What is the difference? Under which criteria should each type of boost be used?  What is the theory behind each of these methods?
120869
How is AdaBoost different from a Gradient Boosting algorithm since both of them use a Boosting technique? I could not figure out actual difference between these both algorithms from a theory point of view.
47279
When dealing with the class imbalance problem in a binary classifier, there are three ways I know of to address it: over-sampling, under-sampling and using cost-sensitive methods.   Are there any guidelines, rules of thumb or general strategies to choose among these methods? A possible answer would be: over-sample when the positive class has more than 100 instances (I just made it up).
44372
I find in many places mentions of pre-amplifiers being used to boost audio signals prior to feeding to a power amplifier.  Wikipedia's article on  gives some hints at their usage, but it doesn't really explain why it would be necessary to have two amplifiers in series rather than a single one with greater gain.  If a pre-amp is just a power amp with a smaller gain, what's the point, and why the distinction between the two kinds? Is the distinction in function, or simply purpose?
86995
In my machine learning class these two methods were discussed and mentioned that both should be used. I have a couple questions about this:  1) Can I mix and match these two approaches? e.g. Feature Scale x1 and Mean Normalize x2?  2) How do you determine which of these options to apply? It seems that either could accomplish the task of increasing your convergence rates... I suppose you just need to know your data set to understand which will reliably reduce your values while leaving as few outliers as possible?
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I know the definitions of both but can't specifically tell how they are different and if one is better than the other.  Please help.
108957
When writing CUDA applications, you can either work at the driver level or at the runtime level as illustrated on this image (The libraries are CUFFT and CUBLAS for advanced math):   (source: )    I assume the tradeoff between the two are increased performance for the low-evel API but at the cost of increased complexity of code. What are the concrete differences and are there any significant things which you cannot do with the high-level API?  I am using CUDA.net for interop with C# and it is built as a copy of the driver API. This encourages writing a lot of rather complex code in C# while the C++ equivalent would be more simple using the runtime API. Is there anything to win by doing it this way? The one benefit I can see is that it is easier to integrate intelligent error handling with the rest of the C# code.
65793
I have studied the activation function types for neural networks. The functions themselves are quite straightforward, but the application difference is not entirely clear.  It's reasonable that one differentiates between logical and linear type functions, depending on the desired binary/continuous output but what is the advantage of sigmoid function over the simple linear one?  ReLU is especially difficult to understand for me, for instance: what is the point to use a function that behaves like linear in case of positive inputs but is "flat" in case of negatives? What is the intuition behind this? Or is it just a simple trial-error thing, nothing more?
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I know it's a rather debated question on Stack Exchange communities but let me explain the points of this question.  I'm writing my capstone on Machine Learning and I need to clarify deeply, giving valid references, the differences among Data Mining, Big Data, Statistical Learning and Machine Learning. As far as I understand (I'm reading The Elements of Statistical Learning, An Introduction to Statistical Learning which are well known books) and based on the questions linked below, those are basically Machine Learning. Correct me if you do not agree with me.  However, I can't find an article or book with a detailed discussion on the origins of those fields and relationship of each other and I can't cite, like, the questions below. They're not considered valid references.
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I'm trying to figure out which of these interfaces I need to implement. They both essentially do the same thing. When would I use one over the other?
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I am programming with normal pointers, but I have heard about libraries like Boost that implement smart pointers. I have also seen that in Ogre3D rendering engine there is a deep use of shared pointers.  What exactly is the difference between the three, and should I stick on using just a type of them?
123777
What is the difference between the three functions and when to use them??
6890
What is the difference between g++ and gcc?  Which one of them should be used for general c++ development?
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I don't quite understand the differences between the two besides the obvious price differences.  What does each offer over the other?
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Unlike rotations, the boost transformations are non-unitary. Therefore, the boost generators are not Hermitian. When boosts induce transformations in the Hilbert space, will those transformation be unitary? I think no. If that is the case, what is the physical significance of such non-unitary transformations corresponding to boosts in the Hilbert space?
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I see in C++ there are multiple ways to allocate and free data and I understand that when you call malloc you should call free and when you use the new operator you should pair with delete and it is a mistake to mix the two (e.g. Calling free() on something that was created with the new operator), but I'm not clear on when I should use malloc/ free and when I should use new/ delete in my real world programs.  If you're a C++ expert, please let me know any rules of thumb or conventions you follow in this regard.
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I wonder if someone knows any general rules of thumb regarding the number of bootstrap samples one should use, based on characteristics of the data (number of observations, etc.) and/or the variables included?
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Is there a difference? Both versions are common.  If there is a difference, which do I use when, and why?