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Compare up to three published courses by skills, level, duration, prerequisites and certification requirements.
| Compare | Multi-Layer Neural Networks From Scratch |
|---|---|
| Level | Intermediate |
| Duration | 18–26 hours |
| Format | Self-paced course |
| Access | Premium access |
| Skills | weights matrix, bias vector, input/output shapes, parameter storage, initialization, hidden layers, activation placement, logits, output layer, batch dimensions, local gradients, chain rule across layers, parameter gradients, gradient accumulation, backward order, constant initialization failure, small random initialization, Xavier/Glorot intuition, He initialization intuition, epochs, batches, forward, loss, backward, update, logging, validation, shape mismatches, NaNs, exploding gradients, dead activations, learning-rate issues, gradient checks, overfitting tiny datasets, dataset preparation, network design, training, evaluation, error analysis, README |
| Modules | 7 |
| Prerequisites | Neural Network Foundations; Activation Functions |
| Assessment | Course assessment |
| Certificate | Completion requirements apply |