Click the above “Beginner’s Visual Learning” to choose to add “Starred” or “Pinned“. Heavyweight content delivered to you first Author: Song Gu Source: Zhihu Link: https://zhuanlan.zhihu.com/p/33194897 Editor: Wang Meng (Deep Learning Go Go Go public account) Copyright belongs to the author, this article is for academic sharing only. If there is any infringement, please contact … Read more
The topic of deep learning seems to have lost its previous popularity. Thanks to the myriad tutorials available online, anyone can talk about deep learning for five minutes. But has the threshold for deep learning dropped to the level of statistical methods like EOF decomposition? On one hand, deep learning is overly touted as a … Read more
Click the above “Beginner’s Visual Learning” and choose to add “Star” or “Top“. Heavyweight content delivered in real time Author | Peter Pan Xin Source | Xixiaoyao’s Cute Store I have been dealing with deep learning frameworks for many years. From Google’s TensorFlow to Baidu’s PaddlePaddle, and now Tencent’s Wuliang. I am fortunate to have … Read more
Author: Chen ZhiyanThis article is about 3500 words, and it is recommended to read in 5 minutes This article introduces several very useful deep learning frameworks, their advantages and applications. By comparing each framework, developers can learn how to selectively use them to efficiently and quickly complete project tasks. For beginners in data science, using … Read more
Source: Data STUDIO This article is approximately 3600 words, recommended reading time is 9 minutes. Why does the error message indicate enough memory, yet still encounter OOM? What is the multi-level allocation mechanism of memory? Why is it designed this way? Analyzing the PyTorch Memory Management Mechanism Mainly Aims to Reduce “Memory Fragmentation” Impact. A … Read more
Source:DeepHub Imba This article is approximately 2500 words long and is recommended to be read in 5 minutes. In this article, we will introduce how to implement a simpler HydraNet in Pytorch. In machine learning, we usually focus on a single task, which means optimizing a single metric. However, Multi-Task Learning (MTL) has achieved success … Read more
Reproduced from: Machine Heart Master these 17 methods to accelerate your PyTorch deep learning training in the most effortless way. Recently, a post on Reddit gained massive popularity. The topic is about how to speed up PyTorch training. The original author is LORENZ KUHN, a master’s student in computer science at ETH Zurich. The article … Read more
Click on the above “Beginner’s Guide to Vision”, choose to add “Star” or “Top” Essential insights delivered promptly IntroductionWhat does a high-performance PyTorch training pipeline look like? Is it about achieving the highest accuracy model? The fastest runtime? Easy to understand and extend? Or easily parallelized? The answer is, it includes all of the above. … Read more
Introduction Using PyTorch to implement the ViT model from scratch, training the ViT model on the CIFAR-10 dataset for image classification. Architecture of ViT The architecture of ViT is inspired by BERT, which is a transformer model that uses only encoders, typically used for supervised learning tasks in NLP such as text classification or named … Read more
Transformers shine in the field of computer vision, and the Detection Transformer (DETR) is a successful application of Transformers in object detection. By utilizing the attention mechanism in Transformers, it effectively models long-range dependencies in images, simplifying the object detection pipeline and constructing an end-to-end object detector. Object detection can be understood as a set … Read more