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Docker ํ™˜๊ฒฝ์—์„œ ROS ์„ค์น˜

์—ฌ๋Ÿฌ ๋ธ”๋กœ๊ทธ๋ฅผ ์ฐธ๊ณ ํ•ด ROS ์ปจํ…Œ์ด๋„ˆ๋ฅผ ์„ค์น˜ํ•ด๋ดค๋Š”๋ฐ ์ž˜ ๋˜์ง€ ์•Š์•˜๋‹ค.โžก๏ธ Ubuntu 18.04 ์ปจํ…Œ์ด๋„ˆ๋ฅผ ์„ค์น˜ํ•ด์„œ ์—ฌ๊ธฐ์— ROS๋ฅผ ์„ค์น˜ํ–ˆ๋‹ค.docker images -a๋ฅผ ์ž…๋ ฅํ•ด ํ˜„์žฌ ๊ฐ€์ง„ ์ด๋ฏธ์ง€๋“ค์„ ํ™•์ธํ•ด๋ณด์ž!์‹คํŒจํ•œ ros ์ด๋ฏธ์ง€๋“ค๊ณผ docker๋ฅผ ์ฒ˜์Œ ๊น”์•˜์„ ๋•Œ

์–ด์ œ
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Windows์— Docker ์„ค์น˜

๋จผ์ € Docker๋ฅผ ์„ค์น˜ํ•ด๋ณด์ž! (ํ•„์ž๋Š” Windows๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค.)Install Docker Desktop on Windows ์‚ฌ์ดํŠธ์— ๋“ค์–ด๊ฐ„๋‹ค.์‚ฌ์ดํŠธ์— ๋“ค์–ด๊ฐ€๋ฉด ์•„๋ž˜ ์‚ฌ์ง„๊ณผ ๊ฐ™์€ ํ™”๋ฉด์ด ๋‚˜์˜ค๋Š”๋ฐ, ์—ฌ๊ธฐ์„œ ํŒŒ๋ž€์ƒ‰ ๋ฒ„ํŠผ "Docker Desktop for Windows

2์ผ ์ „
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[ROS] 11. SLAM๊ณผ ๋‚ด๋น„๊ฒŒ์ด์…˜

SLAM (Simultaneous Localization and Mapping)

2022๋…„ 5์›” 18์ผ
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[PyTorch] Transfer Learning

์ฐธ๊ณ  ์‚ฌ์ดํŠธcs231n์—์„œ ๋งํ•˜๋Š” ์ „์ดํ•™์Šต(๋ฌด์ž‘์œ„ ์ดˆ๊ธฐํ™”๋ฅผ ํ†ตํ•ด) ๋งจ ์ฒ˜์Œ๋ถ€ํ„ฐ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง(Convolutional Network) ์ „์ฒด๋ฅผ ํ•™์Šตํ•˜๋Š” ์‚ฌ๋žŒ์€ ๋งค์šฐ ์ ๋‹ค. ์ถฉ๋ถ„ํ•œ ํฌ๊ธฐ์˜ ๋ฐ์ดํ„ฐ์…‹์„ ๊ฐ–์ถ”๊ธฐ๋Š” ์‹ค์ œ๋กœ ๋“œ๋ฌผ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค.ํ•˜์ง€๋งŒ ๋งค์šฐ ํฐ ๋ฐ์ดํ„ฐ์…‹(ex. 100๊ฐ€์ง€

2022๋…„ 5์›” 3์ผ
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[PyTorch] Training a Classifier

colab(https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/\_downloads/17a7c7cb80916fcdf921097825a0f562/cifar10_tutorial.ipyn

2022๋…„ 5์›” 2์ผ
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3-2. DFS/BFS ๊ธฐ์ถœ ๋ฌธ์ œ

