Loss Functions

Implementing CNN From Scratch: A Detailed Explanation of Convolutional Networks’ Mathematical Essence

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Implementing CNN From Scratch: A Detailed Explanation of Convolutional Networks' Mathematical Essence

| Full text is 4175 words, estimated reading time is 4 minutes | This article is authorized to be reprinted by Machine Heart (WeChat public account: almosthuman2014), and secondary reprinting is prohibited. Excerpted from: arXiv Translation: Machine Heart Contributors: Huang Xiaotian, Lu Xue, Jiang Siyuan Recently, researchers from Nanyang Technological University published a paper describing … Read more

5 Types of Regression Loss Functions Every Beginner in Machine Learning Should Know

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5 Types of Regression Loss Functions Every Beginner in Machine Learning Should Know

All algorithms in machine learning rely on minimizing or maximizing a function, which we call the “objective function.” The function that is minimized is called the “loss function,” which measures the model’s ability to predict the expected outcome. The most commonly used method for minimizing the loss function is the “gradient descent method.” You can … Read more

Introduction to TensorFlow Image Segmentation Loss Functions

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In previous articles, we have introduced two types of image segmentation loss functions. Today, we will share commonly used multi-class image segmentation loss functions such as multi-class cross-entropy, weighted multi-class cross-entropy, multi-class Dice coefficient, multi-class Focal Loss, etc., and provide code to reproduce the above loss functions in TensorFlow. 1. Cross Entropy The cross-entropy loss … Read more

Introduction to Image Segmentation Loss Functions in TensorFlow

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Commonly used image segmentation loss functions include binary cross-entropy, dice coefficient, Tversky, and Focal Loss. Today, I will reproduce the above loss functions in TensorFlow and compare the results. 1. Cross Entropy The cross-entropy loss function compares the predicted class values with the target values on a pixel-by-pixel basis, and then averages the values over … Read more

An Overview of Nineteen Loss Functions in Pytorch

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An Overview of Nineteen Loss Functions in Pytorch

Click the "Little White Learns Vision" above, choose to add "Star" or "Top" Heavy content delivered to you immediately For academic sharing only, does not represent the position of this public account, please contact for deletion if there is any infringement Reprinted from:Author:mingo_敏 Original link: https://blog.csdn.net/shanglianlm/article/details/85019768 Introduction This article summarizes nineteen loss functions, introducing their … Read more

19 Types of Loss Functions in Pytorch

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19 Types of Loss Functions in Pytorch

Source: Algorithm Advancement This article is about 1800 words, suggested reading time is 8 minutes. This article introduces you to different types of loss functions. Source: CSDN-mingo_敏 Address: https://blog.csdn.net/shanglianlm/article/details/85019768 Basic Usage criterion = LossCriterion() # The constructor has its own parameters loss = criterion(x, y) # Call standard also has parameters 19 Types of Loss … Read more

Summary of PyTorch Loss Functions

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Summary of PyTorch Loss Functions

Source: Pythonic Biologist This article is about 1900 words long, and it is recommended to read it in 8 minutes. TensorFlow and PyTorch are quite similar; this article introduces loss functions using PyTorch as an example. 19 Types of Loss Functions 1. L1 Loss L1Loss Calculates the absolute difference between output and target. torch.nn.L1Loss(reduction='mean') Parameters: … Read more

Understanding the Mathematical Essence of Convolutional Networks

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Understanding the Mathematical Essence of Convolutional Networks

Researchers from the South China University of Technology have published a paper that describes the mathematical principles of convolutional networks. This paper explains the operations and propagation processes of convolutional networks from a mathematical perspective.This paper is very helpful for understanding the mathematical essence of convolutional networks and assists readers in implementing convolutional networks “by … Read more

Understanding the Mathematical Essence of Convolutional Networks

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Understanding the Mathematical Essence of Convolutional Networks

Recently, researchers from Nanyang Technological University published a paper that describes the mathematical principles of convolutional networks. This paper explains the operations and propagation processes of convolutional networks from a mathematical perspective. It is very helpful for understanding the mathematical essence of convolutional networks and aids readers in implementing convolutional networks “from scratch” (without using … Read more