Click the “CVer” above to add it to your “Favorites” list. Essential insights delivered promptly. This article is reprinted from: Smarter Since the introduction of the Transformer, it has dominated the NLP field. However, its impact in the CV domain has been moderate, with initial thoughts suggesting it was unsuitable for CV until recently. A … Read more
Fingerprint unlocking, facial recognition, speech-to-text, robots diagnosing diseases, Alphago······ We have profoundly felt that artificial intelligence is changing our work methods and perceptions. According to the report on enterprise AI readiness by SAS, most companies believe that artificial intelligence is still in its early stages, “Currently, many application scenarios we are deploying contain AI components”. … Read more
ClickI Love Computer Vision to star and get CVML new technologies faster Introduction: The author of this article is a friend from 52CV, recommending not only learning paths but also practical projects and some classic tasks, which are worth referencing for friends who are exploring. Author: I Am Not Good at This Link: https://www.zhihu.com/question/337889115/answer/770797118 Source: … Read more
Li Haopeng, Wang Yu, Yao Shanglong Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 International Journal of Anesthesiology and Resuscitation, 2023, 45(03): 313-316. DOI: 10.3760/cma.j.cn321761-20230914-01001 REVIEW ARTICLES [Review] Computer Vision (CV) technology has been widely applied in various fields of medical diagnosis and treatment. In recent years, … Read more
▲Click the above Leifeng Network to follow To some extent, the greatest advantage of deep learning is its ability to automatically create features that no one would think of. Now, deep learning has a place in many fields, especially in computer vision. Although many people are fascinated by it, the deep network is essentially a … Read more
Big Data Digest Work Students studying the Stanford CS231n open course, take note! The detailed explanations for Assignment 1 – 3 are now available! Yesterday, Big Data Digest initiated a call for participants in the course by Andrew Ng and Li Fei-Fei, and the enthusiasm for the#SpringFestivalCheckIn# activity was exceptionally high! The Digest team has … Read more
Source: Pythonic Biologist This article is about 1900 words long, and it is recommended to read it in 8 minutes. TensorFlow and PyTorch are quite similar; this article introduces loss functions using PyTorch as an example. 19 Types of Loss Functions 1. L1 Loss L1Loss Calculates the absolute difference between output and target. torch.nn.L1Loss(reduction='mean') Parameters: … Read more
Click the “CVer” above to select “Star” or “Pin”. Heavyweight content delivered at the first time. Author: Bi Ji Ji https://zhuanlan.zhihu.com/p/59271905 This article is authorized, and no secondary reproduction is allowed without permission. 1. The Differences Between nn.Module.cuda() and Tensor.cuda() Both the cuda() function can achieve memory migration from CPU to GPU for models and … Read more
Author: Nicolas Affiliation: Researcher at Zhuiyi Technology AI Lab Research Direction: Information Extraction, Machine Reading Comprehension Do you want to experience double the training speed? Do you want to instantly double your GPU memory? If I tell you that it only takes three lines of code, would you believe it? In this article, the author … Read more
Machine Heart Report Contributors: Siyuan, Yiming Keras and PyTorch are both among the most beginner-friendly deep learning frameworks. They operate like a simple language for describing architectures, telling the framework what layer should use what. Many researchers and developers are pondering which framework is better, but currently, both frameworks are very popular, each with its … Read more