์–ด๋–ค ๋‚˜๋ผ์—๋Š” 1~N๋ฒˆ๊นŒ์ง€์˜ ๋„์‹œ์™€ M๊ฐœ์˜ ๋‹จ๋ฐฉํ–ฅ ๋„๋กœ๊ฐ€ ์กด์žฌํ•œ๋‹ค. ๋ชจ๋“  ๋„๋กœ์˜ ๊ฑฐ๋ฆฌ๋Š” 1์ด๋‹ค. ์ด๋•Œ ํŠน์ •ํ•œ ๋„์‹œ X๋กœ๋ถ€ํ„ฐ ์ถœ๋ฐœํ•˜์—ฌ ๋„๋‹ฌํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋“  ๋„์‹œ ์ค‘์—์„œ, ์ตœ๋‹จ ๊ฑฐ๋ฆฌ๊ฐ€ ์ •ํ™•ํžˆ K์ธ ๋ชจ๋“  ๋„์‹œ์˜ ๋ฒˆํ˜ธ๋ฅผ ์ถœ๋ ฅํ•˜๋Š” ํ”„๋กœ๊ทธ๋žจ์„ ์ž‘์„ฑํ•˜์‹œ์˜ค. ๋˜ํ•œ ์ถœ๋ฐœ ๋„์‹œ X์—์„œ

2022๋…„ 4์›” 28์ผ
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7. Training Neural Networks โ…ก

์šฐ๋ฆฌ๋Š” ์ง€๋‚œ์‹œ๊ฐ„์— 6๊ฐœ์˜ activation function์„ ๋ฐฐ์› ๋‹ค. ์ด์ค‘์—์„œ Sigmoid์™€ ReLU๋งŒ ๋‹ค์‹œ ๋ด๋ณด์ž!Sigmoid๋Š” ๊ณผ๊ฑฐ์— ์œ ๋ช…ํ–ˆ์ง€๋งŒ Vanishing Gradients์˜ ๋ฌธ์ œ์  ๋•Œ๋ฌธ์— ์ด์ œ๋Š” ์ž˜ ์“ฐ์ง€ ์•Š๋Š”๋‹ค.์ด์ œ๋Š” ReLU๋ฅผ ์“ฐ๋Š” ๊ฒƒ์ด ๊ฐ€์žฅ ์ข‹์€

2022๋…„ 4์›” 28์ผ
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8-1. ๊ทธ๋ž˜ํ”„ ์ด๋ก  ๊ฐœ๋… & ์‹ค์ „ ๋ฌธ์ œ

๋ณต์Šต ์„œ๋กœ์†Œ ์ง‘ํ•ฉ ์„œ๋กœ์†Œ ์ง‘ํ•ฉ ์ž๋ฃŒ๊ตฌ์กฐ ๊ธฐ๋ณธ์ ์ธ ์„œ๋กœ์†Œ ์ง‘ํ•ฉ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์†Œ์Šค์ฝ”๋“œ ๊ฒฝ๋กœ ์••์ถ• ๊ธฐ๋ฒ• ์†Œ์Šค์ฝ”๋“œ ์„œ๋กœ์†Œ ์ง‘ํ•ฉ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์‹œ๊ฐ„ ๋ณต์žก๋„ ์„œ๋กœ์†Œ ์ง‘ํ•ฉ์„ ํ™œ์šฉํ•œ ์‚ฌ์ดํด ํŒ๋ณ„ ์„œ๋กœ์†Œ ์ง‘ํ•ฉ์„ ํ™œ์šฉํ•œ ์‚ฌ์ดํด ํŒ๋ณ„ ์†Œ์Šค์ฝ”๋“œ -- ์‹ ์žฅ ํŠธ๋ฆฌ ํฌ๋ฃจ์Šค์นผ ์•Œ๊ณ ๋ฆฌ์ฆ˜

2022๋…„ 4์›” 19์ผ
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7-1. ์ตœ๋‹จ๊ฒฝ๋กœ ๊ฐœ๋… & ์‹ค์ „ ๋ฌธ์ œ

