Author: Zhang Jianzhong Source: http://blog.csdn.net/zouxy09/article/details/8775518 Deep learning is a new field in the study of machine learning, motivated by the establishment and simulation of neural networks that analyze and learn like the human brain. It mimics the mechanisms of the human brain to interpret data such as images, sounds, and text. Deep learning is a … Read more
Editor: Amusi | Source: Zhihu, originally from: cver https://www.zhihu.com/question/25097993 This article is for academic sharing only. If there is infringement, the article will be deleted. What Are the Techniques for Tuning Parameters in Deep Learning? The effectiveness of deep learning largely depends on how well the parameters are tuned. So how can we quickly and … Read more
Deep learning and process understanding for data-driven Earth system science Markus Reichstein et al 2019 in Nature This is a review article that discusses the applications and challenges of machine learning in geosciences. The Earth system science has entered the era of big data. Earth system data is a typical big data, characterized by the … Read more
Selected from Medium Author: Arvind N Translated by Machine Heart Contributors: Lu Xue, Li Zenan On August 8, Andrew Ng officially launched Deeplearning.ai—a series of deep learning courses based on Coursera, aiming to spread foundational knowledge of artificial intelligence to more people. A week later, many have completed the first three courses that are currently … Read more
Click the above“Gu Mu Pai Li Mo Chou” Follow *Friendly reminder:If you want to part-time join various high-tech startup teams, please add Li Mo Chou’s WeChat number: 13575329911 Say JoinTeam. Li Mo Chou said Today, Mo Chou wants to talk to everyone about deep learning, which is a great thing. It is a major breakthrough … Read more
Introduction The concept of deep learning originates from the study of artificial neural networks. A multi-layer perceptron with multiple hidden layers is a type of deep learning structure. Deep learning combines low-level features to form more abstract high-level representations of attribute categories or features, in order to discover distributed feature representations of data. The concept … Read more
In October 2018, the BERT model launched by Google made a stunning impact, sweeping various rankings and even surpassing human baseline scores, achieving a milestone breakthrough in the field of NLP. Today, for NLP algorithm engineers, BERT has become an essential tool. “What if there’s too little data?” — “Just fine-tune BERT!” “What if RNN … Read more
Author: Dr. Zhang Junlin, Senior Algorithm Expert at Sina Weibo Zhihu Column:Notes on the Frontiers of Deep Learning This article has been authorized, you can click “Read the original” at the end of the article to go directly: https://zhuanlan.zhihu.com/p/65470719 In the past two months, I have been paying close attention to the current application status … Read more
BERT has been very popular recently, so let’s gather some related resources, including papers, code, and article interpretations. 1. Official Google resources: 1) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Everything started with this paper released by Google in October, which instantly ignited the entire AI community, including social media: https://arxiv.org/abs/1810.04805 2) GitHub: … Read more
©PaperWeekly Original · Author｜Xiong Zhiwei School｜Tsinghua University Research Direction｜Natural Language Processing Since BERT was proposed in 2018, it has gained significant success and attention. Based on this, various related models have been proposed in academia to improve BERT. This article attempts to summarize and organize these models. MT-DNN MT-DNN (Multi-Task DNN) was proposed by Microsoft … Read more