找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Deep Generative Modeling; Jakub M. Tomczak Textbook 2024Latest edition The Editor(s) (if applicable) and The Author(s), under exclusive li

[復(fù)制鏈接]
查看: 31411|回復(fù): 46
樓主
發(fā)表于 2025-3-21 16:23:52 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Deep Generative Modeling
編輯Jakub M. Tomczak
視頻videohttp://file.papertrans.cn/285/284496/284496.mp4
概述Comprehensive explanation of Generative AI techniques, providing code snippets for all presented models.Revised and expanded edition with new chapters on?LLMs, Gen AI systems, and Probabilistic Modeli
圖書封面Titlebook: Deep Generative Modeling;  Jakub M. Tomczak Textbook 2024Latest edition The Editor(s) (if applicable) and The Author(s), under exclusive li
描述.This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based Models, and Large Language Models. In addition, Generative AI Systems are discussed, demonstrating how deep generative models can be used for neural compression, among others...Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics of machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It should find interest among students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics who wish to get familiar with deep generative modeling..In order to engage with a reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is availa
出版日期Textbook 2024Latest edition
關(guān)鍵詞Generative AI; Large Language Models; Autoregressive models; Diffusion models; Score-based Generative Mo
版次2
doihttps://doi.org/10.1007/978-3-031-64087-2
isbn_softcover978-3-031-64089-6
isbn_ebook978-3-031-64087-2
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書目名稱Deep Generative Modeling影響因子(影響力)




書目名稱Deep Generative Modeling影響因子(影響力)學(xué)科排名




書目名稱Deep Generative Modeling網(wǎng)絡(luò)公開度




書目名稱Deep Generative Modeling網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Generative Modeling被引頻次




書目名稱Deep Generative Modeling被引頻次學(xué)科排名




書目名稱Deep Generative Modeling年度引用




書目名稱Deep Generative Modeling年度引用學(xué)科排名




書目名稱Deep Generative Modeling讀者反饋




書目名稱Deep Generative Modeling讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 23:52:20 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:09:22 | 只看該作者
地板
發(fā)表于 2025-3-22 06:11:29 | 只看該作者
Instrumente der strukturellen Führungort) in Chap. .. Both ARMs and flows model the likelihood function directly, that is, either by factorizing the distribution and parameterizing conditional distributions .(.|.) as in ARMs or by utilizing invertible transformations (neural networks) for the change of variables formula as in flows. No
5#
發(fā)表于 2025-3-22 08:45:37 | 只看該作者
6#
發(fā)表于 2025-3-22 14:09:23 | 只看該作者
7#
發(fā)表于 2025-3-22 18:43:30 | 只看該作者
Selbst-Führung – der Weg aus dem Hamsterradquarter and full year 2020 results, 2020.). Assuming that users uploaded, on average, a single photo each day, the resulting volume of data would give a very rough (let me stress it, .) estimate of around 3000 TB of new images per day. This single case of Facebook alone already shows us the potentia
8#
發(fā)表于 2025-3-22 22:23:13 | 只看該作者
interesting concepts? How come? The answer is simple: language. We communicate because the human species developed a pretty distinctive trait that allows us to formulate sounds in a very complex manner to express our ideas and experiences. At some point in our history, some people realized that we
9#
發(fā)表于 2025-3-23 03:04:50 | 只看該作者
https://doi.org/10.1007/978-3-031-64087-2Generative AI; Large Language Models; Autoregressive models; Diffusion models; Score-based Generative Mo
10#
發(fā)表于 2025-3-23 07:56:41 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-6 12:46
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
上栗县| 镇江市| 古丈县| 航空| 南澳县| 长海县| 汝城县| 甘肃省| 雅安市| 磐石市| 桂东县| 保亭| 涟源市| 邻水| 油尖旺区| 南皮县| 德化县| 丰顺县| 旺苍县| 南涧| 晋州市| 阿尔山市| 邓州市| 淅川县| 金坛市| 富宁县| 左云县| 大石桥市| 玛沁县| 新宾| 北宁市| 丰宁| 白山市| 丰镇市| 清远市| 昂仁县| 安龙县| 贵溪市| 双鸭山市| 友谊县| 府谷县|