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Statistical efficiency in this context essentially means that a CNN would require fewer training examples than a fully connected network to learn. Intuitively this seems reasonable: more parameters to learn should mean more samples needed. Of course it is always desirable to minimise the number of training samples needed, so that's a definite advantage of ...


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In essence, your question is about convergence of infinite series. The mathematical discipline that studies such series is hundreds (if not thousands) years old an has nothing to do with "hardware architecture". A basic example of an infinite series is the geometric series: $$ S = 1 + \gamma + \gamma^2 + \gamma^3 + \dots$$ Note that the series is ...


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A quick review of resolving expectations: If you know that a discrete random variable $X$, drawn from set $\mathcal{X}$ has probability distribution $p(x) = \mathbf{Pr}\{X=x \}$, then $$\mathbb{E}[X] = \sum_{x \in \mathcal{X}} xp(x)$$ This equation is the core of what is going on when resolving the expectation in your quoted equation. Resolving the ...


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It's much easier to deal with logarithms, as the relevant numbers are usually very small or very large. If you have a long exponential expression, it's hard to see the difference, but if you're looking at 4.3 vs 5.6, you can immediately see what's happening. And logarithms are a well-known (and well-understood) way of achieving this compression. You can ...


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In its most raw form, convolution is defined as: $(f*g)(t) = \int_{-\infty}^\infty f(\tau) \cdot g(t-\tau) d\tau$. Here, t doesn't represent the time domain. Infact, it represents the real valued argument the book is talking about. In this notion, at moment t, convolution can be thought of as a weighted average of the function $f(\tau)$ weighted by $g(–\tau)$...


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I think that these terms may be used inconsistently across sources. If someone says held-out dataset, I would immediately think of a dataset that is not used for training, but can be used for anything else, validation (hyper-parameter tuning or early stopping) or testing; so, to determine what they are referring to, I would probably take into account the ...


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It is very confusingly worded, and I would think it's incorrect according to linguistic terminology. A lemma is the canonical form of a word, commonly the infinitive of a verb, the nominative singular of a noun, and the positive of an adjective. The inflected forms belonging to a word would the the forms used for other tenses and persons etc for verbs, case ...


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Please note: I am only referring the decision boundary to be a line for simplicity, more often than not it is a hyperplane which is difficult to visualize and spans over n dimensions where n is the dimensionality of your feature space. The explanation is toned in a more general way for emphasizing explainability. Answer What are the 'noisy factors' here? ...


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I would like to add details to Oliver's answer. From the book "Pattern Recognition and Machine Learning" by Bishop (Section 1.2.5): In practice, it is more convenient to maximize the log of the likelihood function. Because the logarithm is monotonically increasing function of its argument, maximization of the log of a function is equivalent to ...


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A bag-of-words-model (BOW) is usually used to represent a text: you throw all the words together (as if in a bag), without keeping track of their sequence. This is a gross simplification over a text, as word sequencing plays an important role in creating the meaning of a text. But on the positive side it's easier to handle, eg in information retrieval tasks, ...


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I can't really make much sense of Eisenstein's distinction between distributional and distributed. And I think in your question you actually mix up the two terms as well, as distributed semantics involve symbolic structures, whereas distributional semantics are numerical vectors according to his definition. EDIT: actually, he seems to mix it up himself there?...


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Having a sound understanding on language processing will help you understand all its concepts. This summarise must reads for NLP.


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I propose you try this. It's about modern Natural Language Processing, Computational Linguistics and Speech Recognition, including Embeddings methods.


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Some of the books that you mention are often used as reference books in introductory courses to machine learning or artificial intelligence. For example, if I remember correctly, in my introductory course to machine learning, the professor suggested the book Pattern Recognition And Machine Learning (2006) by Bishop, although we never used it during the ...


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Pattern Recognition And Machine Learning is a great theoretical book. I don't know anything better on standard ML. I read several pages from it myself and all my colleagues researchers suggest to look there if you are not sure about some concepts. The 2 problems with it are that it's huge and it doesn't cover almost all deep learning models known for today. ...


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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) 1st Edition This book does not give a detailed background information on Markov Decision Processes, different Bellman equations and relationships between the value function and action-value function, etc. It focuses on Deep Reinforcement ...


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Artificial intelligence is a broad field: that's why Artificial Intelligence: A Modern Approach may look a bit dense to newcomers, given that it covers many different aspects of AI, such as search, machine learning, and natural language processing. The first book in this answer is a good book, but it focuses on evolutionary computation approaches, which are ...


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