We have been learning programming and AI since the beginning of this website. Now, we are excited to explore the world of generative AI art using the Stable Diffusion WebUI. This innovative web interface provides us with a simple and efficient way to generate AI art. In one of our previous posts, we experimented with …
Easy Guide for Beginner: Learn how to use Stable Diffusion WebUI – Part 1 Read More »
Other than our last hand writing challenge, there is another Kaggle challenge featuring image recognition — TGS Salt Identification Challenge. But this time, we are going for an “upgrade”. As we are dealing with object detection. It is all about salt. In this challenge, our mission is finding geophysical images that contain salt. Oh wait, does it sound weird? Actually …
U-Net and IoU for Object Detection in Image Processing Read More »
In our past machine learning topic, “Ensemble Modeling“, we mentioned how blending helps on improving our prediction. Then in another topic, “Why are people frustrated on Kaggle’s challenge?“, we mentioned how blending ruins a data science competition. Okay, we have a question here, is blending good or bad?
Last time, we tried the Kaggle’s TalkingData Click Fraud Detection challenge. And we used limited resources to handle a 200 million records sized dataset. Although we can make our classification with Random Forest model, we still want a better scoring result. Inside the Click Fraud Detection challenge’s leaderboard, I find that most of the high scoring …
You have probably read the above comic in 9gag or imgur before. It is a funny joke, but on the other hand, it is also a material for our Machine Learning topic. It sounds weird? Oh yeah, sometimes knowledge comes from strange ideas.
Up to now, we have tried 3 different Kaggle journeys, the Titanic Survivors, the Iowa House Prices and the hand written digits recognition. Those journeys covered popular Machine Learning topics, such as classification, regression, deep learning, and so on. I would suggest fans of Machine Learning to start with those journeys. So we can learn …
When I have a data project in mind and have no idea on where to start modeling, I will always use the Random Forest model. It is not because of its catchy name and the fact that I always misspell it as Rain Forest, it is quick, convenient, easy to understand and, it provides decent …
Previously on CodeAStar: The data alchemist wannabe opened the first door to “the room for improvement”, where he made better prediction potions. His hunger for the ultimate potions became more and more. He then discovered another door inside the room. The label on the door said, “Ensemble Modeling”. This is our last chapter on the …
To win big in real estate market using data science – Part 3: Ensemble Modeling Read More »
Previously on CodeAStar: A data alchemist wannabe tried to win big in real estate market, he then used Kaggle’s Housing Regression data set, engineered the features and fit them in a bunch of models. Dang! Nothing fancy happened. But he then discovered “the room”, the room for improvement — model params tuning.
Okay, yes, once again, it is a catchy topic. BUT, this post is indeed trying to help people (including me) to gain an upper hand in real estate market, using data science. From our last post (2 months ago, I will try to update this blog more frequently :]] ), we learn that we can …
To win big in real estate market using data science – Part 1 Read More »
