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Korean translation of the Keras documentation.

Korean translation of the Keras documentation

This is the repository for the Korean-language .md sources files of keras.io.

Existing files in sources/ should be edited in-line.


์ผ€๋ผ์Šค ๊ณต์‹ ๋ฌธ์„œ ํ•œ๊ตญ์–ดํŒ

์ผ€๋ผ์Šค ๊ณต์‹ ๋ฌธ์„œ์˜ ํ•œ๊ตญ์–ดํŒ์ž…๋‹ˆ๋‹ค. ์ด๋ฏธ ๋”ฅ๋Ÿฌ๋‹์— ์ต์ˆ™ํ•œ ์—ฐ๊ตฌ์ž ๋ฐ ๊ฐœ๋ฐœ์ž ์™ธ์—๋„ ์ฒ˜์Œ ๋”ฅ๋Ÿฌ๋‹์„ ์ ‘ํ•˜๋Š” ์‚ฌ์šฉ์ž๋“ค์ด ์ตœ๋Œ€ํ•œ ์‰ฝ๊ฒŒ ์ดํ•ดํ•˜๊ณ  ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ทธ ์˜๋ฏธ์™€ ์šฉ๋ฒ•, ์šฉ๋ก€๊ฐ€ ์ •ํ™•ํ•˜๊ณ  ๋ช…๋ฃŒํ•˜๊ฒŒ ๊ทธ๋ฆฌ๊ณ  ์ตœ๋Œ€ํ•œ ์ž์—ฐ์Šค๋Ÿฌ์šด ๋ฌธ์žฅ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋„๋ก ์ž‘์„ฑ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๐Ÿ“–โœ๏ธ๐ŸŒ

๋ฒˆ์—ญ ๊ฐ€์ด๋“œ๋ผ์ธ

  • ๋ชจ๋“  ๋ฒˆ์—ญ๋ฌธ์€ ํ•œ๊ตญ์–ด ์ •์„œ๋ฒ•์„ ์ค€์ˆ˜ํ•ฉ๋‹ˆ๋‹ค.
  • ๋ฒˆ์—ญ์€ ๋ฌธ์„œํ™” ๋‚ด์— ์žˆ๋Š” ๋ณธ๋ฌธ ๋‚ด์šฉ๊ณผ ์ฝ”๋“œ ์ฃผ์„๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ํ•ฉ๋‹ˆ๋‹ค.
  • ๋ฒˆ์—ญ์‹œ ๋ฌธ์žฅ ๋์— ๋ถ™๋Š” ๊ฒฉ์‹์ฒด๋Š” '-ใ…‚๋‹ˆ๋‹ค'์ฒด๋ฅผ ๋”ฐ๋ฅด๋ฉฐ ๋น„์†์–ด๋‚˜ ๋ฐ˜๋ง์€ ์“ฐ์ง€ ์•Š์Šต๋‹ˆ๋‹ค.
  • ํฐ ๋”ฐ์˜ดํ‘œ๋‚˜ ์ž‘์€ ๋”ฐ์˜ดํ‘œ๋Š”('๏ผŒ") ํŠน์ˆ˜๋ฌธ์ž๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ๊ธฐ๋ณธ์ ์œผ๋กœ ์ œ๊ณต๋œ ๊ฒƒ์„ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค.
  • ์ฝ”๋“œ ๊ฐ•์กฐ(syntax highlight) ๋’ค์— ์กฐ์‚ฌ๊ฐ€ ๋ถ™๋Š” ๊ฒฝ์šฐ, ๊ณต๋ฐฑ์„ ๋„ฃ์ง€ ์•Š์Šต๋‹ˆ๋‹ค(e.g. model.fit()์„ ์‹คํ–‰ํ•˜๋ฉด).
  • ํ‚ค์›Œ๋“œ๋ฅผ ๋ฒˆ์—ญํ•  ๋•Œ ์•„๋ž˜์— ์žˆ๋Š” ์ž‘์„ฑ ๊ทœ์น™ ๋ฐ ์šฉ์–ด ํ†ต์ผ์•ˆ์„ ์ตœ์šฐ์„ ์œผ๋กœ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค.
  • ๊ณผํ•œ ๋ณต๋ฌธ์˜ ๊ฒฝ์šฐ ๋‹จ๋ฌธ์œผ๋กœ ๋‚˜๋ˆ„์–ด์„œ ์”๋‹ˆ๋‹ค.
  • ์›๋ฌธ ๋‚ด์šฉ์ด ๋ถˆ์ถฉ๋ถ„ํ•œ ๊ฒฝ์šฐ ์›๋ฌธ์ด ์ „๋‹ฌํ•˜๊ณ ์ž ํ•˜๋Š” ๋‚ด์šฉ์„ ์ถฉ์‹คํžˆ ์ „๋‹ฌํ•˜๋Š” ๋ฒ”์œ„ ๋‚ด์—์„œ ์‚ฌ์šฉ์ž๊ฐ€ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ฐ„๋žตํ•œ ์„ค๋ช…์„ ๋ณด์ถฉํ•ฉ๋‹ˆ๋‹ค.
  • ๋ฒˆ์—ญ์€ ๋‹ค๋ฅธ ์–ธ์–ด๋กœ ๋œ ๋ฌธ์„œ์˜ ์˜๋ฏธ๋ฅผ ์ดํ•ดํ•˜๊ณ  ํ•œ๊ตญ์–ด๋กœ ๋‹ค์‹œ ํ‘œํ˜„ํ•˜๋Š” ๊ฒƒ์ด๋‹ˆ ๋ฒˆ์—ญ์ฒด๋Š” ์ž์ œํ•ด ์ฃผ์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค(์šฐ๋ฆฌ๋Š” ํ•œ๋‹ค ๋ฒˆ์—ญ์„).

