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ResNet

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May 1, 2024 3 minute read
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ResNet, short for Residual Network, is a type of deep neural network architecture that was introduced in 2015 by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. ResNet has since become one of the most popular and widely used deep learning architectures for a variety of tasks, including image classification, object detection, and semantic segmentation.

What is ResNet?

ResNet is a type of deep neural network architecture that uses a series of residual blocks to improve the accuracy and performance of the network. Residual blocks are a type of skip connection that allows the network to learn from its own mistakes and improve its performance over time.

Why is ResNet important?

Path to ResNet

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Reading list

We've selected three books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in ResNet.
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Deep Learning
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Covers ResNet as part of a broader discussion on deep learning architectures for image classification.
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
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Grokking Deep Learning
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Provides a gentle introduction to machine learning. The book covers the basics of machine learning, as well as more advanced topics such as residual connections and transfer learning.
Grokking Deep Learning
Kindle Edition
$$
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Machine Learning with TensorFlow, Second Edition
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Provides a comprehensive overview of TensorFlow, a popular deep learning library. The book covers the basics of TensorFlow, as well as more advanced topics such as residual connections and transfer learning.
Machine Learning with TensorFlow, Second Edition
Paperback
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Machine Learning with TensorFlow, Second Edition
Kindle Edition
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