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์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ @UCtrFqEyYdDvpVF4j_r9Z6_A@youtube.com

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29:27
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์ธ๊ณต์ง€๋Šฅ ์ž…๋ฌธ
33:55
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] Style GAN Review
47:53
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์ธ๊ณต์ง€๋Šฅ ๋น„์ง€๋‹ˆ์Šค (์ˆ˜์ต์œผ๋กœ ์—ฐ๊ฒฐ๋˜๋Š” ์ธ๊ณต์ง€๋Šฅ)
47:19
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์Šค๋งˆํŠธํฐ์„ ์ด์šฉํ•œ ์Šค๋งˆํŠธํŒฉํ† ๋ฆฌ ์‹œ์ž‘ํ•˜๊ธฐ
46:40
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] Attention์„ ํ™œ์šฉํ•œ ๋ณต์žก๊ณ„์˜ ํ•ด์„
51:06
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์ธ๊ณต์ง€๋Šฅ ์ฐฝ์—…, ์–ด๋–ป๊ฒŒ ํ• ๊ฒƒ์ธ๊ฐ€?
06:42
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] โ€œ์ง€์„ฑ์ธ, ์—ฌ๋Ÿฌ๋ถ„ ์ด์ œ ์›€์ง์ผ๋•Œ ์ž…๋‹ˆ๋‹ค.โ€
57:15
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ณผํ•™์ž๊ฐ€ ๋ณธ ์—ญ์‚ฌ์™€ ์ฃผ์ฒด์‚ฌ์ƒ
26:17
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (35), Eligibility traces ์†Œ๊ฐœ, Reinforcement learning
25:19
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (34), policy Gradient and Actor Critic, Reinforcement learning
29:03
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (33), ์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ ๊ฐ•ํ™”ํ•™์Šต ๊ฐœ๋ก , Reinforcement learning
37:59
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (32), ์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ value function Prediction, Reinforcement learning
15:04
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (31), On-policy Prediction with Approximation ๊ฐ„๋žต์„ค๋ช…, Reinforcement learning
35:59
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (30), Model based RL์˜ ์ง๊ด€์  ์ดํ•ด, Reinforcement learning
54:58
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (29), Monte Carlo Tree Search, ์ƒ์„ธํžˆ ์•Œ์•„๋ณด๊ธฐ, Reinforcement learning
44:52
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (28), ๊ธฐ์ดˆ๋‹ค์ง€๊ธฐ II, Planning and Learning, Reinforcement learning
17:23
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (27), Planning and Learning with Tabular Methods ์˜ ๊ฐ„๋žต์„ค๋ช… Reinforcement learning
30:05
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (26), Temporal-Difference Learning TD ์˜ ์ง๊ด€์  ์ดํ•ด, Reinforcement learning
30:13
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (25), Monte Carlo ๊ฐœ๋…์žก๊ธฐ, Reinforcement learning
17:09
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (24-1), q-value, Reinforcement learning
17:07
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (24), ๊ธฐ์ดˆ ๋‹ค์ง€๊ธฐ, Reinforcement learning
42:31
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๊ฐ•ํ™”ํ•™์Šต (23), ๋‹ค์‹œ ์‹œ์ž‘ํ•˜๊ธฐ, Reinforcement learning
43:30
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] Bellman equation๊ณผ optimal value function (22)
40:15
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] Reinforcement learning ๊ฐ•ํ™”ํ•™์Šต์„ ์‹œ์ž‘ํ•˜๋ฉฐ (21)
59:30
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๋น„์ „๊ณต์ž๋ฅผ ์œ„ํ•œ ์›นํฌ๋กค๋ง ๋ง›๋ณด๊ธฐ (20)
39:44
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์ธ๊ณต์ง€๋Šฅ ์ค‘๊ฐ„ ์ ๊ฒ€ (19)
34:24
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] Attention ๊ณผ less Attention (18-3)
47:55
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] Attention ๊ณผ Tacotron ์‚ดํŽด๋ณด๊ธฐ (18-2)
47:44
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] Attention ๊ณผ Transformer ์˜ ์ง๊ด€์  ์ดํ•ด (18-1)
44:08
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] WaveNet ๋…ผ๋ฌธ์ฝ๊ธฐ (17-3)
52:00
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] Attention ๊ณผ WaveNet (17-2)
47:54
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] WaveNet์— ๋Œ€ํ•˜์—ฌ (17-1)
34:20
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] STYLE GAN์˜ ๋ณต์Šต (16-5)
29:37
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] STYLE GAN์˜ ์‹ฌํ™”ํ•™์Šต (16-4)
33:29
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] STYLE GAN์˜ ์ดํ•ด (16-3)
01:21:27
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] GAN๊ณผ STYLE GAN์˜ ์ง๊ด€์  ์ดํ•ด (16-2)
51:50
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] GAN์˜ ์ง๊ด€์  ์ดํ•ด (16-1)
44:28
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] AutoEncode ์™€ VAE์˜ ์ฒ ์ €ํ•œ ์ดํ•ด (15-4)
34:57
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] AutoEncode์™€ VAE์˜ ์‹ฌํ™”ํ•™์Šต (15-3)
01:01:28
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] AutoEncode ์™€ VAE์˜ ์ข€๋” ์ดํ•ดํ•˜๊ธฐ (15-2)
01:09:10
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] AutoEncode ์™€ VAE์˜ ์ง๊ด€์  ์ดํ•ด (15-1)
39:19
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] CNN Architectures์˜ ์ง๊ด€์  ์ดํ•ด (14)
47:03
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] CNN์˜ ์ง๊ด€์  ์ดํ•ด (13)
27:01
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] LSTM์˜ ์ฒ ์ €ํ•œ์ดํ•ด (12)
40:50
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] LSTM์˜ ์ง๊ด€์ ์ดํ•ด (11)
15:16
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] Resnet (10)
06:27
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ๋ฐ”๋‘‘๊ณผ ์ธ์ƒ (๋ถ€๋ก2)
29:05
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์ธ๊ณต์ง€๋Šฅ๊ณผ ์—ญ์ „ํŒŒ (8)
22:33
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์—”ํŠธ๋กœํ”ผ์™€ ์ธ๊ณต์ง€๋Šฅ (7)
25:07
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์ธ๊ณต์ง€๋Šฅ๊ณผ ํ†ต๊ณ„ (6)
27:54
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์ธ๊ณต์ง€๋Šฅ๊ณผ ํ†ต๊ณ„ (5)
19:18
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] 4์ฐจ ์‚ฐ์—…๊ณผ ์ธ๊ณต์ง€๋Šฅ โ…ฃ
19:23
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] 4์ฐจ ์‚ฐ์—…๊ณผ ์ธ๊ณต์ง€๋Šฅ โ…ข
41:26
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] 4์ฐจ ์‚ฐ์—…๊ณผ ์ธ๊ณต์ง€๋Šฅ โ…ก
12:54
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] ์ฑ… ์†Œ๊ฐœ (๋ถ€๋ก)
30:53
[์„œ์šด์‚ฐ๋ฏธ๋””์–ด์„ผํ„ฐ] 4์ฐจ ์‚ฐ์—…ํ˜๋ช… ์ธ๊ณต์ง€๋Šฅ ๊ฐ•์˜