๊ฐ€์žฅ ์งง์€ ๊ฒฝ๋กœ๋ฅผ ์ฐพ๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜'๊ธธ ์ฐพ๊ธฐ' ๋ฌธ์ œ๋ผ๊ณ ๋„ ๋ถ€๋ฆ„์‚ฌ๋ก€ํ•œ ์ง€์ ์—์„œ ๋‹ค๋ฅธ ํŠน์ • ์ง€์ ๊นŒ์ง€์˜ ์ตœ๋‹จ ๊ฒฝ๋กœ๋ฅผ ๊ตฌํ•ด์•ผ ํ•˜๋Š” ๊ฒฝ์šฐ๋ชจ๋“  ์ง€์ ์—์„œ ๋‹ค๋ฅธ ๋ชจ๋“  ์ง€์ ๊นŒ์ง€์˜ ์ตœ๋‹จ ๊ฒฝ๋กœ๋ฅผ ๋ชจ๋‘ ๊ตฌํ•ด์•ผ ํ•˜๋Š” ๊ฒฝ์šฐ๋“ฑ๋“ฑ๐Ÿ“Œ ์‹ค์ œ ์ฝ”๋”ฉ ํ…Œ์ŠคํŠธ์—์„œ๋Š” ์ตœ๋‹จ ๊ฒฝ๋กœ๋ฅผ ๋ชจ๋‘ ์ถœ๋ ฅํ•˜๋Š” ๋ฌธ์ œ๋ณด๋‹ค๋Š” ๋‹จ์ˆœ

2022๋…„ 4์›” 14์ผ
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[๋ฐ‘๋”ฅ] 3์žฅ. ์‹ ๊ฒฝ๋ง

ํผ์…‰ํŠธ๋ก ์—์„œ ์‹ ๊ฒฝ๋ง์œผ๋กœ

2022๋…„ 4์›” 10์ผ
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6. Training Neural Networks, Part 1

http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture6.pdfhttps://inhovation97.tistory.com/23One time setupactivation functionspreproce

2022๋…„ 4์›” 8์ผ
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[๋ฐ‘๋”ฅ] 2์žฅ. ํผ์…‰ํŠธ๋ก  (Perceptron)

ํผ์…‰ํŠธ๋ก ์€ ์‹ ๊ฒฝ๋ง(๋”ฅ๋Ÿฌ๋‹)์˜ ๊ธฐ์›์ด ๋˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜โžก๏ธ ํผ์…‰ํŠธ๋ก ์˜ ๊ตฌ์กฐ๋ฅผ ๋ฐฐ์šฐ๋Š” ๊ฒƒ์€ ์‹ ๊ฒฝ๋ง๊ณผ ๋”ฅ๋Ÿฌ๋‹์œผ๋กœ ๋‚˜์•„๊ฐ€๋Š” ๋ฐ ์ค‘์š”ํ•œ ์•„์ด๋””์–ด๋ฅผ ๋ฐฐ์šฐ๋Š” ๊ฒƒ!: ๋‹ค์ˆ˜์˜ ์‹ ํ˜ธ๋ฅผ ์ž…๋ ฅ์œผ๋กœ ๋ฐ›์•„ ํ•˜๋‚˜์˜ ์‹ ํ˜ธ๋ฅผ ์ถœ๋ ฅ์ „๋ฅ˜๊ฐ€ ์ „์„ ์„ ํƒ€๊ณ  ํ๋ฅด๋Š” ์ „์ž๋ฅผ ๋‚ด๋ณด๋‚ด๋“ฏ, ํผ์…‰ํŠธ๋ก  ์‹ ํ˜ธ๋„ ํ๋ฆ„์„ ๋งŒ๋“ค๊ณ 

2022๋…„ 4์›” 7์ผ
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[๋ฐ‘๋”ฅ] 1์žฅ. ํ—ฌ๋กœ ํŒŒ์ด์ฌ

2022๋…„ 4์›” 7์ผ
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6-1. ๋‹ค์ด๋‚˜๋ฏน ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๊ฐœ๋… & ์‹ค์ „ ๋ฌธ์ œ

์ปดํ“จํ„ฐ๋ฅผ ํ™œ์šฉํ•ด๋„ ํ•ด๊ฒฐํ•˜๊ธฐ ์–ด๋ ค์šด ๋ฌธ์ œ์ตœ์ ์˜ ํ•ด๋ฅผ ๊ตฌํ•˜๊ธฐ์— ์‹œ๊ฐ„์ด ๋งค์šฐ ๋งŽ์ด ํ•„์š”๋ฉ”๋ชจ๋ฆฌ ๊ณต๊ฐ„์ด ๋งค์šฐ ๋งŽ์ด ํ•„์š”๐Ÿ’ก ํ•˜์ง€๋งŒ ์–ด๋–ค ๋ฌธ์ œ๋Š” ๋ฉ”๋ชจ๋ฆฌ ๊ณต๊ฐ„์„ ์•ฝ๊ฐ„ ๋” ์‚ฌ์šฉํ•˜๋ฉด ์—ฐ์‚ฐ ์†๋„๋ฅผ ๋น„์•ฝ์ ์œผ๋กœ ์ฆ๊ฐ€์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค.โžก๏ธ ๋‹ค์ด๋‚˜๋ฏน ํ”„๋กœ๊ทธ๋ž˜๋ฐ (๋™์  ๊ณ„ํš๋ฒ•)Q. ๋‹ค์ด๋‚˜๋ฏน ํ”„๋กœ๊ทธ๋ž˜

