How is error handling done in Julia?

This recipe explains how error handling is done in Julia.

Recipe Objective: How is error handling done in Julia?

This recipe explains how error handling is done in Julia.
For more related projects-
Project 1
Project 2

Access House Price Prediction Project using Machine Learning with Source Code

Error Handling

Julia provides a try/catch statement which can be helpful in error handling. Try/catch statement allows exception handling in an organized manner without breaking the code. Generally, the try/catch statement is slower than using conditional branching. Try/catch statement allows the exception to be saved in a variable.

calc_cbrt(a) = try
    cbrt(a)
catch b
    if isa(b, DomainError)
       cbrt(complex(a, 0))
    elseif isa(b, BoundsError)
       cbrt(a)
    end
end
calc_cbrt(8)
calc_cbrt(-8)

What Users are saying..

profile image

Ed Godalle

Director Data Analytics at EY / EY Tech
linkedin profile url

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More

Relevant Projects

Linear Regression Model Project in Python for Beginners Part 1
Machine Learning Linear Regression Project in Python to build a simple linear regression model and master the fundamentals of regression for beginners.

Learn to Build an End-to-End Machine Learning Pipeline - Part 1
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, addressing a major challenge in the logistics industry.

Create Your First Chatbot with RASA NLU Model and Python
Learn the basic aspects of chatbot development and open source conversational AI RASA to create a simple AI powered chatbot on your own.

Learn How to Build a Logistic Regression Model in PyTorch
In this Machine Learning Project, you will learn how to build a simple logistic regression model in PyTorch for customer churn prediction.

Tensorflow Transfer Learning Model for Image Classification
Image Classification Project - Build an Image Classification Model on a Dataset of T-Shirt Images for Binary Classification

Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

Learn to Build Generative Models Using PyTorch Autoencoders
In this deep learning project, you will learn how to build a Generative Model using Autoencoders in PyTorch

CycleGAN Implementation for Image-To-Image Translation
In this GAN Deep Learning Project, you will learn how to build an image to image translation model in PyTorch with Cycle GAN.

Build an optimal End-to-End MLOps Pipeline and Deploy on GCP
Learn how to build and deploy an end-to-end optimal MLOps Pipeline for Loan Eligibility Prediction Model in Python on GCP

Build CI/CD Pipeline for Machine Learning Projects using Jenkins
In this project, you will learn how to create a CI/CD pipeline for a search engine application using Jenkins.