ºÝºÝߣshows by User: TingYunTseng / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: TingYunTseng / Wed, 04 Oct 2017 17:50:20 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: TingYunTseng Stepping into the AI Wave - Words from an Industry Newbie /slideshow/stepping-into-the-ai-wave-words-from-an-industry-newbie/80465933 steppingintotheaiwave-wordsfromanindustrynewbie-171004175020
* Link of the talk: https://www.youtube.com/watch?v=47ZTn8saHTo Data Science is a multidisciplinary field consisting of Statistics, Mathematics, Computer Science and even some specific domain knowledge based on what problem you are solving. It’s often the case that a single individual cannot be equipped with all the requirements when entering the field. This talk is mainly about how I, a non-CS major with pure Mathematics and Statistical background, worked through 2 startups as a Data Scientist/ Machine Learning Engineer and along the way gradually picked up those missing skillsets. I will also share what it is like to be a Data Scientist in a startup and some essential skills I found necessary if you are seeking for similar opportunities.]]>

* Link of the talk: https://www.youtube.com/watch?v=47ZTn8saHTo Data Science is a multidisciplinary field consisting of Statistics, Mathematics, Computer Science and even some specific domain knowledge based on what problem you are solving. It’s often the case that a single individual cannot be equipped with all the requirements when entering the field. This talk is mainly about how I, a non-CS major with pure Mathematics and Statistical background, worked through 2 startups as a Data Scientist/ Machine Learning Engineer and along the way gradually picked up those missing skillsets. I will also share what it is like to be a Data Scientist in a startup and some essential skills I found necessary if you are seeking for similar opportunities.]]>
Wed, 04 Oct 2017 17:50:20 GMT /slideshow/stepping-into-the-ai-wave-words-from-an-industry-newbie/80465933 TingYunTseng@slideshare.net(TingYunTseng) Stepping into the AI Wave - Words from an Industry Newbie TingYunTseng * Link of the talk: https://www.youtube.com/watch?v=47ZTn8saHTo Data Science is a multidisciplinary field consisting of Statistics, Mathematics, Computer Science and even some specific domain knowledge based on what problem you are solving. It’s often the case that a single individual cannot be equipped with all the requirements when entering the field. This talk is mainly about how I, a non-CS major with pure Mathematics and Statistical background, worked through 2 startups as a Data Scientist/ Machine Learning Engineer and along the way gradually picked up those missing skillsets. I will also share what it is like to be a Data Scientist in a startup and some essential skills I found necessary if you are seeking for similar opportunities. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/steppingintotheaiwave-wordsfromanindustrynewbie-171004175020-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> * Link of the talk: https://www.youtube.com/watch?v=47ZTn8saHTo Data Science is a multidisciplinary field consisting of Statistics, Mathematics, Computer Science and even some specific domain knowledge based on what problem you are solving. It’s often the case that a single individual cannot be equipped with all the requirements when entering the field. This talk is mainly about how I, a non-CS major with pure Mathematics and Statistical background, worked through 2 startups as a Data Scientist/ Machine Learning Engineer and along the way gradually picked up those missing skillsets. I will also share what it is like to be a Data Scientist in a startup and some essential skills I found necessary if you are seeking for similar opportunities.
Stepping into the AI Wave - Words from an Industry Newbie from Ting-Yun (Joanne) Tseng
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https://cdn.slidesharecdn.com/profile-photo-TingYunTseng-48x48.jpg?cb=1523503580 I am a Machine Learning Engineer who is enthusiastic in algorithms, and at the same time puts emphasis on practical integration of machine learning methodologies into systems / products. At work, I always try to demonstrate my main philosophy: great and successful machine learning adoptions must embody data lifecycle as a whole, and not solely the algorithm. This includes question-initiated decisioning of how and what data is collected, to designing of the automated pipeline for data preprocessing, cleaning, and modeling. Areas of interest: - Automated data pipeline designs for ML-integrated solutions - Machine learning algorithms, including SVM, Random Forest & Deep Learning tzeng-git.github.io/