์ž‘์„ฑ ๊ทœ์น™

  • ์šฉ์–ด ๋ฒˆ์—ญ์˜ ๊ฒฝ์šฐ ๋ฌธ์„œ ๋‚ด์—์„œ ์ฒ˜์Œ ๋‚˜์˜จ ๊ฒฝ์šฐ์— ํ•œํ•ด subscript๋กœ ์›์–ด๋ฅผ ๋ณ‘ํ–‰ํ‘œ๊ธฐํ•ฉ๋‹ˆ๋‹ค. (์˜ˆ: ์ธตlayer)
    • ๋ฐœ์Œ๋งŒ ํ•œ๊ธ€๋กœ ์˜ฎ๊ธด ๊ฒฝ์šฐ subscript๋Š” ์ƒ๋žตํ•ฉ๋‹ˆ๋‹ค. (์˜ˆ: ์ŠคํŠธ๋ผ์ด๋“œ)
    • ํŠน์ˆ˜ํ•œ ๊ฒฝ์šฐ๋ฅผ ์ œ์™ธํ•˜๋ฉด subscript๋Š” ์†Œ๋ฌธ์ž๋กœ ์ž‘์„ฑํ•ฉ๋‹ˆ๋‹ค. (ํŠน์ˆ˜ํ•œ ๊ฒฝ์šฐ: 1. ๋Œ€๋ฌธ์ž ๊ณ ์œ ๋ช…์‚ฌ ๋ฐ ๋Œ€๋ฌธ์ž ์•ฝ์นญ, 2. ์ œ๋ชฉ์˜ ๊ฒฝ์šฐ ๊ด€์‚ฌ์™€ ์ ‘์†์‚ฌ, ์ „์น˜์‚ฌ๋ฅผ ์ œ์™ธํ•œ ๋‹จ์–ด์™€ ์ œ๋ชฉ ์ฒซ ๋‹จ์–ด์˜ ์ฒซ๊ธ€์ž๋Š” ๋Œ€๋ฌธ์ž๋กœ ์ž‘์„ฑ)
  • list, dict ๋“ฑ ํŒŒ์ด์ฌ ๊ธฐ๋ณธ ์ž๋ฃŒํ˜•์˜ ๊ฒฝ์šฐ ๋ฐœ์Œ๋Œ€๋กœ ํ‘œ๊ธฐํ•˜๊ณ  ์›์–ด๋Š” ๋ณ‘๊ธฐํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค.
  • int, float, integer ๋“ฑ ์ž๋ฃŒํ˜• ํ‚ค์›Œ๋“œ/๋‹จ์–ด์˜ ๊ฒฝ์šฐ
    • ๋ฌธ์žฅ ๋‚ด์— ๋“ฑ์žฅํ•˜๋Š” ๊ฒฝ์šฐ ํ•œ๊ตญ์–ด๋กœ ๋ฒˆ์—ญํ•ฉ๋‹ˆ๋‹ค. (์˜ˆ: "~ is tuple of integers" โ†’ "~๋Š” ์ •์ˆ˜ํ˜• ํŠœํ”Œ์ž…๋‹ˆ๋‹ค.")
