How TorchDynamo Works: New Compilation Features in PyTorch

How TorchDynamo Works: New Compilation Features in PyTorch

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

Understanding Graph Neural Networks with PyTorch

Understanding Graph Neural Networks with PyTorch

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

Beginner’s Guide to Pytorch for Deep Learning

Beginner's Guide to Pytorch for Deep Learning

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

Building Neural Network Prediction Models with PyTorch

Click on the above “Mechanical and Electronic Engineering Technology” to follow us To build a neural network model based on PyTorch for predicting outputs, you can follow these steps: Define the Problem: First, clarify whether the problem is a regression problem or a classification problem. If the output is a continuous value, it may be … Read more

13 Image Augmentation Methods in Pytorch

13 Image Augmentation Methods in Pytorch

MLNLP(Machine Learning Algorithms and Natural Language Processing) community is a well-known natural language processing community both domestically and internationally, covering NLP graduate 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, especially for beginners. … Read more

10 Basic Tensor Operations in PyTorch

10 Basic Tensor Operations in PyTorch

Source: DeepHub IMBA This article is about 2000 words long and is recommended to be read in 5 minutes. This article will introduce some basic tensor operations in PyTorch. PyTorch is a scientific computing package based on Python. Its flexibility allows for easy integration of new data types and algorithms, and the framework is also … Read more

Understanding and Implementing Diffusion Models with PyTorch

Understanding and Implementing Diffusion Models with PyTorch

Source: Machine Learning Algorithms<br/><br/> This article is approximately 6500 words long and is suggested to take 13 minutes to read. This article provides a complete introduction to the necessary knowledge about diffusion models and implements it fully using PyTorch. The trigger for diffusion models began with the introduction of the Denoising Diffusion Probabilistic Model (DDPM) … Read more

Minimal Implementation of Elastic Training in Pytorch

Minimal Implementation of Elastic Training in Pytorch

Click the above “Getting Started with Vision” to add a Star or “Pin” Important content delivered immediately Scan the QR code below to join the cutting-edge academic paper exchange group!You can get the latest top conference/journal paper idea interpretations and the interpretation PDFs and materials from beginner to advanced in CV, as well as the … Read more

An Overview of Nineteen Loss Functions in Pytorch

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

Summary of Memory Saving Strategies in PyTorch

Summary of Memory Saving Strategies in PyTorch

Click the "Xiaobai Learns Vision" above, select "Star" or "Top" Heavyweight content delivered at the first time Source丨https://zhuanlan.zhihu.com/p/430123077 Introduction With the rapid development of deep learning, the explosive growth of model parameters has put higher demands on the memory capacity of GPUs. How to train models on GPUs with small memory capacity has always been … Read more