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๐Ÿค” DeepIntoDeep ์†Œ๊ฐœ

DeepIntoDeep์€ AIKU๋งŒ์˜ ์ฒด๊ณ„์ ์ธ ์ปค๋ฆฌํ˜๋Ÿผ์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ์ฃผ๋‹ˆ์–ด ํ•™ํšŒ์› ๋Œ€์ƒ ๋”ฅ๋Ÿฌ๋‹ ๋ถ€ํŠธ์บ ํ”„์ž…๋‹ˆ๋‹ค.

DeepIntoDeep์—์„œ๋Š” ๋”ฅ๋Ÿฌ๋‹ ์ž…๋ฌธ์ž๋ผ๋ฉด ๊ผญ ์•Œ์•„์•ผ ํ•  ๊ธฐ๋ณธ์ ์ธ ๋”ฅ๋Ÿฌ๋‹ ์ด๋ก ๊ณผ ๊ฐœ๋…๋ถ€ํ„ฐ, ๋‹ค์–‘ํ•œ ๋”ฅ๋Ÿฌ๋‹ ์‘์šฉ ๋ฐฉ๋ฒ•๋ก ๊นŒ์ง€ ํญ๋„“์€ ์ฃผ์ œ๋ฅผ ๋ฐ€๋„ ์žˆ๊ฒŒ ๋‹ค๋ฃน๋‹ˆ๋‹ค.

DeepIntoDeep ๊ฐ•์˜๋Š” AIKU ๊ณต์‹ ์œ ํŠœ๋ธŒ ๋ฐ Github ํŽ˜์ด์ง€์—์„œ ๊ฐ•์˜ ์˜์ƒ๊ณผ ๊ฐ•์˜ ์ž๋ฃŒ๋ฅผ ํ™•์ธํ•˜์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค!

๋ฐฉํ•™ ์ค‘ ํ™”,๋ชฉ์— ์ง„ํ–‰๋˜๋Š” DeepIntoDeep์—์„œ๋Š” AIKU ์‹œ๋‹ˆ์–ด ๋ฐ OB ํ•™ํšŒ์›๋“ค์ด ์ง์ ‘ ๊ฐ•์˜ ์ž๋ฃŒ์™€ ๊ณผ์ œ๋ฅผ ์ค€๋น„ํ•˜์—ฌ ์šด์˜ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์–‘์งˆ์˜ ๊ณผ์ œ์™€ ๋ฐ€์ฐฉ ์งˆ์˜์‘๋‹ต์„ ํ†ตํ•ด ์ƒํ˜ธ ์„ฑ์žฅ์„ ๋„๋ชจํ•ด ๊ฐ‘๋‹ˆ๋‹ค.

Untitled

๐Ÿš€ DeepIntoDeep์—์„œ๋Š” ๋ฌด์—‡์„ ํ•˜๋‚˜์š”?

๐ŸŸก ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ์ดˆ ์ด๋ก  ๋ฐ ์‘์šฉ ๊ด€๋ จ ํ•™์Šต


๋”ฅ๋Ÿฌ๋‹ ์ž…๋ฌธ ๋‹จ๊ณ„์ธ AIKU ์ฃผ๋‹ˆ์–ด ํ•™ํšŒ์›๋“ค์„ ๋Œ€์ƒ์œผ๋กœ, ๋”ฅ๋Ÿฌ๋‹์„ ๊ณต๋ถ€ํ•˜๋Š” ๋ฐ ์žˆ์–ด ๊ผญ ํ•„์š”ํ•œ ๊ธฐ์ดˆ ์ด๋ก ๊ณผ, ์—ฌ๊ธฐ์„œ ํ•œ ๋ฐœ์ง ๋‚˜์•„๊ฐ„ ์‘์šฉ ๋‹จ๊ณ„๊นŒ์ง€ ์ฒด๊ณ„์ ์œผ๋กœ ๋‹ค๋ฃฌ ๊ฐ•์˜๊ฐ€ ์ œ๊ณต๋ฉ๋‹ˆ๋‹ค. ๊ฐ•์˜๋Š” ์ง์ ‘ ๊ธฐ์ดˆ ์ด๋ก ๋ถ€ํ„ฐ ๊ณต๋ถ€ํ•ด ์˜จ AIKU ์‹œ๋‹ˆ์–ด ๋ฐ OB ํ•™ํšŒ์›๋“ค์ด, ์ฃผ๋‹ˆ์–ด ํ•™ํšŒ์›๋“ค์˜ ์ˆ˜์ค€์„ ์„ธ์‹ฌํ•˜๊ฒŒ ๊ณ ๋ คํ•˜์—ฌ ์ค€๋น„ํ•ฉ๋‹ˆ๋‹ค.

<aside> ๐Ÿ’ก DeepIntoDeep ์ปค๋ฆฌํ˜๋Ÿผ ์˜ˆ์‹œ(2024 ํ•˜๋ฐ˜๊ธฐ ๊ธฐ์ค€)

Lecture 1 Machine Learning and Multi-layer Perceptrons, Pytorch tutorial 1
Lecture 2 Training Techniques, Pytorch tutorial 2
Lecture 3 Convolutional Neural Networks
Lecture 4 Recurrent Neural Networks
Lecture 5 Attention Mechanism (w/Machine Translation)
Lecture 6 Transformers
Lecture 7 Transformers in NLP
Lecture 8 Transformers in CV
Lecture 9 Object Detection
Lecture 10 Segmentation
Lecture 11 Various NLP tasks
Lecture 12 Image Generative Models
Lecture 13 Large Language Models
Lecture 14 Multimodality
</aside>

๐ŸŸก ๊ฐ•์˜ ๊ด€๋ จ ์ž์ฒด ์ œ์ž‘ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๊ณผ์ œ ์ œ๊ณต


DeepIntoDeep ๊ฐ•์˜๋ฅผ ๋งก์€ AIKU ํ•™ํšŒ์›๋“ค์ด ์ง์ ‘ ๊ฐ•์˜ ๋‚ด์šฉ๊ณผ ๊ด€๋ จ๋œ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๊ณผ์ œ๋ฅผ ์ถœ์ œํ•ฉ๋‹ˆ๋‹ค. ์ฃผ๋‹ˆ์–ด ํ•™ํšŒ์›๋“ค์ด ์ด๋ก ์— ๋Œ€ํ•œ ๊ธฐ๋ณธ์ ์ธ ์ดํ•ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ์ง์ ‘ ์ฝ”๋“œ๋ฅผ ๊ตฌํ˜„ํ•˜๋ฉฐ ์ž‘๋™ ์›๋ฆฌ๋ฅผ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ์‚ผ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.

๐ŸŸก ๋ฌด์—‡์ด๋“  ๋ฌผ์–ด๋ณด์„ธ์š”