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Latent Semantic Analysis and its Uses in Natural Language Processing

Analytics Vidhya

The post Latent Semantic Analysis and its Uses in Natural Language Processing appeared first on Analytics Vidhya. Textual data, even though very important, vary considerably in lexical and morphological standpoints. Different people express themselves quite differently when it comes to […].

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5 Error Handling Patterns in Python (Beyond Try-Except)

KDnuggets

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 5 Error Handling Patterns in Python (Beyond Try-Except) Stop letting errors crash your app.

Python 171
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Discover your potential: 5 Data Science projects to help you stand out as a Python student

Data Science Dojo

In this blog post, we’ll explore five project ideas that can help you build expertise in computer vision, natural language processing (NLP), sales forecasting, cancer detection, and predictive maintenance using Python.

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Top 17 trending interview questions for AI Scientists

Data Science Dojo

These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential. This is used for tasks like clustering, dimensionality reduction, and anomaly detection. What are some emerging AI applications that excite you?

AI 364
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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). Why we did it? It is a nice show-case many people are interested in.

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Syngenta develops a generative AI assistant to support sales representatives using Amazon Bedrock Agents

Flipboard

Agent architecture The following diagram illustrates the serverless agent architecture with standard authorization and real-time interaction, and an LLM agent layer using Amazon Bedrock Agents for multi-knowledge base and backend orchestration using API or Python executors. Domain-scoped agents enable code reuse across multiple agents.

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How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model

AWS Machine Learning Blog

During the training process, our SageMaker HyperPod cluster was connected to this S3 bucket, enabling effortless retrieval of the dataset elements as needed. The deduplication process involved embedding dataset elements using a text embedder, then computing cosine similarity between the embeddings to identify similar elements.