Jonathan Sands

"We’ll review how pandas handles dates and times in this Quick Success Data Science article. The goal is to introduce you to the basics of working with time series in pandas and to make you conversant in the subject." Introducing Time Series in pandas by Lee Vaughan

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Graph-based Euclidean clustering is reshaping point cloud segmentation by automating processes, reducing noise, and offering robust scalability for complex scenes. Dive deeper in this article by Dr. Florent POUX. #MachineLearning #Clustering

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An underrated component of RAG evals is retrieval evals - is potentially more important than LLM evals! Some requirements for performing retrieval evals is 1) identifying the right retrieval metrics, and 2) having a unified dataset representation. This article by Ross Ashman takes you through tour of retrieval/ranking metrics (e.g. precision@K, NDCG, and more), and also shows you how to convert a dataset into the BEIR format in order to run these evals. Once retrieval evals is setup, run it over your LlamaIndex retrievers. https://lnkd.in/gfha8kiX

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A Survey of RAG and RAU: Advancing Natural Language Processing with Retrieval-Augmented Language Models Quick read: https://lnkd.in/gnGqk44W Paper: https://lnkd.in/g8-VftbX #llms #largelanguagemodels #ai

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A survey on MultiModal Large Language Models covers: - cost-effective training strategies - comprehensive model architecture and training pipeline overview - performance evaluation on mainstream benchmarks It's a deep dive into 26 cutting-edge models https://lnkd.in/esjakMbz

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"Deep data scientists and data engineers are not replaceable. That said, many people without the word 'data' in their title are already doing analytics and data science work," writes Jignesh Patel in this article for TDWI. That AI and its role in data science is a hot-button issue is not novel, but honest and informed conversations about its impact on data jobs can be hard to find. This article offers a sincere assessment of how generative AI, much like any other broad technological advancement, will impact—not impede— data analysis jobs. Click the link below to read the full article, and let us know what you think in the comments⤵️ https://lnkd.in/ebHyi583 #AI #machinelearning #datascience

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BITE SIZED DATA-SCIENCE -->> Day #4 https://lnkd.in/g94tGDFq This next video covers the np array( ) method, pertaining to single dimentional and muti-dimensional data structures. When we explore these, what is DIMENSION and SHAPE as it pertains to rows and columns?

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Looking to optimize your RAG retrieval system but short on time? We've got you covered! 🙌 Our team has created a video summary of our article on evaluation of RAG Retrieval Chunking Methods. In just one minute, you'll discover insights and pro tips you need to boost your RAG retrieval performance. 📈 🔍 Key highlights: - The top-performing chunking method that outshined the competition by 10% - The surprising effectiveness of the TinyBERT reranker - Why sentences are the ideal units for chunking - Actionable strategies for maximizing your RAG retrieval pipeline Whether you're a seasoned professional or just starting out, this video is packed with valuable information to help you stay ahead of the curve. 🌟 📹 Watch the video: https://buff.ly/4b2CqtQ 📄 Read the full article: https://buff.ly/4aZt5TF #RAGRetrieval #DataScience #MachineLearning #Efficiency #Optimization #Superlinked

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In this tutorial, you’ll learn to summarize a complete book considering the price and the contextual limit of the model. #AIEngineering #LargeLanguageModels #DataScience by Usama Jamil thanks to MyScale

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CI/CD in ML has unique challenges like data tracking, non-deterministic outcomes, and governance. Learn how Weights & Biases can streamline CI/CD for ML—from dataset management to automated model delivery. Explore the full workflow: https://lnkd.in/grtzYUA5

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이현민, SeungYoung Oh, Jin-Hyun Han, Hyunggu Jung (2024). Creating a bias-free dataset with food delivery app reviews under data poisoning attack. Data in Brief, June 2024. Read and share here: https://lnkd.in/gv7GeDVQ #ai #hci #bias #poisoningattack #fairnessai #visualization #hcail

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In our video library: 📢 Intro to NumPy Array Operations 🗣️ @meenaljhajharia #python #datascience And, subscribe to our YT channel for more videos. https://lnkd.in/eX2gisPJ

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Hypothesis testing involves testing whether a hypothesis or inference is true. And it's important when you're working with different datasets. Here, Nelson explains how hypothesis testing works in Python with Numpy & how to interpret the results. https://lnkd.in/gJM7scDQ

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💡 Check this #EINST4INE #blog on clustering texts using Latent Dirichlet Allocation (LDA) by #ESR Finn Tryggvason! The post explores how #LDA helps identify hidden themes in documents, making it easier to #analyze and #categorize vast amounts of #information. Read more: https://lnkd.in/dPs39P9M University of Stuttgart #EINST4INE #MSCA #H2020 #ITN #DItigalTransformation #MachineLearning #LDA #DataScience #TextClustering #AI

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