Pytorch

Try These 4 Advanced Optimization Techniques in Deep Learning If Your PyTorch Optimizer Isn’t Performing Well

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Try These 4 Advanced Optimization Techniques in Deep Learning If Your PyTorch Optimizer Isn't Performing Well

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

13 Practical Image Augmentation Techniques in Pytorch (With Code)

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13 Practical Image Augmentation Techniques in Pytorch (With Code)

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

Common Methods for Building Neural Networks with PyTorch

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Common Methods for Building Neural Networks with PyTorch

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

Mastering Neural Network Construction With PyTorch in Ten Minutes

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Mastering Neural Network Construction With PyTorch in Ten Minutes

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

Essence of PyTorch: Tensors and Tensor Operations

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Essence of PyTorch: Tensors and Tensor Operations

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

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

Principles and Implementation of Diffusion Models in PyTorch

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Principles and Implementation of Diffusion Models in PyTorch

MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning in China and abroad, … Read more

Normalization Techniques Evolution: In-Depth Analysis of Six Key PyTorch Normalization Layers

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This article is about 4200 words long, recommended reading time is over 10 minutes. This article provides a detailed introduction to several key normalization techniques in deep learning. Normalization is a key concept in deep learning that ensures faster convergence, more stable training, and better overall performance. PyTorch includes several normalization layers, and we will … Read more

Implementing Denoising Diffusion Probabilistic Model (DDPM) from Scratch in PyTorch

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Implementing Denoising Diffusion Probabilistic Model (DDPM) from Scratch in PyTorch

Source: DeepHub IMBA This article is approximately 6500 words long and is recommended to be read in 13 minutes. This article will build a basic unconditional diffusion model, namely the Denoising Diffusion Probabilistic Model (DDPM). Diffusion models are typically a type of generative deep learning model that creates data by learning the denoising process. There … Read more