Reprinted from | Xinzhiyuan Author | JinHui Source | https://zhuanlan.zhihu.com/p/220403674 1 『Visualization of Network Structure』 When training a neural network, in addition to observing the trend of the loss function with each step or epoch to establish a basic understanding of the network optimization, we can also use some additional visualization libraries to visualize our … Read more
Source: Deephub Imba This article is approximately 3200 words long and is recommended to be read in 7 minutes. This article will introduce gradient checkpointing, a technique that allows you to train larger models on the GPU at the cost of increased training time. We will implement it in PyTorch and train a classifier model. … Read more
Click on the above “Beginner’s Guide to Vision”, select to add “Star” or “Top” Heavyweight content, delivered first time Author:Igor Susmelj Translation: ronghuaiyang Introduction Since PyTorch 1.0, more and more people have chosen to use PyTorch. Today, we introduce a GitHub project where the author summarizes a very useful set of best practices for using … Read more
Author: Jack Chih-Hsu Lin<br/>Translator: Chen Zhi Yan<br/>Proofreader: Wang Zi Yue<br/><br/><br/><br/>About 4600 words, recommended reading time 9 minutes.<br/>18 essential PyTorch speed-up tips: how they work and methods. Adjusting the deep learning pipeline is like finding the right gear combination (Image source: Tim Mossholder) Why Read This Blog? The training/inference process of deep learning models involves multiple … Read more
Click on the above “Beginner Learning Vision” to select “Star” or “Top” Important content delivered first Author: Aryan Jadon Source: DeepHub IMBA Editor: Extreme City Platform Guide to Extreme City This article briefly summarizes four methods of few-shot learning image classification algorithms and implements a simple classification model using PyTorch, along with operational code. In … Read more
Follow our official account “ML_NLP“ Set as “Starred“, heavy content delivered first hand! Author | Tirthajyoti Sarkar Source | Medium Editor | Code Doctor Team Introduction This article will demonstrate a simple step-by-step process to build a PyTorch 2-layer neural network classifier (fully connected) to illustrate some key features and styles. PyTorch provides great flexibility … Read more
Author | hyk_1996 Source: CSDN Blog Compiled by: Da Bai 1. Difference in Effects of nn.Module.cuda() and Tensor.cuda() Both the cuda() function can achieve memory migration from CPU to GPU for models and data, but their effects differ. For nn.Module: model = model.cuda() model.cuda() The above two lines achieve the same effect, which is memory … Read more
Click on the above“Beginner’s Guide to Vision” to select and add a Bookmark or “Pin” Important content delivered promptly This article is reprinted from: AI Park Introduction While there are other ways to achieve the same results, these operations make usage more convenient. PyTorch is a scientific package based on Python that performs advanced operations … Read more
This article discusses the development, principles, and code practices of diffusion models. The catalyst for diffusion models began with the introduction of DDPM (Denoising Diffusion Probabilistic Model) in 2020. Before delving into the details of how the Denoising Diffusion Probabilistic Model (DDPM) works, let’s first look at some developments in existing generative artificial intelligence, which … Read more
Click on the above “Beginner’s Guide to Vision” to select and add “Star” or “Pin“ Heavy content delivered first Author | OpenMMLab Editor | Jishi Platform Original link: https://zhuanlan.zhihu.com/p/430123077 Introduction With the rapid development of deep learning, the explosion of model parameters has raised increasingly high requirements for GPU memory capacity. How to train models … Read more