Remove Artificial Intelligence Remove Data Preparation Remove Definition
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Implementing Approximate Nearest Neighbor Search with KD-Trees

PyImageSearch

With reaching billions, no hardware can process these operations in a definite amount of time. 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.

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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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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. It equips you to build and deploy intelligent systems confidently and efficiently.

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Machine learning algorithms

Dataconomy

Their application spans a wide array of tasks, from categorizing information to predicting future trends, making them an essential component of modern artificial intelligence. Machine learning algorithms are specialized computational models designed to analyze data, recognize patterns, and make informed predictions or decisions.

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A comprehensive comparison of RPA and ML

Dataconomy

Robotic process automation vs machine learning is a common debate in the world of automation and artificial intelligence. Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. What is machine learning (ML)?

ML 133
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Fine-tune large language models with Amazon SageMaker Autopilot

Flipboard

We use Amazon SageMaker Pipelines , which helps automate the different steps, including data preparation, fine-tuning, and creating the model. We demonstrated an end-to-end solution that uses SageMaker Pipelines to orchestrate the steps of data preparation, model training, evaluation, and deployment.

AWS 130
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Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

AWS Machine Learning Blog

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

AWS 123