    • argument๋“ฑ ๋ณ€์ˆ˜ ์„ค๋ช…์—์„œ ์ž…๋ ฅ๊ฐ’์˜ ์ž๋ฃŒํ˜•์„ ๋‚˜ํƒ€๋‚ด๋Š” ๊ฒฝ์šฐ highlight๋กœ ํ‘œ์‹œํ•˜๊ณ  ํŒŒ์ด์ฌ ์ž๋ฃŒํ˜• ํ‘œ๊ธฐ๋Œ€๋กœ ์ ์Šต๋‹ˆ๋‹ค. (์˜ˆ: X: Integer, โ†’ int.)
  • ๋ฌธ์žฅ ๋์˜ colon(:)์€ ๋งˆ์นจํ‘œ๋กœ ๋Œ€์ฒดํ•ฉ๋‹ˆ๋‹ค.
    • ๋ฌธ์žฅ ๋์˜ semicolon(;)์€ ๋ฌธ์žฅ์„ ๋‘ ๊ฐœ๋กœ ๋ถ„๋ฆฌํ•˜๊ณ  ํ•„์š”ํ•œ ๊ฒฝ์šฐ ์ ํ•ฉํ•œ ์ ‘์†์‚ฌ๋ฅผ ์ถ”๊ฐ€ํ•ฉ๋‹ˆ๋‹ค.
  • Keras๋ฅผ ์ œ์™ธํ•œ ๋ชจ๋“  API ๋ฐ ์„œ๋น„์Šค ๋“ฑ์˜ ์ด๋ฆ„(TensorFlow, NumPy, CNTK, Amazon, Google ๋“ฑ)์€ ์›๋ฌธ ๊ทธ๋Œ€๋กœ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค
  • ํ•จ์ˆ˜ ์ธ์ž ์„ค๋ช…์‹œ [์ธ์ž: data type, ์„ค๋ช… ๋‚ด์šฉ, ๊ธฐ๋ณธ๊ฐ’ ]์˜ ํ˜•์‹์„ ๋”ฐ๋ฆ…๋‹ˆ๋‹ค. (์˜ˆ: batch_size: int ํ˜น์€ None. ์†์‹ค๋กœ๋ถ€ํ„ฐ ๊ทธ๋ž˜๋””์–ธํŠธ๋ฅผ ๊ตฌํ•˜๊ณ  ๊ฐ€์ค‘์น˜๋ฅผ ์—…๋ฐ์ดํŠธํ•˜๋Š” ๊ณผ์ • ํ•œ ๋ฒˆ์— ์‚ฌ์šฉํ•  ํ‘œ๋ณธ์˜ ๊ฐœ์ˆ˜์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ 32์ž…๋‹ˆ๋‹ค.)
  • Raises๋ž€์˜ ๊ฒฝ์šฐ ์˜ค๋ฅ˜๋กœ ๋ฒˆ์—ญํ•˜๋ฉฐ, ๋ณธ๋ฌธ์€ "(~ํ•˜๋Š” ๊ฒฝ์šฐ, ~ํ•˜๋ฉด, ~๊ฐ€) ๋ฐœ์ƒํ•ฉ๋‹ˆ๋‹ค."๋กœ ์ •๋ฆฌํ•ฉ๋‹ˆ๋‹ค.

