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A Complete Guide to Matplotlib: From Basics to Advanced Plots

KDnuggets

Annotate when appropriate: ax.annotate(Record Sales in 2020!, xy=(2020, 400), xytext=(2018, 350), arrowprops=dict(facecolor=black, shrink=0.05)) Leveling Up: Advanced Techniques 1. First thing to do: import Matplotlib in the conventional way. Annotate Key Points Is there a data point that needs some extra explanation?

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Scaling de-duplication in WorldCat: Balancing AI innovation with cataloging care | OCLC

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Unify structured data in Amazon Aurora and unstructured data in Amazon S3 for insights using Amazon Q

AWS Machine Learning Blog

You can verify the output by cross-referencing the PDF, which has a target as $12 million for the in-store sales channel in 2020.

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Why You Need RAG to Stay Relevant as a Data Scientist

KDnuggets

Patrick Lewis first introduced RAG in this academic article first in 2020. Starting with the academic article that introduced it and how it’s now used to cut costs when working with large language models (LLMs). But first, let’s cover the basics. What is Retrieval-Augmented Generation (RAG)? The idea behind this is simple.

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Racing into the future: How AWS DeepRacer fueled my AI and ML journey

AWS Machine Learning Blog

Image 3 – Unitary at the AWS London Summit showcasing dynamically scaled inference using 1000+ EKS nodes 2020-2023: Virtual racing and continued growth The COVID-19 pandemic shifted AWS DeepRacer competitions online for 2020 and 2021. Despite this, exciting events like the AWS DeepRacer F1 Pro-Am kept the community engaged.

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Build Interactive Machine Learning Apps with Gradio

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In this blog, we’ll take a fun, hands-on approach to learning the key Gradio components by building a text-to-speech (TTS) web application that you can run on an AI PC or Intel® Tiber™ AI Cloud and share with others. list_models()) For this blog, we will use the XTTS-v2 model, which supports 17 languages and 58 speaker voices.

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3D Gaussian Splatting vs NeRF: The End Game of 3D Reconstruction?

PyImageSearch

In the 1st blog of this series , you were introduced to Photogrammetry, which is based on 3D Reconstruction via heavy geometry. And in the 2nd blog of this series , you were introduced to NeRFs, which is 3D Reconstruction via Neural Networks, projecting points in the 3D space. And how is that done? Yes, using COLMAP! Who knows?