Edited, memorised or added to reading list

on 05-Jan-2021 (Tue)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

#MLBook #machine-learning #weight
Some algorithms, like SVM, allow the data analyst to provide weightings for each class. These weightings influence how the decision boundary is drawn. If the weight of some class is high, the learning algorithm tries to not make errors in predicting training examples of this class (typically, for the cost of making an error elsewhere).
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 4968281672972

Tags
#MLBook #clustering #dimensionality-reduction #machine-learning #model #outlier-detection #unsupervised-learning
Question
In unsupervised learning, the dataset is [...]. Again, \(\mathbf x\) is a feature vector, and the goal of an unsupervised learning algorithm is to create a model that takes a feature vector \(\mathbf x\) as input and either transforms it into another vector or into a value that can be used to solve a practical problem. For example, in clustering , the model returns the id of the cluster for each feature vector in the dataset. In dimensionality reduction, the output of the model is a feature vector that has fewer features than the input \(\mathbf x\); in outlier detection, the output is a real number that indicates how \(\mathbf x\) is different from a “typical” example in the dataset.
Answer
a collection of unlabeled examples \(\{\mathbf x_i\}^N_{i=1}\)

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In unsupervised learning, the dataset is a collection of unlabeled examples \(\{\mathbf x_i\}^N_{i=1}\). Again, \(\mathbf x\) is a feature vector, and the goal of an unsupervised learning algorithm is to create a model that takes a feature vector \(\mathbf x\) as input and either transfor

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 5525591690508

Tags
#continuum-mechanics #has-images
Question

Describe some simplifications which arise when considering a single fixed reference configuration.

Answer
When working with a single fixed reference configuration, as we will most often do, one can dispense with talking about the body \(\mathcal B\), a configuration \(\chi\) and the particle \(p\), and work directly with the region \(\mathcal R_\textrm{ref}\), the deformation \(\mathbf y \left( \mathbf x \right)\) and the position \( \mathbf x\).

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6178543635724

Tags
#cinemática #has-images #mecanismos
Question
Defina cinemática.
[unknown IMAGE 6178541276428]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6178550451468

Tags
#has-images #mecanismos #partícula
Question
Defina partícula.
[unknown IMAGE 6178549140748]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6178557267212

Tags
#corpo #corpo-deformável #corpo-rígido #has-images #mecanismos
Question
Defina corpo, corpo rígido e corpo deformável.
[unknown IMAGE 6178555956492]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180536454412

Tags
#has-images #mecanismos
Question
Defina acoplamento, acoplamento direto e acoplamento indireto.
Answer


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180568173836

Tags
#atuador #has-images #mecanismos
Question
Defina atuador.
[unknown IMAGE 6180566863116]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180571581708

Tags
#has-images #mecanismos #ponto-de-interesse
Question
Discorra sobre ponto de interesse.
Answer

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180582329612

Tags
#has-images #mecanismos
Question
Defina mecanismo articulado.
Answer


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180587310348

Tags
#has-images #mecanismos #representações-de-mecanismos
Question
De que formas um mecanismo pode ser representado?
[unknown IMAGE 6180585999628]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180602514700

Tags
#estrutura #has-images #mecanismos
Question
Defina o significado de estrutura de um mecanismo ou cadeia cinemática.
[unknown IMAGE 6180601203980]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180605922572

Tags
#grau-de-liberdade #has-images #mecanismos
Question
Defina grau de liberdade.
[unknown IMAGE 6180604611852]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180609330444

Tags
#espaço-de-trabalho #espaço-de-trabalho-espacial #espaço-de-trabalho-planar #has-images #mecanismos
Question
Discorra sobre espaço de trabalho, dimensão do espaço de trabalho (\(\lambda\)), espaço de trabalho planar e espaço de trabalho espacial.
[unknown IMAGE 6180608019724]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180612738316

Tags
#circuito #has-images #mecanismos
Question
Discorra sobre circuito e número de circuitos independentes (\(\nu\)).
Answer



statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180620078348

Tags
#análise #has-images #mecanismos #síntese
Question
Conceitue e diferencie análise e síntese de mecanismos.
[unknown IMAGE 6180618767628]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6180635806988

Tags
#deslocamento #has-images #mecanismos
Question
Discorra sobre deslocamento.
Answer


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

pdf

cannot see any pdfs







Flashcard 6194683841804

Tags
#espaço-amostral #evento
Question
Defina espaço amostral e evento.
Answer
Em teoria das probabilidades, o espaço amostral ou espaço amostral universal, geralmente denotado S, E, Ω ou U (de "universo"), de um experimento aleatório é o conjunto de todos os resultados possíveis do experimento. Por exemplo, se o experimento é lançar uma moeda e verificar a face voltada para cima, o espaço amostral é o conjunto \({\displaystyle \{cara,coroa\}}\). Para o lançamento de um dado de seis faces, o espaço amostral é \({\displaystyle \{1,2,3,4,5,6\}}\). Qualquer subconjunto de um espaço amostral é comumente chamado um evento, enquanto subconjuntos de um espaço amostral contendo apenas um único elemento são chamados de eventos elementares ou eventos atômicos.

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill
Espaço amostral – Wikipédia, a enciclopédia livre
abilidade condicional Independência Independência condicional Lei da probabilidade total Lei dos grandes números Teorema de Bayes Desigualdade de Boole Diagrama de Venn Diagrama de árvore v d e <span>Em teoria das probabilidades, o espaço amostral ou espaço amostral universal, geralmente denotado S, E, Ω ou U (de "universo"), de um experimento aleatório é o conjunto de todos os resultados possíveis do experimento. Por exemplo, se o experimento é lançar uma moeda e verificar a face voltada para cima, o espaço amostral é o conjunto { c a r a , c o r o a } {\displaystyle \{cara,coroa\}} . Para o lançamento de um dado de seis faces, o espaço amostral é { 1 , 2 , 3 , 4 , 5 , 6 } {\displaystyle \{1,2,3,4,5,6\}} . Qualquer subconjunto de um espaço amostral é comumente chamado um evento, enquanto subconjuntos de um espaço amostral contendo apenas um único elemento são chamados de eventos elementares ou eventos atômicos. Para alguns tipos de experimentos, podem existir dois ou mais espaços amostrais possíveis plausíveis. Por exemplo, quando retirado uma carta de um baralho de 52 cartas, uma possibilidad