2022๋…„ 4์›” 6์ผ
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5-1. ์ด์ง„ํƒ์ƒ‰ ๊ฐœ๋… & ์‹ค์ „ ๋ฌธ์ œ

์ด๋ฒˆ ์žฅ์—์„œ๋Š” ๋ฆฌ์ŠคํŠธ ๋‚ด์—์„œ ๋ฐ์ดํ„ฐ๋ฅผ ๋งค์šฐ ๋น ๋ฅด๊ฒŒ ํƒ์ƒ‰ํ•˜๋Š” ์ด์ง„ ํƒ์ƒ‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•ด ๋‹ค๋ฃฌ๋‹ค. ์ด์ง„ ํƒ์ƒ‰์„ ์•Œ์•„๋ณด๊ธฐ ์ „์— ๊ฐ€์žฅ ๊ธฐ๋ณธ ํƒ์ƒ‰ ๋ฐฉ๋ฒ•์ธ ์ˆœ์ฐจ ํƒ์ƒ‰์„ ๊ณต๋ถ€ํ•ด๋ณด์ž! ์ˆœ์ฐจ ํƒ์ƒ‰ (Sequential Search) ๋ฆฌ์ŠคํŠธ ์•ˆ์— ์žˆ๋Š” ํŠน์ •ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์ฐพ๊ธฐ ์œ„ํ•ด

2022๋…„ 4์›” 5์ผ
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5. Convolutional Neural Networks

1957๋…„ Frank Rosenblatt๊ฐ€ Mark I Perceptron machine์„ ๊ฐœ๋ฐœ์ตœ์ดˆ์˜ ํผ์…‰ํŠธ๋ก  ๊ธฐ๊ณ„1960๋…„ Widrow์™€ Hoff๊ฐ€ Adaline and Madaline ๊ฐœ๋ฐœ์ตœ์ดˆ์˜ Multilayer Perceptron Network1986๋…„ Rume

2022๋…„ 4์›” 4์ผ
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4-1. ์ •๋ ฌ ๊ฐœ๋… & ์‹ค์ „ ๋ฌธ์ œ

์ •๋ ฌ(Sorting) : ๋ฐ์ดํ„ฐ๋ฅผ ํŠน์ •ํ•œ ๊ธฐ์ค€์— ๋”ฐ๋ผ์„œ ์ˆœ์„œ๋Œ€๋กœ ๋‚˜์—ดํ•˜๋Š” ๊ฒƒโžก๏ธ ์ •๋ ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ด์ง„ ํƒ์ƒ‰(Binary Search)์˜ ์ „์ฒ˜๋ฆฌ ๊ณผ์ • โžก๏ธ ๋ฐ์ดํ„ฐ๋ฅผ ์ •๋ ฌํ•˜๋ฉด ์ด์ง„ ํƒ์ƒ‰ ๊ฐ€๋Šฅ์ด ์ฑ…์—์„œ๋Š” ๋‹ค์Œ์˜ ์ •๋ ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๋‹ค๋ฃฌ๋‹ค.์„ ํƒ ์ •๋ ฌ์‚ฝ์ž… ์ •๋ ฌํ€ต ์ •๋ ฌ๊ณ„์ˆ˜ ์ •๋ ฌํŒŒ์ด์ฌ

2022๋…„ 3์›” 31์ผ
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3-1. DFS/BFS ๊ฐœ๋… & ์‹ค์ „ ๋ฌธ์ œ

1. ์ž๋ฃŒ๊ตฌ์กฐ ๊ธฐ์ดˆ ํƒ์ƒ‰

2022๋…„ 3์›” 31์ผ
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