Training Pipeline
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Training 2-Layer MLP on MNIST Dataset
Configuration
Model Architecture
Input Layer
784 neurons (28×28 pixels)
Hidden Layer
128 neurons + ReLU
Output Layer
10 neurons (digits 0-9)
Training Progress
Loss
0.0000Accuracy
0%Training Log
Waiting to start training...
Training Steps
1
Forward Pass
Compute predictions through the network
2
Compute Loss
Calculate cross-entropy loss
3
Backward Pass
Compute gradients via autograd
4
Update Weights
Apply SGD optimizer step