Questions tagged [theory]

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Evolution of classification methods

In the book "Deep Learning with Python" by Francois Chollet (2018), in section 1.2.4 one can find: Decisions trees learned from data began to receive significant research interest in the 2000s, and ...
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What is the name of the prediction method?

Suppose, I have a data set: ix m_t1 m_t2 1 42 84 2 12 12 3 100 50 then, we can calculate the difference between ...
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Is it possible for a neural net to score as high as a different form of supervised learning?

I've been working with the Adult Census Income dataset from UCI http://archive.ics.uci.edu/ml/datasets/adult I've created two different models, one using a gradient boosted classifier with sklearn, ...
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A question on realizable sample complexity

I came across the following exercise, and I just can't seem to crack it: Let $l$ be some loss function such that $l \leq 1$. Let $H$ be some hypothesis class, and let $A$ be a learning algorithm. ...
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Situations where advanced theoretical knowledge of ML helped solve a real world problem?

I've invested lot of time trying to understand the theoretical aspects of Deep Learning and Neural Networks - but I'm now questioning whether it is worth it or not, given that I am someone who works ...
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Why study properties with infinitesimal change?

I read about analysis on local properties of neural networks. Some of them study the impact of "infinitesimal" change to an input. Like in Percy Liang's paper Understanding Black-box Predictions via ...
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What's the math for real world back-propagation?

Considering a simple ANN: $$x \rightarrow f=(U_{m\times n}x^T)^T \rightarrow g = g(f) \rightarrow h = (V_{p \times m}g^T)^T \rightarrow L = L(h,y)$$ where $x\in\mathbb{R}^n$, $U$ and $V$ are ...
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Is there a name for a scale which mixes ordinal and nominal?

The textbooks I have differentiate between nominal, ordinal, interval and ratio scales. The ordinal scale is quite popular in the wild, used for basically all subjective data, and also for dividing ...
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Softmax function forms

The constraint that the n outputs must sum to $1$ means that only $n−1$ parameters are necessary; the probability of the $n^{th}$ value may be obtained by subtracting the first $n−1$ probabilities ...
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Difference between machine learning and artificial intelligence

My question is this: Is there any difference between machine learning and artificial intelligence? Or do these terms refer to the same thing?
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Deriving backpropagation equations “natively” in tensor form

Image shows a typical layer somewhere in a feed forward network: $a_i^{(k)}$ is the activation value of the $i^{th}$ neuron in the $k^{th}$ layer. $W_{ij}^{(k)}$ is the weight connecting $i^{th}$ ...
In the standard example of decomposing the MSE into Bias, Variance and Irreducible error: MSE(x) = \left(\mathbb{E}[\hat{f}(x)] - f(x) \right)^2 + \mathbb{E}\left[\left(\hat{f}(x) - f(x)\right)^2\...