Speaker: Ken Hu
Behind the glamour of mobile apps, business SaaS products have been steadily building itself into a billion dollar industry. With companies such as Salesforce spearheading the adaption of business SaaS products in corporate, startups are rising to take on the SME market.
With hot topics such as "big data" and "machine learning" flying around, it may seem overwhelming to put an idea into action. I will walk through the process of building an data analytics minimum viable product (MVP). Attendees will have hands on experiences with the free and/or affordable technology and services that can get their MVP up and running.
This talk assumes attendees understand the basic of Python. Basic understanding of MapReduce and NoSQL databases will be big pluses as well.
About the speaker
I am a data scientist, software developer, and aspiring entrepreneur. I'm especially interested in machine learning, information retrieval, text analytics, and cloud technology. My weapon of choice is Python.
I previously founded a social data analysis company and developed its core technology. I grew the startup into a profitable entity, without any outside funding.
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https://www.youtube.com/watch?v=BJkWl5RiCAg
Day 3, R2 13:00–13:30
I have been using Ren'Py for eight years in Doujin activities. Ren'Py is Python based visual novel game engine. In the past eight years, I felt many benefits using Ren'Py. This talk demonstrates the benefits of using it. The author also describes how Ren'Py evolved from the user's point of view.
Slides not uploaded by the speaker.
Speaker: Daisuke Saito
Daisuke Saito is a assistant professor of the School of Fundamental Science and Engineering, Waseda University in Japan. He acquired a Doctor of Engineering degree from Waseda University in Japan. His research interests include programming education and digital game-based learning.
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https://www.youtube.com/watch?v=bzkhPxLvk58
Speaker: Chia-Chi Chang
https://docs.google.com/document/d/1LwQG8pLLO2PEviExoU3xiqyclnaT9kfhwsCGVKVRB2I/edit?usp=sharing
Although there are several data mining tools in python, you can use them to deal with almost every kind of data (numeric, text, image, audio, ...) you met. Besides, there are also lots of modeling tools in python, you can use them to build the FIRST LIGHTING MODEL to solve the problems.
However, if you want to solve problems deeply, most of time, you need to write down the customerized models and solve them by yourself. Instead of using fast modeling tools, you need to know more about the essential things in modeling:
What is a model ?
- How models solve your problems ?
- What is the connection between models and data ?
- What is the important data ? important model ?
I deeply believe ... The more the connection between models and data you know, the deeper the problem you can solve.
Outline:
What is Modeling ?
Data, Model, Evaluators
Direct Problem: Data + Evaluators → Model
Inverse Problem: Data + Models → Evaluator
Hacking Models with Metric Learning
Data as a Model & Model as a Data
Duality between Dimension Reduction and Clustering
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https://www.youtube.com/watch?v=PznPp-BbwyU