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Author丨Mia Luo @ Zhihu (Authorized Reprint) Source丨https://zhuanlan.zhihu.com/p/486360176Editor丨Xiaoshutong, Jizhi Shutong 1. Background Introduction The analysis of the PyTorch memory management mechanism mainly aims to reduce the impact of “memory fragmentation”. A simple example is: As shown in the figure above, suppose we want to allocate 800MB of memory. Although the total free memory is 1000MB, the … Read more
MLNLP ( Machine Learning Algorithms and Natural Language Processing ) community is a well-known natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The vision of the community is to promote communication between the academic and industrial fields of natural language processing and machine learning, … Read more
Click the above“Beginner Learning Vision” to choose to add Star or Pin. Important content delivered at the first time Dog Sprint Output from DINO Model Unlabeled Self-Distillation (DINO) The article “Reconstructing Complete Images from Several ‘Patches’ | Building Scalable Learners with Masked Autoencoders” discusses how to build scalable learners, continuing my series on vision transformers, … Read more
Source: DeepHub IMBA This article is approximately 3700 words long, recommended reading time is over 10 minutes. This article will introduce four advanced optimization techniques that may outperform traditional methods in certain tasks, especially when faced with complex optimization problems. In the field of deep learning, the choice of optimizer is crucial for model performance. … Read more
Author丨Jiefa Shouzhangsheng@Zhihu Link丨https://zhuanlan.zhihu.com/p/559887437 Using data augmentation techniques can increase the diversity of images in the dataset, thereby improving the model’s performance and generalization ability. The main image augmentation techniques include: Resizing Grayscale Transformation Normalization Random Rotation Center Cropping Random Cropping Gaussian Blur Brightness and Contrast Adjustment Horizontal Flip Vertical Flip Gaussian Noise Random Blocks Central … Read more
Click the above “Mechanical and Electronic Engineering Technology” to follow us The following uses various methods to construct the network structure shown in the figure below: 1.1 Inheriting nn.Module Base Class to Build Model import torch from torch import nn import torch.nn.functional as F class Model_Seq(nn.Module): """ Build model by inheriting the base class nn.Module … Read more
Click the above “Beginner Learning Vision” to choose to add Star Mark or “Top” Important content delivered immediately I recently started using PyTorch and have encountered quite a few pitfalls. I’m recording them here as I feel they are common issues that many might face. I would also like to thank my friends who helped … Read more
Click the above “Beginner Learning Vision”, select to add Star or “Pin” Essential Knowledge Delivered at First Time In the field of deep learning (including areas built with ChatGPT), one of the most important libraries is PyTorch. Like the TensorFlow framework, PyTorch is one of the most renowned neural network training frameworks available for software … Read more
Editor’s Note In this article, we will implement a simple neural network from scratch using PyTorch. Before reading this article, we assume you already understand how neural networks work. Reprinted from丨GuYueJu Recently, I noticed some beginner friends around me holding various PyTorch guides, typing code while reading, and in the end, they became mere typists. … Read more
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