Remove 2015 Remove Data Preparation Remove Machine Learning
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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services.

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MLOps and the evolution of data science

IBM Journey to AI blog

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

professionals

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Top 10 Deep Learning Platforms in 2024

DagsHub

Source: Author Introduction Deep learning, a branch of machine learning inspired by biological neural networks, has become a key technique in artificial intelligence (AI) applications. Deep learning methods use multi-layer artificial neural networks to extract intricate patterns from large data sets.

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3 Takeaways from Gartner’s 2018 Data and Analytics Summit

DataRobot Blog

Today’s data management and analytics products have infused artificial intelligence (AI) and machine learning (ML) algorithms into their core capabilities. These modern tools will auto-profile the data, detect joins and overlaps, and offer recommendations. 2) Line of business is taking a more active role in data projects.

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Why is Git Not the Best for ML Model Version Control

The MLOps Blog

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., Most of its products use machine learning or deep learning models for some or all of their features.

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A Guide to Convolutional Neural Networks

Heartbeat

ResNet is a deep CNN architecture developed by Kaiming He and his colleagues at Microsoft Research in 2015. It consists of up to 152 layers and uses a novel “residual block” architecture that allows the network to learn residual connections between layers. The data should be split into training, validation, and testing sets.

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Image Segmentation with U-Net in PyTorch: The Grand Finale of the Autoencoder Series

PyImageSearch

The dataset provides a rich resource for exploring advanced machine learning techniques, especially in image segmentation. Key steps encompass: Data preparation and splitting into training and validation sets. As depicted in Figure 1 , the architecture resembles the letter ‘U’, giving rise to its name, U-Net.