# Tag Info

### If fine tuning produces better performance than feature extraction, is there any advantage of using feature extraction?

A very common scenario is that we simply do not have the resources or time (or at least it's a get most of the benefit for minimal effort situation) to fine-tune a model appropriately, but can run ...
• 24.1k

### If fine tuning produces better performance than feature extraction, is there any advantage of using feature extraction?

If the dataset is of similar nature, the features extracted for the original (possibly very large) dataset, which is conceptually achieved in the first layers of a neural net, should be representative ...
• 53.1k

### $\sin(x)$ is a counterexample to the universal approximation theorem

The classical (Cybenko) universal approximation theorem has a condition about the function being approximated on a compact space. On the real line, the Heine-Borel theorem says that compacts sets are ...
• 35.6k

### Cross-Entropy or Log Likelihood in Output layer

I think that @user650654 made a mistake in his formulation of the Cross Entropy and therefore his conclusion is incorrect. In the case of hard labels (i.e., using one-hot vectors for ground truth, ...

### Why Deep Learning needs to be performed in Graphical representations?

Because traditional deep learning methods do not take into account a crucial property of the adjacency matrix representation: node permutation equivariance. One axiom of deep learning is that ...
• 16.1k
1 vote

### Why Deep Learning needs to be performed in Graphical representations?

Same could have been said about image data, but CNNs were born. Models that exploit the structure of the underlying data are likely to be more successful than generic methods.
• 53.1k

### Autoencoder accuracy with standardized data

In the input of the network it's fine to input normalized data. This is exactly what batch normalization does. In the output layer you don't add an activation. Instead you output the values from the ...
• 814

### Can naive Bayes model this type of (approx. circular) decision boundary?

You were right. Naive Bayes can actually create a circular decision boundary as the variance of red and black data points is different. For Gaussian distribution estimation, if NB is forced to use ...
1 vote

• 53.1k