Tag: Algorithms

Large language models tutorial — natural language database queries

Posted on November 21, 2023 by Chika Ibeneme | Updated: November 22, 2023

Category: Hosting Basics | Tags: Algorithms, Artificial Intelligence (AI), Chat Generative Pre-trained Transformer (ChatGPT), Database, Models, Natural Language Processing, Natural Language Understanding

Reading Time: 7 minutes

If you have been following technology news over the last twelve months, you have heard about large language models (LLMs) like GPT and LaMDA several times. Chatbots like ChatGPT, Claude 2, and Google Bard are powered by these LLMs. Over the last year, large language models have completely transformed workflows in many industries, including software development and database management.

Continue reading →

Guide to Installing and Configuring Vault

Posted on March 26, 2021 by Liquid Web | Updated: May 8, 2023

Category: Tutorials | Tags: ACL, Alert Logic, Algorithm, Algorithms, API, Audit, Backups, CentOS, Compliance, Compromise, Content Security Policy, Data Leak, Encryption, Fail2Ban, Firewall, Hacking, HashiCorp, HCL, HIPAA, IDS, Intrusion, Intrusion Detection, Intrusion Detection System, JSON, Least Privilege, Linux, LUKS, Lynis, macOS, Malware, Master Key, Passphrase, Password, Password Policy, PCI, PCI Compliant, Pen testing, Permissions, Root, Secrets, Secrets Engine, Security, Security Policy, Security Practices, Server, Shamir's Secret Sharing, SQL Injection, Threat, Threat Stack, Tool, Ubuntu, Vault, VPN, Vulnerability, Window, Zero Trust

Reading Time: 7 minutes

What is Vault?

Vault protects and secures access to multiple types of confidential data. It stores and manages sensitive password information, API keys, and access tokens that exist in a low trust environment and generates dynamic access to authenticate users to ensure they have authorized ingress and availability to a file, location, service, or application.

Continue reading →

What is the Difference Between AI and Machine Learning?

Posted on September 11, 2020 by Danny Jensen

Category: Tutorials | Tags: Adaptive Methods, Algorithms, bayesian, classification, Clustering, Confusion Matrix, Convergence, Convolutional Neural Networks, decision tree, evolutionary algorithms, fault diagnosis, generalization, Gradient Descent, Learning Mode, Natural Language Understanding, Normalization, parameter tuning, Pattern Identification Recognition, Prediction Predictive Analytics, Probabilistic Graphical Models, Regression, reinforcement learning, Supervised Learning, svm, Unsupervised Learning

Reading Time: 4 minutes

Introduction

Both machine learning (ML) and artificial intelligence (AI) are fundamental parts of humanity’s quest to develop brilliant, self-educating, learning machines. While the two terms may sound similar, in reality many differences define them. It is enough to ask ourselves this simple question to better understand the difference between AI and ML:
How do we teach a machine?

Continue reading →

Introduction to Machine Learning

Posted on January 30, 2020 by Matt Holtz

Category: Tutorials | Tags: Adaptive Methods, Algorithms, bayesian, classification, Clustering, Confusion Matrix, Convergence, Convolutional Neural Networks, decision tree, evolutionary algorithms, fault diagnosis, generalization, Gradient Descent, Learning Mode, Natural Language Understanding, Normalization, parameter tuning, Pattern Identification Recognition, Prediction Predictive Analytics, Probabilistic Graphical Models, Regression, reinforcement learning, Supervised Learning, svm, Unsupervised Learning

Reading Time: 7 minutes

Two of the biggest catchphrases being thrown around today in computing are Artificial Intelligence (AI) and Machine Learning. Many times people use them interchangeably. The truth is that AI encompasses a lot more than just Machine Learning, but Machine Learning is one of the most promising aspects of AI.

Continue reading →

What Can Machine Learning Do? [Infographic]

Posted on May 3, 2019 by Echo Diaz | Updated: April 27, 2023

Category: Featured Articles | Tags: Adaptive Methods, Algorithms, bayesian, classification, Clustering, Confusion Matrix, Convergence, Convolutional Neural Networks, decision tree, evolutionary algorithms, fault diagnosis, generalization, Gradient Descent, Learning Mode, Machine Learning, Natural Language Understanding, Normalization, parameter tuning, Pattern Identification Recognition, Prediction Predictive Analytics, Probabilistic Graphical Models, Regression, reinforcement learning, Supervised Learning, svm, Unsupervised Learning

Reading Time: < 1 minute

What can machine learning do?

Continue reading →

What is Machine Learning?

Posted on May 1, 2019 by Lance Rangel

Category: Other | Tags: Adaptive Methods, Algorithms, bayesian, classification, Clustering, Confusion Matrix, Convergence, Convolutional Neural Networks, Data, decision tree, evolutionary algorithms, fault diagnosis, generalization, Gradient Descent, intro, Learning Mode, Machine Learning, Natural Language Understanding, Normalization, parameter tuning, Pattern Identification Recognition, Prediction Predictive Analytics, Probabilistic Graphical Models, Regression, reinforcement learning, Supervised Learning, svm, Unsupervised Learning

Reading Time: 3 minutes

It was 2017 when American businessman Mark Cuban said that if you don’t understand artificial intelligence, deep learning and machine learning “you’ll be a dinosaur within three years.” Time will tell whether he is right, but if his theory has substance, some companies are well into the 12-month countdown of becoming extinct.

Continue reading →