. After 100 training epochs, the model achieves exactly random chance (10% for a 10-class problem). What went wrong?
A
Zero initialization causes NaN during forward propagation
B
Zero initialization causes the symmetry problem: all neurons in each layer compute the same output and receive the same gradient. All neurons in a layer update identically on every step. The model effectively has only 1 neuron per layer regardless of the layer width. With all weights zero, every hidden layer outputs 0, and the gradient for every neuron in a layer is identical, so they all update to the same non-zero value — they remain symmetric forever
C
Zero initialization prevents the optimizer from computing gradients
D
Zero initialization only fails for layers with more than 100 neurons
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