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Uri Goren @UCkyxqgNBVSX6kYjee-d2-kw@youtube.com

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Tech talks by Uri Goren (goren.ml)


08:29
Predictive mean matching and imputation - ื”ืฉืœืžืช ืขืจื›ื™ื ื—ืกืจื™ื ืื™ื˜ืจื˜ื™ื‘ื™ืช
10:27
ืจืง ืœื ืจืื’ RAG
18:46
ื˜ืจื ืกืคื•ืจืžืจื™ื ื•ืกื“ืจื•ืช ืขื™ืชื™ื•ืช - ืฉ.ื—
24:31
ื›ื™ืฉื•ืจื™ื ืจืงื™ื ืœืžื“ืขื ื™ ื ืชื•ื ื™ื - ืขื ื‘ื•ืจื™ืก ื’ื•ืจืœื™ืง
50:41
ืขืœ ืœืžื™ื“ื” ืžื•ื ื—ื™ืช ืขืฆืžื™ืช ืขื ืžื™ื™ืง ืืจืœื™ื›ืกื•ืŸ Self Supervised
27:54
ืื™ืš ืœืœืžื“ ืžื•ื“ืœ ืฉืคื” ืœื“ื‘ืจ ื›ืžื• ืขืจืก ืขื ื—ืŸ ืžืจื’ืœื™ืช ืž LSports
24:09
ืžืขืจื›ื•ืช ื”ืžืœืฆื” ื›ืžืฉืง ื›ืœื›ืœื™ - ืขื ื“ืดืจ ืขื•ืžืจ ื‘ืŸ ืคื•ืจืช
31:07
ืื™ืžื•ืช ืคื•ืจืžืœื™ ืฉืœ ืจืฉืชื•ืช ื ื•ื™ืจื•ื ื™ื ืขื ืื™ืชื™ ื‘ื•ื—ื ื™ืง ื•ื“ืดืจ ื’ื™ื ืืžื™ืจ
49:31
Mamba ืกืงื™ืจื” ื˜ื›ื ื™ืช ืขื ืžื™ื™ืง ืืจืœื™ื›ืกื•ืŸ
33:55
ืขื•ืœื ืœืœื ืขื•ื’ื™ื•ืช - ืคืจืง ื”ืคื•ืš ืขื ื“ื™ืŸ ืคืœื‘ืŸ
27:56
ืœืžื™ื“ื” ืžื•ืœื˜ื™ ืžื•ื“ืืœื™ืช ืขื ื“ืดืจ ื—ืŸ ื—ื’ืณื’ืณ
22:40
ื ื™ื”ื•ืœ ืฆื•ื•ืชื™ ื“ืื˜ื ืกื™ื™ื ืก ืขื ื—ืŸ ืงืจื ื™
29:51
ื˜ืจื ืกืคื•ืจืžืจื™ื ื‘ืขื™ื‘ื•ื“ ืชืžื•ื ื” Visual Transformers
36:23
ืขืœ ืื™ื ื˜ืœื’ื ืฆื™ื” ืžืœืื›ื•ืชื™ืช ื‘ืขื•ืœื ื”ืžืฉืคื˜ - ืขื ืขื•ืžืจ ื—ื™ื•ืŸ
25:36
AI Junk - ืื™ืš ืขื•ืฉื™ื ืœืžื™ื“ืช ืžื›ื•ื ื” ืขื ืงืจื˜ื•ืŸ ื‘ื™ืฆื™ื
27:25
ืžื” ืขื•ืฉื™ื ื›ืฉื™ืฉ ืžืขื˜ ื ืชื•ื ื™ื - ืขื ื ืชื ืืœ ื“ื•ื™ื“ื•ื‘ื™ืฅ
19:45
ืžื•ื“ืœื™ ืฉืคื” ื•ืคืœื˜ ืจืฆื•ื™
00:34
Alfred+ChatGPT
36:40
ืขืœ ืžื•ื“ืœื™ ืฉืคื” ื’ื“ื•ืœื™ื ื‘ืคืจื•ื“ืงืฉืŸ ืขืœ ืื™ืชื™ ืฆื™ื˜ื‘ืจ
23:48
ืื™ืš ื ื•ืœื“ื™ื ื ืชื•ื ื™ื ืžืชื•ื™ื’ื™ื ืขื ื“ื ื™ืืœ ืžื ื•ื—ื™ืŸ
33:39
ืขืœ GLM ื• AGLM ืขื ืœื•ื‘ื” ืื•ืจืœื•ื‘ืกืงื™
34:35
ืขื ืฉื•ืงื™ ื•ื™ื•ืืœ ืขืœ ืžืื—ื•ืจื™ ื”ืงืœืขื™ื ืฉืœ One Shot Learning
30:36
ืชื•ืจืช ื”ืžืฉื—ืงื™ื ืขื ืžื•ืจืŸ ืงื•ืจืŸ
20:39
ืขืœ ืžืขืจื›ื•ืช ื—ื™ืคื•ืฉ - ืกื™ื›ื•ื ื‘ื™ืงื•ืจ ื‘ื›ื ืก ื”ื™ื™ืกื˜ืืง 2023 ืขื ืืžื™ืจ ืœื‘ื ื˜ืœ
46:18
ืžืขื‘ืจ ืœื“ืื˜ื ืื ืœื™ืกื˜ ืœื“ืื˜ื ืกื™ื™ื ืก - ื‘ื—ืกื•ืช YDATA
55:18
