Compare models side-by-side — same input, different architectures
Model A
VS
Model B
M3
MLP
Parameters
235K
Layers
3 (784→128→64→10)
Train Time
~30s
MNIST Accuracy97.2%
Strengths
+Universal approximator
+Learns non-linear boundaries
+Foundation of deep learning
Weaknesses
−No spatial awareness
−Many parameters for images
−Fully connected = inefficient
Best for: Tabular data, non-linear classification, function approximation
VS
M4
CNN
Parameters
62K
Layers
5 (Conv→Pool→Conv→Pool→FC)
Train Time
~2min
MNIST Accuracy99.1%
Strengths
+Spatial awareness
+Weight sharing = efficient
+Translation invariant
Weaknesses
−Needs spatial input
−Fixed receptive field
−Not good for sequences
Best for: Images, spatial data, video frames
Head-to-Head: MNIST Accuracy
MLP97.2%
CNN99.1% ★
Key Takeaway
CNN wins on images because weight sharing and local receptive fields exploit spatial structure. MLP treats every pixel independently — no awareness of neighbors. For tabular data though, MLP can be better since there's no spatial structure to exploit.