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ML interpretability

Dataconomy

ML Interpretability is a crucial aspect of machine learning that enables practitioners and stakeholders to trust the outputs of complex algorithms. What is ML interpretability? To fully grasp ML interpretability, it’s helpful to understand some core definitions.

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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.

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Accelerating ML experimentation with enhanced security: AWS PrivateLink support for Amazon SageMaker with MLflow

AWS Machine Learning Blog

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker , users want a seamless and secure way to experiment with and select the models that deliver the most value for their business.

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Muvera: Making multi-vector retrieval as fast as single-vector search

Hacker News

The embeddings are generally compared via the inner-product similarity , enabling efficient retrieval through optimized maximum inner product search (MIPS) algorithms. We have provided an open-source implementation of our FDE construction algorithm on GitHub.

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ML Project – Insurance Claim Approval using XGBoost Algorithm

Data Flair

Program 1 Insurance Claim Approval # Step 1: Import required libraries import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from xgboost import XGBClassifier from sklearn.metrics import accuracy_score, confusion_matrix import matplotlib.pyplot... The post ML Project – (..)

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Regularization algorithms

Dataconomy

Regularization algorithms play a crucial role in enhancing the performance of machine learning models by addressing one of the most significant challenges: overfitting. What are regularization algorithms? Regularization algorithms are techniques designed to prevent overfitting in machine learning models.

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How I Automated My Machine Learning Workflow with Just 10 Lines of Python

Flipboard

The world’s leading publication for data science, AI, and ML professionals. You don’t need deep ML knowledge or tuning skills. Why Automate ML Model Selection? Identify top-performing algorithms based on accuracy, F1 score, or RMSE. It’s not just convenient, it’s smart ML hygiene. These are lazypredict and pycaret.