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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.
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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.
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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.
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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.
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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.
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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.
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DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS (https://arxiv.org/pdf/1511.06434.pdf)
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Generative Adversarial Nets (https://arxiv.org/pdf/1406.2661.pdf)
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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”
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This is summary and what I learned from the paper “Deep neural networks for youtube recommendations”
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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.
Published:
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.
Published:
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.
Published:
DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS (https://arxiv.org/pdf/1511.06434.pdf)
Published:
Generative Adversarial Nets (https://arxiv.org/pdf/1406.2661.pdf)
Published:
This is summary and what I learned from the paper “Related pins at pinterest the evolution of a real-world recommender system”
Published:
This is summary and what I learned from the paper “Deep neural networks for youtube recommendations”
Published:
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.
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.
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.
Published:
There is a term “Sampling” in statistics. In this page, will see the concept of sampling and how it is adjusted to machine learning.
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.