๋ณธ๋ฌธ ๋ฐ”๋กœ๊ฐ€๊ธฐ

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3/16 ์ˆ˜ ์ˆ˜์š”์ผ! ์–ด์ œ~์˜ค๋Š˜๊นŒ์ง€ Numpy! ์˜ค๋Š˜ ์˜คํ›„~์ด๋ฒˆ ์ฃผ๊นŒ์ง€ Pandas ์ง„๋„! Anaconda Prompt์—์„œ Jupyter notebook ์‹คํ–‰ conda activate machine jupyter notebook 1. ํ–‰๋ ฌ๊ณฑ ์—ฐ์‚ฐ์€ ์•ž์ชฝ์˜ 2์ฐจ์› matrix ์—ด๊ณผ ๋’ค์ชฝ์˜ 2์ฐจ์› matrix ํ–‰ ๊ฐœ์ˆ˜๊ฐ€ ๊ฐ™์•„์•ผ ํ•จ. (3, 2) * (2, 2) import numpy as np arr1 = np.array([[1,2,3], [4,5,6]]) # (2,3) arr2 = np.array([[4,5],[6,7],[8,9]]) # (3,2) print(np.matmul(arr1, arr2)) # matmul() ํ•จ์ˆ˜ ์‚ฌ์šฉํ•ด์„œ ๊ณ„์‚ฐ. ๊ฒฐ๊ณผ๋Š” (2,2) # [[ 40 46] # [ 94 109]] 2. ์ „์น˜ํ–‰๋ ฌ(.. ๋”๋ณด๊ธฐ
3/15 ํ™” ํ™”์š”์ผ! ์šฐ์˜ค์˜ค์˜ค ๋“œ๋””์–ด ์ „๊ณต์ธ ๋จธ์‹ ๋Ÿฌ๋‹ ํ•™์Šต ์‹œ์ž‘! ์ˆ˜์—… ํ›„์—” ํ˜„์—… ๊ฐœ๋ฐœ์ž(๋ฌด๋ ค ์—”์”จ์†Œํ”„ํŠธ, ๋„ฅ์Šจ์ฝ”๋ฆฌ์•„๋ฅผ ๊ฑฐ์ณ ์ง๋ฐฉ)์˜ ์ทจ์—…ํŠน๊ฐ•์ด ์žˆ๋‹ค~ '์ปค๋ฆฌ์–ดํŒจ์Šค ๊ฐ€์ด๋“œ' : ์ปค๋ฆฌ์–ด ๋งต, ๊ธฐ์—…๋ณ„ ์žฅ๋‹จ์ , ํ˜„์—… ๊ฐœ๋ฐœ์ž์˜ ์กฐ์–ธ ๋ฐ์ดํ„ฐ์˜ ์ˆ˜์ง‘ · ๊ฐ€๊ณต(Data Handling)์ด Machine Learning์˜ 60~80%๋ฅผ ์ฐจ์ง€ํ•จ ์ •์ œ๋œ ๋ฐ์ดํ„ฐ → ์ •ํ™•ํ•œ ๊ฒฐ๊ณผ 1. Anaconda Prompt์—์„œ Jupyter notebook ์‹คํ–‰ 2022.01.18 - [๋ฉ€ํ‹ฐ์บ ํผ์Šค ํ”„๋กœ์ ํŠธํ˜• AI ์„œ๋น„์Šค ๊ฐœ๋ฐœ 5ํšŒ์ฐจ/Python] - 1/18 ํ™” 2. Python module ์ค‘ Pandas๊ฐ€ data handling์— ์‚ฌ์šฉ๋จ ← data๋ฅผ ์‚ฌ์šฉํ•˜๊ธฐ์— ์š”๊ธดํ•˜๊ฒŒ ๋งŒ๋“ค์–ด์คŒ Pandas์˜ data-type์„ ๊ตฌ์„ฑํ•˜๊ณ  ์žˆ๋Š” Numpy mo.. ๋”๋ณด๊ธฐ

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