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Implementing Approximate Nearest Neighbor Search with KD-Trees

PyImageSearch

We will start by setting up libraries and data preparation. Setup and Data Preparation For implementing a similar word search, we will use the gensim library for loading pre-trained word embeddings vector. My mission is to change education and how complex Artificial Intelligence topics are taught.

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Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock

AWS Machine Learning Blog

We discuss the important components of fine-tuning, including use case definition, data preparation, model customization, and performance evaluation. This post dives deep into key aspects such as hyperparameter optimization, data cleaning techniques, and the effectiveness of fine-tuning compared to base models.

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End-to-End model training and deployment with Amazon SageMaker Unified Studio

Flipboard

Organizations need a unified, streamlined approach that simplifies the entire process from data preparation to model deployment. To address these challenges, AWS has expanded Amazon SageMaker with a comprehensive set of data, analytics, and generative AI capabilities.

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Large Language Models: A Self-Study Roadmap

Flipboard

By Kanwal Mehreen , KDnuggets Technical Editor & Content Specialist on July 7, 2025 in Language Models Image by Author | Canva Large language models are a big step forward in artificial intelligence. They can predict and generate text that sounds like it was written by a human.

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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

This strategic decision was driven by several factors: Efficient data preparation Building a high-quality pre-training dataset is a complex task, involving assembling and preprocessing text data from various sources, including web sources and partner companies. The team opted for fine-tuning on AWS.

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Supervised vs Unsupervised Learning: Key Differences

How to Learn Machine Learning

It groups similar data points or identifies outliers without prior guidance. Type of Data Used in Each Approach Supervised learning depends on data that has been organized and labeled. This data preparation process ensures that every example in the dataset has an input and a known output.

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Building a RAG chatbot with LangChain, Chroma, Hugging Face, and Arcee Conductor

Julien Simon

Data Preparation The first step in building the RAG chatbot is to prepare the data. In this case, the data consists of PDF documents, which can be research articles or any other PDF files of your choice. Its recommended to use a virtual environment to manage dependencies and avoid conflicts with other projects.