ืขืœ ืœืžื™ื“ื” ื—ื™ื–ื•ืงื™ืช ื‘ืื™ืžื•ืŸ ืžื•ื“ืœื™ ืฉืคื” RLHF ืขื ืžื™ื™ืง
36:05
ืกื™ื‘ืชื™ื•ืช ืขื ืื•ื”ื“ ืœื•ื™ื ืงืจื•ืŸ ืคื™ืฉ
14:10
ื—ื™ืคื•ืฉ ื•ืงื˜ื•ืจื™ ืžืงื•ืจื‘
20:56
ืขืœ ื”ืชืื•ืจื™ื” ืฉืœ Replay Buffer ืขื ืฉื™ืจืœื™ ื“ื™ ืงืกื˜ืจื• ืฉืขืฉื•ืข
20:23
ื”ืขื‘ื•ื“ื” ื”ืจืืฉื•ื ื” ื‘ื“ืื˜ื ืฉืœื™
21:17
ืขืœ ืงื•ืจืกื˜ื™ื ืขื ืื™ืชืŸ ื ืฆืจ
32:26
ืืชื’ืจ ื”ืœื•ื•ื™ื ื•ืช ืฉืœ ืžืคืโ€ืช
37:07
ื–ื™ื”ื•ื™ ื”ื•ื ืื•ืช ืขืœ ื™ื“ื™ ืื ื•ืžืœื™ื•ืช ืขื ืื ื“ืจืก ืžRiskified
22:03
ืื™ืš ืœื ืœื”ืขืกื™ืง ื’โ€™ื•ื ื™ื•ืจื™ื ื‘ืžืงืฆื•ืขื•ืช ื”ื“ืื˜ื
31:10
ืจืฉืชื•ืช ื ื•ื™ืจื•ื ื™ื ืขืœ ื’ืจืคื™ื ืขื ื—ื’ื™ ืžืจื•ืŸ
01:01:38
ื™ืฆื™ืจื” ืื•ื˜ื•ืžื˜ื™ืช ืฉืœ ืงื•ื“ ืขื ืคืจื•ืค ืขืจืŸ ื™ื”ื‘ ื•ื“โ€ืจ ืื•ืจื™ ืืœื•ืŸ
20:14
Data Centric AI ืขื ืกื™ื’ืœ ืฉืงื“
30:29
ืขืœ ื—ื™ื ื•ืš ื‘ืชื—ื•ื ื”ื‘ื™ื ื” ื”ืžืœืื›ื•ืชื™ืช ืขื ืฉื™ ื™ืคืจื—
36:16
ื–ืจื™ืžื•ืช ืžื ื•ืจืžืœื•ืช ืขื ืžื™ื™ืง ืืจืœื™ื›ืกื•ืŸ
35:03
ืื ื•ืžืœื™ื•ืช ื‘ืชืžื•ื ื•ืช ืขื ืขืจืŸ ืื™ืœืช
42:32
ืื™ืš ืคืจื™ืœื ืกืจ ื ื™ื’ืฉ ืœืคืจื•ื™ืงื˜ AI
26:05
Co-Pilot - Codex ื•ื™ืฆื™ืจื” ืฉืœ ืงื•ื“ ืขื ืžื•ื“ืœื™ ืฉืคื”
25:46
ืฉื™ืฉื” ืžืืžืจื™ื ืฉื›ืœ ื“ืื˜ื ืกื™ื™ื ื˜ื™ื ืกื˜ ื—ื™ื™ื‘ ืœื”ื›ื™ืจ ืขื ืฉืงื“ ื–ื™ื›ืœื™ื ืกืงื™
22:05
Novel Class Discovery ื–ื™ื• ืคืจื•ื™ื ื“ ืขืœ
18:20
Multi-Task Learning ืขื ืื™ืชื™ ืžืจื’ื•ืœื™ืŸ
35:45
ืžืขืจื›ื•ืช ื”ืžืœืฆื” ื‘ื˜ืื‘ื•ืœื” ืขื ื“ื ื” ืงื ืจ
24:02
ื–ื™ื”ื•ื™ ืื•ื‘ื™ื™ืงื˜ื™ื ืขื ืื‘ืจื”ื ืจื‘ื™ื‘
29:20
Defusion Denoising Models ืขื ืžื™ื™ืง ืืจืœื™ื›ืกื•ืŸ
29:33
MLOps ืขื ืื•ืจืŸ ืจื–ื•ืŸ
42:36
Machine Learning Engineering ืขื ืืกืฃ ืคื ื—ืกื™
22:15
ืกื˜ื˜ื™ืกื˜ื™ืงื” ื‘ื™ื™ืกื™ืื ื™ืช
25:10
ื ื™ื•ื•ื˜ ืื™ื ืจืฆื™ืืœื™ ืขื ื‘ืจืง ืื•ืจ
15:37
ืขืœ ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช ื‘ืกื™ื™ื‘ืจ ืขื ืจื•ืขื™ ื˜ื‘ื—
34:24
Proximal Policy Optimization ืžื” ื–ื”
23:05
ื‘ื ื“ื™ื˜ื™ื ืขื ื“ื ื™ืืœ ื—ืŸ
21:56
Variational Auto Encoders ืขื ืžื™ื™ืง ืืจืœื™ื›ืกื•ืŸ
14:43
ืขืฆื™ ื”ื—ืœื˜ื” - ื—ื•ื–ืจื™ื ืœื‘ืกื™ืก
12:31
ืœืžื™ื“ื” ื ื™ื’ื•ื“ื™ืช Contrastive Learning
24:35
ืคื“ื™ื—ื•ืช ืฉืœ ืœืžื™ื“ื”
19:58
ืขื™ื‘ื•ื“ ืงื•ืœ ืขื ืืžื™ืจ ืขื‘ืจื™