Author: z.defying Reprinted from: Datawhale Table of Contents: 1 Specify GPU ID 2 View Model Layer Output Details 3 Gradient Clipping 4 Expand Dimensions of a Single Image 5 One-Hot Encoding 6 Prevent Out of Memory When Validating Model 7 Learning Rate Decay 8 Freeze Parameters of Certain Layers 9 Use Different Learning Rates for … Read more
Click on the above“Beginner’s Vision Learning” to selectStar or “Top” Important content delivered at the first time Recently, in a research project, I started to understand the importance of image augmentation techniques. The goal of the project is to train a robust generative model capable of reconstructing the original image. The problem being addressed is … 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
Follow our WeChat public account “ML_NLP“ Set as “Starred“, important content delivered first-hand! Produced by Big Data Digest Compiled by: Hong Yingfei, Ning Jing PyTorch is one of the deep learning framework libraries, an open-source deep learning platform from Facebook, providing seamless connection from research prototype to production deployment. This article aims to introduce the … 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
Source: DeepHub IMBA This article is about 4900 words long and is recommended for a reading time of over 10 minutes. Whether running on high-performance GPUs or edge devices, TorchDynamo adapts to provide optimal performance. Optimizing model performance is crucial in deep learning, especially for applications that require fast execution and real-time inference. However, PyTorch … Read more
In the previous article, we introduced OpenAI Sora, which has once again shocked the AI community with its text-to-video model, and mentioned that Sora is essentially a diffusion model + Transformer. This article continues to discuss the development, principles, and coding practices of diffusion models. The catalyst for diffusion models began with the introduction of … Read more
Source: Algorithm Advancement This article is approximately 4200 words long and is recommended for an 8-minute read. This article will introduce key parts of the "Graph Attention Networks" and implement the concepts proposed in the paper using PyTorch. Graph Neural Networks (GNN) are a powerful class of neural networks that operate on graph-structured data. They … Read more
Click the above “Beginner’s Visual Learning”, select to add a star mark or “pin” Important content delivered promptly Datawhale Insights Author: Li Zuxian, Datawhale University Group Member, Shenzhen University With the development of deep learning, deep learning frameworks have begun to emerge in large numbers. Especially in the past two years, giants like Google, Facebook, … Read more