DagsHub

JULY 25, 2024

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.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 19, 2024

This session covers the technical process, from data preparation to model customization techniques, training strategies, deployment considerations, and post-customization evaluation. Explore how this powerful tool streamlines the entire ML lifecycle, from data preparation to model deployment.

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IBM Journey to AI blog

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

Heartbeat

AUGUST 21, 2023

AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. ResNet is a deep CNN architecture developed by Kaiming He and his colleagues at Microsoft Research in 2015. The data should be split into training, validation, and testing sets.

Natural Language Processing 52

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PyImageSearch

NOVEMBER 6, 2023

This configuration ensures that our model is trained efficiently and effectively, leveraging the best practices in deep learning. The torch library is essential for our deep learning tasks, while the Dataset class from torch.utils.data provides a template for creating custom datasets ( Lines 7 and 9 ).

Deep Learning 67

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The MLOps Blog

NOVEMBER 30, 2022

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.

ML 52

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