์šฉ์–ด ํ†ต์ผ์•ˆ

English ํ•œ๊ตญ์–ด
-er ~ํ™” ํ•จ์ˆ˜ / ํ•จ์ˆ˜
1--9 1--9
accuracy ์ •ํ™•๋„
argument ์ธ์ž
(artificial) neural network (์ธ๊ณต) ์‹ ๊ฒฝ๋ง
augmenter ์ฆ๊ฐ• ํ•จ์ˆ˜
Average Pooling ํ‰๊ท  ํ’€๋ง
axis ์ถ•
batch ๋ฐฐ์น˜
bias ํŽธํ–ฅ
binary classification ์ด์ง„ ๋ถ„๋ฅ˜
cache ์บ์‹œ
callback ์ฝœ๋ฐฑ
cell state ์…€ ์ƒํƒœ
channel ์ฑ„๋„
checkpoint ์ฒดํฌํฌ์ธํŠธ
class ํด๋ž˜์Šค
classification ๋ถ„๋ฅ˜
compile ์ปดํŒŒ์ผ
constraint ์ œ์•ฝ
convolutional neural network (CNN) ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง
corpus ๋ง๋ญ‰์น˜
dense layer ์™„์ „์—ฐ๊ฒฐ์ธต
dimension ์ฐจ์›
dot product ๋‚ด์ 
dropout ๋“œ๋กญ์•„์›ƒ
element-wise ์›์†Œ๋ณ„
embedding ์ž„๋ฒ ๋”ฉ
encoding ์ธ์ฝ”๋”ฉ
epoch ์—ํญ (์„œ์ˆ ์ ์œผ๋กœ ์“ธ ๋•Œ๋Š” 'nํšŒ ๋ฐ˜๋ณต')
factor ๊ฐ’/์š”์ธ/์š”์†Œ
fully-connected, densely connected ์™„์ „ ์—ฐ๊ฒฐ
global ์ „์—ญ
generator ์ œ๋„ˆ๋ ˆ์ดํ„ฐ
gradient ๊ทธ๋ž˜๋””์–ธํŠธ
gradient ascent ๊ฒฝ์‚ฌ์ƒ์Šน๋ฒ•
gradient descent ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ•
hidden unit ์€๋‹‰ ์œ ๋‹›
hidden layer ์€๋‹‰ ์ธต
hidden state ์€๋‹‰ ์ƒํƒœ
hyperparameter ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ
identity matrix ๋‹จ์œ„ ํ–‰๋ ฌ
index ์ธ๋ฑ์Šค (๊ฐœ๋ณ„ index์˜ ๋ฌถ์Œ ์ „์ฒด๋ฅผ ๊ฐ€๋ฆฌํ‚ฌ ๋•Œ๋Š” '๋ชฉ๋ก')
input ์ž…๋ ฅ/์ž…๋ ฅ๊ฐ’
instance ์ธ์Šคํ„ด์Šค
initialization ์ดˆ๊ธฐ๊ฐ’ ์ƒ์„ฑ
initializer ์ดˆ๊ธฐํ™” ํ•จ์ˆ˜
keras ์ผ€๋ผ์Šค
kernel ์ปค๋„
label ๋ ˆ์ด๋ธ”
layer ์ธต
learning rate ํ•™์Šต๋ฅ 
learning rate decay ํ•™์Šต๋ฅ  ๊ฐ์†Œ
locally ๋ถ€๋ถ„ ์—ฐ๊ฒฐ
loss function ์†์‹ค ํ•จ์ˆ˜
LSTM LSTM
