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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Sangho Lee
About me
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Posts
[Paper review] UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
Published:
DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS (https://arxiv.org/pdf/1511.06434.pdf)
[Paper review] Generative Adversarial Nets
Published:
Generative Adversarial Nets (https://arxiv.org/pdf/1406.2661.pdf)
XGBoost
Published:
From the latest post about Machine Learning, I dealt with boosting and bagging. Here, in this post, I want to introduce XGBoost, one of boosting algorithms.
Bagging, Boosting
Published:
When study Machine Learning, there are some confusing concept which is not easily understand. In my case, I got confused with the terms Bagging and Boosting. So, let me explain what I studied about Bagging, Boosting and related terms.
Kaggle - GA Customer Revenue prediction - EDA
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There is an ongoing competition in Kaggle. I want to share the exploration of the data as learned from other competitor’s kernel. Of course I will not include the detailed feature engineering and modeling here and now.
Kaggle - NYC taxi fare description (1)
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As I study machine learning and data science, I have interest to solving Kaggle problem. My goal with Kaggle is to improve my data science knowledge and skill. Before this, my first object is to be accustomed to Kaggle and this kind of problem solving. So firstly, I will analyze how others win and get better score. NYC taxi fare prediction competition is the first problem from Kaggle that I want to study. The goal of this competition is to predict NYC taxi fare, and here in this post, will share how others solved and what I learned from.
Related pins at pinterest the evolution of a real-world recommender system
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This is summary and what I learned from the paper “Related pins at pinterest the evolution of a real-world recommender system”
Sampling
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There is a term “Sampling” in statistics. In this page, will see the concept of sampling and how it is adjusted to machine learning.
Entropy
Published:
When study Machine Learning, there are some confusing concept which is not easily understand. Want to share those terms in this blog. 1st thing I want to share is Entropy mainly learned from Bishop and Shannon.
deep neural networks for youtube recommendations
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This is summary and what I learned from the paper “Deep neural networks for youtube recommendations”
Practical Lessons from Predicting Clicks on Ads at Facebook
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Want to share what I learned, feeled after I read and study the paper. Thesedays, usually study with machine learning, so will start with that category first.
portfolio
publications
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.