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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

Flipboard

In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience. The following diagram shows our solution architecture.

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What are the Different Types of Transformers in AI

Mlearning.ai

Understanding the biggest neural network in Deep Learning Join 34K+ People and get the most important ideas in AI and Machine Learning delivered to your inbox for free here Deep learning with transformers has revolutionized the field of machine learning, offering various models with distinct features and capabilities.

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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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sktime?—?Python Toolbox for Machine Learning with Time Series

ODSC - Open Data Science

Here’s what you need to know: sktime is a Python package for time series tasks like forecasting, classification, and transformations with a familiar and user-friendly scikit-learn-like API. Build tuned auto-ML pipelines, with common interface to well-known libraries (scikit-learn, statsmodels, tsfresh, PyOD, fbprophet, and more!)

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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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UC Berkeley Researchers Propose CRATE: A Novel White-Box Transformer for Efficient Data Compression and Sparsification in Deep Learning

Marktechpost

The practical success of deep learning in processing and modeling large amounts of high-dimensional and multi-modal data has grown exponentially in recent years. Such a representation makes many subsequent tasks, including those involving vision, classification, recognition and segmentation, and generation, easier.

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Re-imagining Glamour Photography with Generative AI

Mlearning.ai

Life however decided to take me down a different path (partly thanks to Fujifilm discontinuing various films ), although I have never quite completely forgotten about glamour photography. Safety Checker —classification model that screens outputs for potentially harmful content. Image created by the author. Image created by the author.