Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
Tips for would-be founders, technical or non-technical, before rolling up their sleeves and develop their products! From various ways of "pretotyping" to accurately gauge target customer's response, lean method, minimum viable product, feature selection, planning a product with robust data cycle, coping with delays, and guiding a team of rockstar engineers to build the right product and build the product right. Some personal experienced shared at the end as case studies.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180526@Taiwan AI Academy, Professional Managers Class.
Covering important concepts of classical machine learning, in preparation for deep learning topics to follow. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
Find Your Passion and Make a Difference in Your CareerAlbert Y. C. Chen
20180314 at National Taiwan Normal University.
Reflection on my own career from being inspired to work on CV/ML research during my graduate studies at NTNU, then going abroad to obtain my Ph.D. and later on my career in this field. The talk emphasizes on the importance of innovation and how to realize ones new ideas within large and small organizations.
Covering important topics of Classical Machine Learning in 16 hours, in preparation for the following 10 weeks of Deep Learning courses at Taiwan AI academy from 2018/02-2018/05. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
AI gold rush, tool vendors and the next big thing
2017/12/27 at Mediatek
- Overview of booming AI applications, from media, entertainment, e-commerce, autonomous driving, surveillance, industrial inspection, medical imaging, bioinformatics, finance, etc., along with expert predictions of their market size and growth.
- Dissect the applications with largest size and growth into their technical components and their unmet demands.
- Among all the unmet demands and uncertainties in this AI gold rush, what should an IC design company do? I’ll briefly cover NVIDIA’s case, which most of us know well already, then supplement case studies of Qualcomm, Intel, Google TPU and other smaller firms.
Even when we have a clear target, it takes years for supporting libraries and software to be properly optimized. I’ll share some thoughts and personal experiences on how to make sequentially-ordered hardware/software/library optimization happen faster and in parallel, and the tools that the IC design house need to provide in order for it to happen.
Practical computer vision-- A problem-driven approach towards learning CV/ML/DLAlbert Y. C. Chen
Practical computer vision-- A problem-driven approach towards learning CV/ML/DL
Albert Chen Ph.D., 20170726 at Academia Sinica, Taiwan
Invited Speech during Academia Sinica's AI month
Think different, in Finance. An outsider's two cents on how could finance majors rethink their role and value in the rapidly changing AI era, with some FinTech case studies.
Albert Y. C. Chen, Ph.D., VP of R&D at Viscovery--Visual Search, Simply Smarter.
Invited speech at Automatic Optical Inspection Equipment Association (AOIEA) Annual Summit, Taiwan, 2017/06/15, "Deep Learning and Automatic Optical Inspection".
陳彥呈博士,Viscovery研發副總裁2017年6月15日於自動光學檢測設備聯盟 會員年會 專題演講「人工智慧下的AOI變革浪潮:影像辨識技術的突破與新契機」。
25. 影⾳音內容關連廣告
Previous moment: dining scene Insert Food Deliver Service ad Next Moment: dining scene
饿了了吗?快点饿了了么!
Food Delivery Service Ad:
Previous moment: dining scene Insert KFC ad Next second: dining scene
炸鸡红包快来
抢!
Restaurant Ad:
35. 通⽤用型 AI vs
專⾨門辨識影⾳音內容商機之 AI
TOP 5 TAGS COMPARISON
TAG AD PLACEMENT VALUE TAG AD PLACEMENT VALUE
Person Low Coulee Nazha (actress) High
Anime Low Sean Sun (actor) High
Screenshot Low Back of smartphone High
Cartoon Low Female Medium
Adult Medium Young Medium
“FIRST LOVE” DRAMA SERIES SCENE
Competitive Analysis
Baidu vs. Viscovery
TOP 5 TAGS COMPARISON
TAG (Man’s Face) AD PLACEMENT VALUE TAG AD PLACEMENT VALUE
Age: 32 Medium Necklace High
Asian Medium Baseball cap High
Male Medium Bracelet High
Not smiling Low (inaccurate) Ziwen Wang High
42. Unstructured
videos
⼴广告主
DSP SSP
⼴广代
媒代
DMP
Viscovery SDK
互动层Ad Exchange
⼴广告版位
(Key moment)
Categories
Users
User segment
Viscovery Server
Video id and key
moments
Publishers
Video crawler
VSP
ranking
system
One video w/
its id
Videos
Categories User profile
From DMP or publishers
Asynchronous processes
Filter
Recommander engine
Category of ad
Tracking Server
监控链结(互动层)之资讯
监控链结(互动层)之
资讯
6
53
2
14
vad.json
Tag2AD
expensive cheaper
ad request
vtag.raw
vtag.raw
Database
Engine
api
api
VDSAgent
web
api
web
api
ad op {(vid, {ti, ci})}
Video id
Key moment
AD category
https://vsp.viscovery.com/
還不僅⽌止於此... 還有媒體⽣生態串串接
44. ⽤用Video AI將影⾳音內容變現!
60 mins0 mins
服饰 汽⻋车
代⾔言⼈人
聚会
⼿手机
居家
z
CTR: 0.2%
60 mins0 mins
旅游 活⼒力力
汽⻋车
⼯工作 聊天
z
60 mins0 mins
学习
using only physical tags
for recommendation
CTR: 0.9%
CTR: 2.0%
z
z
Smartphone Ad physical plus abstract
and emotional tags
physical, abstract and
emotional tags plus feedback
客厅
欢乐
客厅
聊天⼯工作⼿手机 代⾔言⼈人 欢乐旅游