This is a series of articles to evaluate a question from various perspectives: "Do you want to use AI for my business? Can I use AI?" Evaluation perspective of this issue-learning
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Prepare your data for modeling: feature engineering, feature selection, dimensionality reduction (Part 2)
Machine learning algorithms also fail to produce expected results on large amounts of unwanted miscellaneous data. So let's dive into all the options for optimizing your data.
Data preparation for modeling: feature engineering, feature selection, dimensionality reduction (Part 1)
Machine learning algorithms also fail to produce expected results on large amounts of unwanted miscellaneous data. So let's dive into all the options for optimizing your data.
Should I use artificial intelligence in my business? What you need to evaluate before introducing AI (2)
This is a series of articles to evaluate a question from various perspectives: "Do I need AI for my business? Can I use AI?" This issue evaluates the perspective-data.
Can neural networks introduce attention mechanisms? Google thinks they can
Google recently released some work on modeling attention mechanisms in deep neural networks
Should I use artificial intelligence in my business? What you need to evaluate before introducing AI (1)
This is a series of articles that evaluates a question from various perspectives: "Would you like to use AI? Can AI solve my problem?" This issue evaluates the perspective-features.
Put your machine learning model into production with the following 5 simple steps
This article is about a process required for a successful ML project - a production project.
How to choose a machine learning model
Have you ever thought about how we apply machine learning algorithms to problems in order to analyze, visualize, discover trends and find correlations in data? In this article, I will discuss the common steps of building a machine learning model and how to choose the right model for your data. The inspiration for this article comes from common interview questions that are asked about how to deal with data science issues and why they are chosen.
Disassemble the recommendation mechanism for YouTube's next video
This article explains YouTube's recommendation mechanism and how they recommend the next video to users.
Feature selection: importance and method details
In this article, I will share with you some of the methods I studied during the last project led by Fiverr.
You'll get some ideas about the basic methods I've tried and the more complicated methods that get the best results - remove the 60% or more features while maintaining accuracy and achieving higher stability for our model. Sex. I will also share our improvements to the algorithm.