MaxPooling ์ตœ๋Œ“๊ฐ’ ํ’€๋ง
mean squared error (MSE) ํ‰๊ท  ์ œ๊ณฑ ์˜ค์ฐจ(๋ฒ•)
metric (ํ‰๊ฐ€) ์ง€ํ‘œ (๋ฌธ๋งฅ์— ๋”ฐ๋ผ ์œ ์—ฐํ•˜๊ฒŒ ์‚ฌ์šฉ)
mini-batch ๋ฏธ๋‹ˆ ๋ฐฐ์น˜
model ๋ชจ๋ธ
momentum ๋ชจ๋ฉ˜ํ…€
multi-class classification ๋‹ค์ค‘ ๋ถ„๋ฅ˜
multilayer perceptron (MLP) ๋‹ค์ธต ํผ์…‰ํŠธ๋ก 
neuron ๋‰ด๋Ÿฐ
node ๋…ธ๋“œ
noise ๋…ธ์ด์ฆˆ
non-negativity ์Œ์ด ์•„๋‹Œ ~
norm ๋…ธ๋ฆ„
normalization ์ •๊ทœํ™”
normalize ์ •๊ทœํ™”ํ•˜๋‹ค
note ์ฐธ๊ณ 
objective function ๋ชฉ์  ํ•จ์ˆ˜
one-hot encoding ์›-ํ•ซ ์ธ์ฝ”๋”ฉ
optimizer ์ตœ์ ํ™” ํ•จ์ˆ˜
output ์ถœ๋ ฅ(๊ฐ’)
padding ํŒจ๋”ฉ
parameter (ํ•จ์ˆ˜์˜)๋งค๊ฐœ๋ณ€์ˆ˜
parameter (๋ชจ๋ธ์˜)ํŒŒ๋ผ๋ฏธํ„ฐ (๊ฐ€์ค‘์น˜์™€ ํŽธํ–ฅ์„ ํ•จ๊ป˜ ์ด๋ฅด๋Š” ๋ง)
placeholder ํ”Œ๋ ˆ์ด์Šคํ™€๋”
penalty ํŽ˜๋„ํ‹ฐ
pooling ํ’€๋ง
precision ์ •๋ฐ€๋„
queue ๋Œ€๊ธฐ์—ด
recurrent neural network (RNN) ์ˆœํ™˜ ์‹ ๊ฒฝ๋ง
reference ์ฐธ๊ณ 
regression ํšŒ๊ท€ ๋ถ„์„
regression(-ive) model ํšŒ๊ท€ ๋ชจ๋ธ
regularize(-er) ๊ทœ์ œํ™”/๊ทœ์ œ ํ•จ์ˆ˜
repository ์ €์žฅ์†Œ
reshape ํ˜•ํƒœ๋ฐ”๊พธ๊ธฐ
return ๋ฐ˜ํ™˜๊ฐ’
root mean squared error (RMSE) ํ‰๊ท  ์ œ๊ณฑ๊ทผ ์˜ค์ฐจ(๋ฒ•)
sample ํ‘œ๋ณธ
sequence (-tial) ์ˆœ์„œํ˜•
set ์„ธํŠธ
shape ํ˜•ํƒœ
stack ์ธต์„ ์Œ“๋‹ค
stateful ์ƒํƒœ ์ €์žฅ
stochastic gradient descent ํ™•๋ฅ ์  ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ•
stride ์ŠคํŠธ๋ผ์ด๋“œ
target ๋ชฉํ‘œ(๊ฐ’)
temporal ์‹œ๊ณ„์—ด
tensor ํ…์„œ
test ์‹œํ—˜
text ํ…์ŠคํŠธ
timestep ์‹œ๊ฐ„ ๋‹จ๊ณ„/์ˆœ์„œ
token ํ† ํฐ
train (๋ฐ์ดํ„ฐ์˜ ๊ฒฝ์šฐ) ํ›ˆ๋ จ ์„ธํŠธ / (๋™์ž‘์˜ ๊ฒฝ์šฐ) ํ•™์Šต์‹œํ‚ค๋‹ค
utility ๋„๊ตฌ
validation ๊ฒ€์ฆ
weight ๊ฐ€์ค‘์น˜
wrapper ๋ž˜ํผ