Remove 2015 Remove Data Preparation Remove Deep Learning
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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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MLOps and the evolution of data science

IBM Journey to AI blog

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.

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A Guide to Convolutional Neural Networks

Heartbeat

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.

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

PyImageSearch

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 ).