TonTon Huang Ph.D. | 痛痛 - Deep Learning 101

Yoshua Bengio, Yann Lecun, and Geoffrey Hinton. 2021. Deep learning for AI. Commun. ACM 64, 7 (July 2021), 58–65. DOI:https://doi.org/10.1145/3448250
Yann LeCun, A Path Towards Autonomous Machine Intelligence. https://openreview.net/pdf?id=BZ5a1r-kVsf

台灣人工智慧社團 FB | YouTube | Odysee | Deep Learning 101 FB

Deep Learning 101 Meetup (The top private AI Meetup in Taiwan launched on 2016)
http://DeepLearning101.TWMAN.ORG

https://interactionx.pse.is/DLBook101 | https://interactionx.pse.is/DLMeetup101
那些自然語言處理踩的坑 | 御守臺灣・科技抗疫：提高聲紋辨識正確率更添防疫新利器 | 那些語音處理踩的坑
 ComfyUI + Multimodal Model + Segment Anything Model 2 + Stable Diffuision + FLUX
大型語言模型要踩的坑 | 那些ASR和TTS可能會踩的坑 | 白話文手把手帶你科普 GenAI
那些檢索增強生成 (Retrieval-Augmented Generation, RAG) 要踩的坑
Call for Partner or POC (Proof of Concept) Contact: TonTon ( at ) TWMAN.ORG
https://github.com/Deep-Learning-101 | https://huggingface.co/DeepLearning101

Insurance (壽險) FAQ - High Entropy Alloys (材料) FAQ - Cryptocurrency Exchange (交易所) FAQ
語音質檢/噪音去除 (語音增強) - 多模態大型語言模型之單據核銷- 診斷書醫囑擷取分析
 ASR 語音語料辨識修正工具 - 客服ASR文本AI糾錯系統 - 逐字稿文本內容比對工具 - 線上語音合成；GPT-SoVITS Speaker:TWMAN

如果想分享研究心得或產業相關題目，歡迎報名。
本讀書會採實名分享制，目前主要參與者分別來自不同公司的人工智慧研發團隊。
如果您是錄影裡的講者，願意分享個人資料，再給訊息；感謝各位參與過的夥伴們。
本讀書會由 TonTon Huang Ph.D. 無償贊助場地及茶水點心多年，無任何 Co-organizer。
Deeep Learnig 101 屬入門後讀書會，研究實作的討論 (AI是條寂寞且惶恐的道路)。

Welcome to Taiwan ! Prof. Yann LeCun @ 2017/06/29

Prof. Hung-Yi Lee

EE.NTU

Deep Learning 101 - 大神 Q&A I & II @ No. 16 @ 2018/02/02
深度學習界的大神們來台北101給大家解惑，疑難雜症都不用擔心

Prof. Winston Hsu

CSIE.NTU / NVIDIA AI Lab

On the Relationship among Convolution Attention and GNN
No. 49 @ 2022/05/06 @ 杜岳華

深度學習也可以學傅立葉轉換
No. 47 @ 2020/12/04 @ 杜岳華

Machine Teaching
No. 31 @ 2019/06/28 @ Mark Liou

Model-Based Reinforcement Learning
No. 48 @ 2021/07/16 @ 翁崇恒

神經網路的黑執事
 No. 44 @ 2020/08/21 @ Mark Liou

Introduction to geometric deep learning with implementation
No. 37 @ 2020/01/10 @ 杜岳華

幾何深度學習是在幾何什麼的？
No. 43 @ 2020/07/24 @ 杜岳華

高維資料的降維演算法及視覺化
No. 39 @ 2020/03/20 @ 杜岳華

Modeling the Dynamics of SGD by Stochastic Differential EquationNo. 45 @ 2020/09/11 @ Mark Chang

information in the weightsNo. 42 @ 2020/06/19 @ Mark Chang

PAC Bayesian for Deep LearningNo. 38 @ 2020/02/14 @ Mark Chang

Transfer Learnings & Multitask LearningNo. 32 @ 2019/07/19 @ Mark Chang

Towards Principled Methods for Training Generative Adversarial NetworksNo. 19 @ 2018/05/11 @ Mark Chang

Domain adaptationNo. 29 @ 2019/03/08 @ Mark Chang

NLP Landing & Marchine Reading Comprehension
No. 41 @ 2020/05/29 @ Speaker: Ian & Hsiang

Instance Segmentation No. 40 @ 2020/05/01 @ Speaker: 顏志翰 (Bean)

Explainable Artificial IntelligenceNo. 36 @ 2019/12/13 @ Speaker: Jiero Ho

High-Dimensional Continuous Control Using Generalized Advantage Estimation No. 34 @ 2019/10/04 @ Speaker: Cecile Liu

Hardware Accelerators for Machine Learning No. 33 @ 2019/08/23 @ Speaker: 林家銘

The Hackathon/Formosa Grand Challenge Between UsNo. 30 @ 2019/05/17 @ Speaker: Ryan Chao

Deep knowledge representation and reasoningNo. 28 @ 2019/02/15 @ Speaker: Chin-Hui Chen

Semi-Supervised Classification with Graph Convolutional NetworksNo. 27 @ 2019/01/11 @ Speaker: Bean Yen

Machine Reading Comprehension No. 26 @ 2018/12/07 @ Speaker: Nat Lee, Alice Hong, Ian Wang

No. 25 @ 2018/11/09 @ Deep Learning 101 2週年

SOC: Social-network Opinion and CommentNo. 24 @ 2018/10/12 @ Speaker: Nat Lee, TonTon

Recommender SystemNo. 23 @ 2018/09/14 @ Speaker: SAS

Deep Bilateral Learning for Real-Time Image Enhancement No. 22 @ 2018/08/17 @ Speaker: 黃俊仁 (Ken Huang)

Dynamic Routing Between CapsulesNo. 21 @ 2018/07/06 @ Speaker: Jiero Ho

VAE: A generative model for 2D anime character facesNo. 20 @ 2018/06/08 @ Speaker: Nat Lee, Boris Lo, Alice Hong, Ian Wang

Deep Generative Models @ Deep Learning BookChapter 20 @ No.18 @ 2018/04/13

Approximate Inference @ Deep Learning Book Chapter 19 @ No.17 @ 2018/03/16

Confronting the Partition Function @ Deep Learning BookChapter 18 @ No.15 @ 2018/01/12

Speaker: Ian Wang @ Chapter 17 @ No.14 @ 2017/12/15Monte Carlo Methods @ Deep Learning Book

Structured Probabilistic Models @ Deep Learning BookChapter 16 @ No. 12 @ 2017/10/27

Speaker: Nat Lee @ Autoencoders @ Deep Learning Book
Chapter 14 @ No. 11 @ 2017/09/08

Linear Factor Models @ Deep Learning BookChapter 13 @ No. 10 @ 2017/08/11

Representation Learning @ Deep Learning Book Chapter 15 @ No. 09 @ 2017/07/07

Recurrent and Recursive Nets @ Deep Learning Book
Chapter 10 @ No. 07 @ 2017/05/05

Convolutional Networks @ Deep Learning BookChapter 9 @ No. 06 @ 2017/04/14

Optimization for Training Deep Models @ Deep Learning Book Chapter 8 @ No. 05 @ 2017/03/10

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