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Deep learning

Dataconomy

Deep learning is transforming the landscape of artificial intelligence (AI) by mimicking the way humans learn and interpret complex data. These sophisticated algorithms facilitate a deeper understanding of data, enabling applications from image recognition to natural language processing.

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t-SNE (t-distributed stochastic neighbor embedding)

Dataconomy

Researchers, data scientists, and machine learning practitioners alike have embraced t-SNE for its effectiveness in transforming extensive datasets into visual representations, enabling a clearer understanding of relationships, clusters, and patterns within the data.

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Techniques for Data Scientists to Upskill with Large Language Models

Data Science Dojo

Natural Language Processing (NLP): Data scientists are incorporating NLP techniques and technologies to analyze and derive insights from unstructured data such as text, audio, and video. It is widely used for building and training machine learning models, particularly neural networks. H2O.ai: – H2O.ai

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Exploring the intricacies of deep learning models

Dataconomy

Deep learning models have emerged as a powerful tool in the field of ML, enabling computers to learn from vast amounts of data and make decisions based on that learning. In this article, we will explore the importance of deep learning models and their applications in various fields.

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Monitoring of Jobskills with Data Engineering & AI

Data Science Blog

The data is obtained from the Internet via APIs and web scraping, and the job titles and the skills listed in them are identified and extracted from them using Natural Language Processing (NLP) or more specific from Named-Entity Recognition (NER).

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Build a Search Engine: Setting Up AWS OpenSearch

Flipboard

Amazon OpenSearch Service is a fully managed solution that simplifies the deployment, operation, and scaling of OpenSearch clusters in the AWS Cloud. Figure 2 : Amazon OpenSearch Service for Vector Search: Demo Key Features of AWS OpenSearch Scalability: Easily scale clusters up or down based on workload demands.

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Introduction to applied data science 101: Key concepts and methodologies 

Data Science Dojo

It leverages algorithms to parse data, learn from it, and make predictions or decisions without being explicitly programmed. From decision trees and neural networks to regression models and clustering algorithms, a variety of techniques come under the umbrella of machine learning.