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rokujyouhitoma 2026/04/28
IDS

MachineLearning

Python
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Stable Diffusion ã‚’åŸºç¤Žã‹ã‚‰ç†è§£ã—ãŸã„äººå‘ã‘è«–æ–‡æ”»ç•¥ã‚¬ã‚¤ãƒ‰ã€ç„¡æ–™è¨˜äº‹ã€‘
rokujyouhitoma 2022/10/14
DeepLearning

MachineLearning
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rokujyouhitoma 2021/11/04
QML

MachineLearning
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DeNA TechCon 2020 - æŠ€è¡“ã®åŠ›ã§äº‹æ¥ã®æœªæ¥ã‚’ãƒªãƒ¼ãƒ‰ã™ã‚‹ -
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rokujyouhitoma 2020/02/12
DeNA

MachineLearning

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DeNATechCon

DeNATechCon2020
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rokujyouhitoma 2019/06/27
MachineLearning
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rokujyouhitoma 2019/06/15
FPGA

MachineLearning
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Backend.AI | AI Infrastructure OS â€” GPU Orchestration & Virtualization
rokujyouhitoma 2019/05/21
MachineLearning
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Deep learning ã‚’ç”¨ã„ãŸç”»åƒã‹ã‚‰èª¬æ˜Žæ–‡ã®è‡ªå‹•ç”Ÿæˆã«é–¢ã™ã‚‹ç ”ç©¶ã®ç´¹ä»‹
Deep Learningã‚’ç”¨ã„ãŸç”»åƒã‹ã‚‰èª¬æ˜Žæ–‡ã®è‡ªå‹•ç”Ÿæˆã®æœ€æ–°ç ”ç©¶ã‚’èª¿æŸ»ã—ãŸã‚‚ã®ã‚’å…¬é–‹ã„ãŸã—ã¾ã™ã€‚ æ•°å¼ã®èª¬æ˜Žãªã©ã‹ãªã‚Šç°¡ç•¥ã—ãŸã‚‚ã®ã§ã™ãŒã€å…¨ä½“ãŒä¿¯çž°ã§ãã‚‹ã‚ˆã†ã«å·¥å¤«ã—ã¾ã—ãŸã®ã§ã€ä½•ã‹ã®ã”å‚è€ƒã«ãªã‚Œã°ã¨å¹¸ã„ã§ã™ã€‚ ã¾ãŸã€ä½•ã‹é–“é•ã„ãŒã”ã–ã„ã¾ã—ãŸã‚‰ã€ã”æŒ‡æ‘˜ã®ã»ã©ã‚ˆã‚ã—ããŠé¡˜ã„ã—ã¾ã™ã€‚
rokujyouhitoma 2018/09/17
MachineLearning
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GitHub - junyanz/CycleGAN: Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
Torch implementation for learning an image-to-image translation (i.e. pix2pix) without input-output pairs, for example: New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that enables fast and memory-efficient training. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu*, Taesung Park*, Phillip Is
rokujyouhitoma 2018/09/17
CycleGAN

MachineLearning
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GANã§çŠ¬ã‚’çŒ«ã«ã§ãã‚‹ã‹~cycleGANç·¨(1)~ - Qiita
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rokujyouhitoma 2018/09/17
cycleGAN

MachineLearning
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rokujyouhitoma 2018/07/26
BigQuery

BigQueryML

MachineLearning
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rokujyouhitoma 2018/07/06
O'Reilly

MachineLearning

Architecture
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rokujyouhitoma 2018/06/11
Python

MachineLearning
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The document discusses machine learning projects and production. It begins with an introduction of Aki Ariga and their background. It then discusses 4 patterns for machine learning projects: 1) train batch/predict online via REST API, 2) train/predict batch via shared DB, 3) train/predict/serve continuously via streaming, and 4) train batch/predict on mobile apps. The document also covers machine
rokujyouhitoma 2018/05/17
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MachineLearning
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