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Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Han et al.: https://lnkd.in/gh8qfi4k #ArtificialIntelligence #DeepLearning #MachineLearning

Data is the lifeblood of #MachineLearning, and #KnowledgeGraphs are the heart that pumps it. Find out how in our new article about the magic of Knowledge Graphs and ML. #NebulaGraph #NebulaGraphDB

Data is the lifeblood of #MachineLearning, and #KnowledgeGraphs are the heart that pumps it. Find out how in our new article about the magic of Knowledge Graphs and ML. #NebulaGraph #NebulaGraphDB

A Comprehensive Guide to Model Evaluation in R: Step-by-Step with Examples by Shreya Singh https://buff.ly/4dTAUwx #rstats

Linear models play a crucial role in data analysis by determining if it's feasible to predict the target variable using available features. This exploration is often referred to as detecting a signal within the data. Assessing the error of a linear model against more intricate models can aid in the decision making process of opting for complexity. #DataAnalysis #LinearModels #PredictiveAnalytics

🔍 Exploring the world of Data Signal Processing through innovative projects! 🚀 From filtering techniques to feature extraction, join us on a journey of decoding signals into valuable insights. 💡 #datasignalprocess #datascience #projects #innovation

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Han et al.: https://lnkd.in/gXKc4aR5 #ArtificialIntelligence #DeepLearning #MachineLearning

From combining graphs to limiting node influence and adjusting the number of edges, Eivind Kjosbakken outlines several powerful tactics for improving your graphs to give your models' performance a boost.

"This is achieved by organizing the graph into multiple layers, with each layer representing a different granularity of the dataset. The top layers contain fewer nodes, which represent clusters of data points, while the bottom layers contain individual data points." #knowledgegraphs #artificialintelligence #searchsystems #HNSWgraphs #HNSW #searchalgorithms #vectordatabases