Distortions in AI-generated videos can be technically referred to as artifacts or generative artifacts. These are unintended visual or auditory anomalies that occur due to limitations in the AI model, training data, or generation process. Common types of distortions include:

Standardizing data is a vital part of the data cleaning process. It guarantees consistency and uniformity within the dataset, which is essential for precise analysis, reporting, and machine learning models, leading to optimal data integrity. Depending on the data’s nature and the analysis requirements, standardizing data can involve a variety of transformations and operations. The main aspects of data standardization include:
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Removing duplicates in MySQL involves identifying and deleting rows with duplicate data to ensure the integrity and accuracy of your database. This article will serve as a step-by-step guide on how to remove duplicates in MySQL.
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Handling missing values is a critical step in data preprocessing, particularly in data science and machine learning projects, because many algorithms do not function properly or may produce misleading results if the data contains missing or null values. Let’s take a look at the key strategies and considerations for handling missing data.
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Data cleaning is an essential step in the data analysis process. It involves preparing and transforming raw data into a more useful and accurate format. SQL (Structured Query Language) is a powerful tool for data cleaning because it can handle large datasets efficiently and provides various functions and operations to manipulate data.
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Choosing between Retrieval-Augmented Generation (RAG) and fine-tuning depends on the specific requirements, resources, and goals of your application. Here are scenarios when each approach is more sensible.

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Retrieval-Augmented Generation (RAG) and fine-tuning are two approaches used in the development and enhancement of language models. Each has its own strengths, applications, and considerations. Here’s a detailed comparison. Let’s have a look. …

In the context of Retrieval-Augmented Generation (RAG) in AI, chunking refers to the process of dividing a large body of text or a dataset into smaller, manageable segments or “chunks” before feeding them into the system. This is particularly important in scenarios where a model needs to retrieve relevant information from a large corpus to generate accurate and contextually appropriate responses.
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RAG stands for Retrieval-Augmented Generation. RAG is a technique used in natural language processing (NLP) that aims to improve the quality of generated text by incorporating external information which the system retrieves from a large corpus aka documents or databases. Let’s take a closer look at how RAG works.
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In this article we are going to learn more about inpainting, more specifically about adding an object to the generated image. But let’s start with the first question that comes is “what is inpainting with DALL-E”?

Inpainting with DALL-E refers to the process of modifying an image by replacing or editing specific parts of the generated image using the capabilities of the DALL-E model. In this article we are going to add an object to the generated image.
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