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2

This question is very broad, so let me attempt to answer it using my own background in time series analysis. As an example, why would I continue using ARIMA to forecast a time series? Why not simply use an LSTM model by default, since this is a type of recurrent neural network that takes time-related dependencies into account? Well, an LSTM model is not ...

1

Why are still traditional machine learning (ML) models used over neural networks if neural networks seem to be superior to traditional ML models? Of course, the model that achieves state-of-the-art performance depends on the problem, available datasets, etc., so a comprehensive comparison between traditional ML models and deep neural networks is not ...

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About the first question, you are right. The $i$ denotes a sample trajectory corresponding to a whole episode. However, Sutton's version is exactly the same one as Levine's if you choose $N=1$. About the second question, the Policy Gradient theorem only tells you what is the gradient up to a constant, so basically any constant is irrelevant. Now, even if ...

4

Though word-embedding is primarily a language modeling tool, it also acts as a feature extraction method because it helps transform raw data (characters in text documents) to a meaningful alignment of word vectors in the embedding space that the model can work with more effectively (than other traditional methods such as TF-IDF, Bag of Words, etc, on a large ...

2

I think you guys are playing on semantics. If you consider feature extraction to be an unlearned preprocessing step to get inputs for your model, then no, word embeddings are not a feature extraction technique (examples here would be BoW counts, n-gram features, etc) If you consider feature extraction to be any form of conversion from text to a set of ...

2

The difference is simply that non-linear regression learns parameters that in some way control the non-linearity - e.g. any weight or bias that is applied before a non-linear function. For instance: $$y = (w_1 x_1 + w_2 x_2)^2 + w_3$$ With such a function to learn, you cannot separate out transformed values of $w_1$ and $w_2$ and turn this into a linear ...

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