Deep Learning 01

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câu hỏi câu trả lời
accomplish
We describe how to accomplish these goals by specifying a model that represents certain beliefs
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osiągać
throughout
Linear algebra is a branch of mathematics that is widely used throughout science and engineering.
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poprzez
prerequisites
the key linear algebra prerequisites
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warunki wstępne
slope
the slope of the line
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stok / nachylenie
nachylenie linii
can be thought of
scalar can be thought of as a matrix with only a single entry
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można pomyśleć
to yield
We allow the addition of matrix and a vector, yielding another matrix
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wydawać, dawać
commutative
Matrix multiplication is not commutative
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przemienny
undesirable
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niepożądany
we can derive
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możemy wprowadzić
to staring
Sunset came; I stared at the México sky. Isn’t nature splendid
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gapić się
exclusive to
It’s not exclusive to machine learning
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wyłącznie dla
consecutive
Where tokens are groups of N consecutive words
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kolejny
discarding
and those that treat input words as a set, discarding their original order
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odrzucanie
discrepancy
you would face the risk of introducing small preprocessing discrepancies that would hurt the model’s accuracy.
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rozbieżność
reshuffling
Even within a given language, you can typically say the same thing in different ways by reshuffling the words a bit
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przetasowania
is an interesting one
The problem of order in natural language is an interesting one:
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jest ciekawa
Even further
Even further, if you fully randomize the words in a short sentence
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Nawet dalej / co więcej
pivotal
How to represent word order is the pivotal question
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kluczowy
from which spring
from which different kinds of NLP architectures spring.
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z którego źródła?
how/when to leverage which
Let’s see how they work, and when to leverage which.
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jak/kiedy wykorzystać które?
without leveraging
Meanwhile, the best score that can be achieved on this dataset without leveraging external data is around 95% test accuracy.
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bez wykorzystywania
How could we address this?
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Jak moglibyśmy się tym zająć?
sparse, sparsity
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rzadki, rzadkość

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