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LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

Unite.AI

Second, the White-Box Preset implements simple interpretable algorithms such as Logistic Regression instead of WoE or Weight of Evidence encoding and discretized features to solve binary classification tasks on tabular data. Finally, the CV Preset works with image data with the help of some basic tools.

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Training a Custom Image Classification Network for OAK-D

PyImageSearch

Table of Contents Training a Custom Image Classification Network for OAK-D Configuring Your Development Environment Having Problems Configuring Your Development Environment? Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g.,

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Understanding Graph Neural Network with hands-on example| Part-1

Becoming Human

Photo by NASA on Unsplash Hello and welcome to this post, in which I will study a relatively new field in deep learning involving graphs — a very important and widely used data structure. This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications.

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How to Use Hugging Face Pipelines?

Towards AI

A practical guide on how to perform NLP tasks with Hugging Face Pipelines Image by Canva With the libraries developed recently, it has become easier to perform deep learning analysis. Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more.

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Build an image-to-text generative AI application using multimodality models on Amazon SageMaker

AWS Machine Learning Blog

For instance, in ecommerce, image-to-text can automate product categorization based on images, enhancing search efficiency and accuracy. CLIP model CLIP is a multi-modal vision and language model, which can be used for image-text similarity and for zero-shot image classification.

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Introduction to Graph Neural Networks

Heartbeat

They are as follows: Node-level tasks refer to tasks that concentrate on nodes, such as node classification, node regression, and node clustering. Edge-level tasks , on the other hand, entail edge classification and link prediction. Graph-level tasks involve graph classification, graph regression, and graph matching.

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Hosting ML Models on Amazon SageMaker using Triton: XGBoost, LightGBM, and Treelite Models

AWS Machine Learning Blog

With the ability to solve various problems such as classification and regression, XGBoost has become a popular option that also falls into the category of tree-based models. In this post, we dive deep to see how Amazon SageMaker can serve these models using NVIDIA Triton Inference Server. The outputs are then returned.

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