HackCV 资讯

An overview of feature selection strategies

High dimensional data • Machine Learning • Regression Models • Data Science • Regression • 2018/09/19 22:00

Introduction Feature selection and engineering are the most important factors which affect the success of predictive modeling. This remains true … More
Time series classification with Tensorflow

convolutional neural networks • deep learning • Machine Learning • recurrent neural networks • Time-series • 2017/08/23 00:00

Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard approach to time-series problems … More
Machine learning for alchemy

Machine Learning • Physics • Regression Models • web scraping • 2017/08/15 00:10

It is no news to anyone that applications of machine learning span a vast range of fields, from artificial intelligence … More
An example of web scraping with R: Online Food Blogs

Machine Learning • web scraping • 2017/06/14 02:46

In this blog post I will discuss web scraping using R. As an example, I will consider scraping data from … More
Feature Engineering with Tidyverse

Machine Learning • Crimes • Data Science • Kaggle • San Francisco • 2017/04/08 10:02

In this blog post, I will discuss feature engineering using the Tidyverse collection of libraries. Feature engineering is crucial for … More
Yet another introduction to Neural Networks

Machine Learning • Data Science • MNIST • Neural Networks • 2017/02/08 07:12

There are many great tutorials on neural networks that one can find online nowadays. Simply searching for the words “Neural Network” … More
Deciphering the Neural Language Model

Machine Learning • 2016/12/17 15:32

Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. … More
Stacking models for improved predictions

Machine Learning • Regression Models • Data Science • Kaggle • Regression • 2016/12/02 06:08

If you have ever competed in a Kaggle competition, you are probably familiar with the use of combining different predictive models … More