Search

#### Yugesh Verma

Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.

### How to improve time series forecasting accuracy with cross-validation?

Time series analysis, is one of the major parts of data science and techniques like clustering, splitting and cross-validation require a different kind of understanding

### Quick way to find p, d and q values for ARIMA

To make a better explanation of ARIMA we can also write it as (AR, I, MA) and by this, we can assume that in the ARIMA, p is AR, d is I and q is MA.

### 7 tricks to speed up the training of a neural network

There are a few approaches that can be used to reduce the training time time of neural networks.

### 5 methods that will not let your neural network model overfit

Through this post we will discuss about overfitting and methods to use to prevent the overfitting of a neural network.

### How to detect and treat outliers in categorical data?

Detecting outliers in the categorical data is something about the comparison between the percentage of availability of data for all the categories.

### 5 conditions when the ARIMA model should be avoided

ARIMA is the most popular model used for time series analysis and forecasting. Despite being so popular among the community, it has certain limitations as well.

### Exponential smoothing vs Moving average for time series forecasting

The exponential smoothing and moving average are the two basic and important techniques used for time series forecasting.

### How to build a robust ML model using Curriculum learning?

Curriculum learning is also a type of machine learning that trains the model in such a way that humans get trained using their education system

### When does a neural network fail to converge?

There can be various reason behind a neural network fails to converge. failure in convergence can make us confuse about the model results.

### How to use t-SNE for dimensionality reduction?

t-SNE is a nonlinear dimensionality technique that can be utilized in a scenario where the data is very high dimensional.

### All you need to know about Graph Attention Networks

A graph attention network can be explained as leveraging the attention mechanism in the graph neural networks so that we can address some of the shortcomings of the graph neural networks.

### What factors to consider when choosing a supervised learning model?

Thera are many important factors that need to be considered while choosing a machine learning model.