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100 T

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Flashcard 4513809435916

Question
Which field of accounting is assigned to external accounting? A) Statistics and comparative methods B) Cost and performance accounting C) Financial accounting D) Planning/budgeting
Answer
[default - edit me]


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Flashcard 4513811008780

Question
[default - edit me]
Answer
C) Financial accounting


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Caracterização dos sintomas DOR: • Sintoma mais frequente • Linha mediana do epigástrico, logo abaixo do apêndice xifoide • Questionar alívio de dor e ritmicidade • Úlcera duodenal: alívio da dor após ingestão de alimento → dor alivia no período pós-prandial precoce e aumenta no período pós-prandial tardio.

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Flashcard 4513977732364



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FUNKCJA PIERWOTNA


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Flashcard 4513985334540



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TWIERDZENIE O CAŁKOWANIU PRZEZ PODSTAWIANIE


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Flashcard 4513988218124



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CAŁKA NIEOZNACZONA


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Flashcard 4513990577420



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PODSTAWOWE TWIERDZENIE O FUNKCJACH PIERWOTNYCH


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Flashcard 4513993723148



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TWIERDZENIE DE L’HOSPITALA


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Flashcard 4513996082444



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TWIERDZENIE O WARUNKACH KONIECZNYCH ISTNIENIA PUNKTU PRZEGIĘCIA


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Flashcard 4513998441740



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TWIERDZENIE O WARUNKACH WYSTARCZAJĄCYCH ISTNIENIA PUNKTU PRZEGIĘCIA


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Flashcard 4514000801036



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PUNKT PRZEGIĘCIA WYKRESU


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Flashcard 4514005257484



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WKLĘSŁOŚĆ/ WYPUKŁOŚĆ WYKRESU


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Flashcard 4514019675404



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TWIERDZENIE O WARUNKACH WYSTARCZAJĄCYCH ISTNIENIA EKSTREMUM FUNKCJI Z WYKORZYSTANIEM POCHODNYCH WYŻSZYCH RZĘDÓW


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Flashcard 4514024918284



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PUNKT STACJONARNY FUNKCJI


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Flashcard 4514027277580



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TWIERDZENIE O WARUNKACH WYSTARCZAJĄCYCH ISTNIENIA EKSTREMUM FUNKCJI


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Flashcard 4514029636876



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TWIERDZENIE O WARUNKACH KONIECZNYCH ISTNIENIA EKSTREMUM FUNKCJI


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Flashcard 4514032782604



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MAKSIMUM I MINIMUM LOKALNE


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Flashcard 4514035141900



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OTOCZENIE


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Flashcard 4514037501196



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POCHODNA FUNKCJI


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Flashcard 4514052967692



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SĄSIEDZTWO


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ASYMPTOTA PIONOWA


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Flashcard 4514057686284



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TWIERDZENIE O WARUNKACH KONIECZNYCH I WYSTARCZAJĄCYCH ISTNIENIA ASYMPTOTY UKOŚNEJ


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Flashcard 4514059521292



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ASYMPTOTA UKOŚNA


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Flashcard 4514075512076



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CIĄGŁOŚĆ FUNKCJI


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Flashcard 4514083114252

Tags
#nlp #word2vec
Question
What is the name of the neural network architecture for Word2Vec?
Answer
skip gram


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Unknown title
Word2Vec Tutorial - The Skip-Gram Model · Chris McCormick Chris McCormick About Tutorials Archive Word2Vec Tutorial - The Skip-Gram Model 19 Apr 2016 This tutorial covers the skip gram neural network architecture for Word2Vec. My intention with this tutorial was to skip over the usual introductory and abstract insights about Word2Vec, and get into more of the details. Specifically here I’m diving into the sk







Flashcard 4514085473548

Tags
#nlp #word2vec
Question
How are words input to the neural network when training word2vec?
Answer
We’re going to represent an input word like “ants” as a one-hot vector. This vector will have 10,000 components (one for every word in our vocabulary) and we’ll place a “1” in the position corresponding to the word “ants”, and 0s in all of the other positions.


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work, so we need a way to represent the words to the network. To do this, we first build a vocabulary of words from our training documents–let’s say we have a vocabulary of 10,000 unique words. <span>We’re going to represent an input word like “ants” as a one-hot vector. This vector will have 10,000 components (one for every word in our vocabulary) and we’ll place a “1” in the position corresponding to the word “ants”, and 0s in all of the other positions. The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that voc







Flashcard 4514087832844

Tags
#nlp #word2vec
Question
What is the output of the neural network used in training word2vec?
Answer
The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that vocabulary word.


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tor. This vector will have 10,000 components (one for every word in our vocabulary) and we’ll place a “1” in the position corresponding to the word “ants”, and 0s in all of the other positions. <span>The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that vocabulary word. Here’s the architecture of our neural network. There is no activation function on the hidden layer neurons, but the output neurons use softmax. We’ll come back to this later. When train