Linked Questions
12 questions linked to/from What *is* an Artificial Neural Network?
49
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6
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Are neural networks better than SVMs?
For some time now I have been studying both support vector machines and neural networks and I understand the logic behind each of these techniques. Very briefly described:
In a support vector machine,...
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28
votes
6
answers
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Is a neural network essential for deep learning?
I received preliminary materials on deep learning in my class. It was written as follows. This raised me the question of the basic meaning of the word deep learning.
Deep learning is a machine ...
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32
votes
3
answers
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Is logistic regression a specific case of a neural network?
I ended up in a debate regarding logistic regression and neural networks (NNs).
Is it wrong to say that logistic regression is a specific case of a neural network?
I have seen a lot of explanation in ...
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9
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3
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Can a neural network learn "a == b" and "a != b" relationship, with limited data?
For example, I have the following feature set:
{a:1, b:1} -> 1
{a:2, b:2} -> 1
{a:3, b:3} -> 1
{a:1, b:2} -> 0
{a:4, b:6} -> 0
{a:3, b:5} -> 0
...
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15
votes
2
answers
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Why do CNNs conclude with FC layers?
From my understanding, CNNs consist of two parts. The first part (conv/pool layers) which does the feature extraction and the second part (fc layers) which does the classification from the features.
...
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3
votes
2
answers
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What are the predictor variables in a neural network?
In a linear regression model, the predictor or independent (random) variables or regressors are often denoted by $X$. The related Wikipedia article does, IMHO, a good job at introducing linear ...
user82135
7
votes
2
answers
366
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What do neural networks offer that traditional non-linear statistical models do not offer?
I have tried to find an answer to this question but have not found a satisfactory answer. I understand that neural networks(NNs) offer the potential to complex build non-linear models. What I don’t ...
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1
vote
1
answer
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Combining Random forest with Adam (or an other gradient method)
There is no "gradient" in the standard Random Forest formulation, but can I combine random Forests with an optimisation method like Gradient Descent or SGD?
Can I use Adam (Adaptive moment estimation)...
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4
votes
1
answer
411
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What is the definition of layer in neural network?
What is the precise definition of layer in neural network? Are things like concatenate functions, activations, batch normalizations, skip connections considered as layers?
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1
vote
2
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Is there a fundamental difference between artificial neural networks and "other" supervised machine learning models
I would like to link three of these resources, present my understanding of what I read, and pose the question if my understanding is approximately correct
Is Machine Learning glorified curve fitting
...
user139173
0
votes
1
answer
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Meaning of Neural Network Doesnt Imply More Structure on the Model
I am watching a video on Thompson Sampling for Machine Learning and at [16:38] the presenter mentions that we can use various models to fit the data, (such as Gaussian Process, SVM in my opinion), but ...
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1
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0
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Which classic ML algorithms can be written as neural networks?
Many classic machine learning algorithms can be reframed as simple neural networks.
For instance:
Linear regression can be thought of as a neural network with one linear layer with $p$ inputs and $1$ ...
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