Click on the above “Xiaobai Learns Vision“, select “Star” or “Pin“ Important content delivered immediately Authors: Matt H and Daniel R Compiled by: ronghuaiyang Introduction Experience and lessons accumulated from thousands of hours of model training. In our machine learning lab, we have accumulated thousands of hours of training on many high-performance machines. However, it … Read more
Excerpt from fast.ai Authors: Sylvain Gugger, Jeremy Howard Translated by: Machine Heart Contributors: Siyuan, Wang Shuting, Zhang Qian Optimization methods have always been a crucial part of machine learning and are the core algorithms of the learning process. Since its introduction in 2014, Adam has garnered widespread attention, with over 10,047 citations for the original … Read more
Follow our public account “ML_NLP“ Set as “Starred“, heavy content delivered first! Author:Qiu Zhenyu (Algorithm Engineer, Huatai Securities Co., Ltd.) Zhihu Column:My AI Journey Recently, I came across a paper titled “Revisiting Few-sample BERT Fine-tuning”. The paper has just been released on arXiv, and although it hasn’t attracted much attention yet, I found it very … Read more
Click the blue text| Follow “Electrical Engineering” Abstract:Traditional neural networks have low accuracy in load forecasting with strong temporal correlation. To effectively improve the accuracy of short-term power load forecasting, a load forecasting method based on the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network is proposed. Five-dimensional load feature data … Read more
Datawhale Insights Compiled by: Wang Xiaoxin, Source: Quantum Bit When adjusting the way models update weight and bias parameters, have you considered which optimization algorithm can yield better and faster results for the model? Should you use Gradient Descent, Stochastic Gradient Descent, or the Adam method? This article introduces the main differences between various optimization … Read more
Datawhale Insights Compiled by: Wang Xiaoxin, Source: Quantum Bits When adjusting the model’s weight and bias update methods, have you considered which optimization algorithm can yield better and faster results for the model? Should you use gradient descent, stochastic gradient descent, or the Adam method? This article introduces the main differences between various optimization algorithms … Read more
Recommended for You Understanding Deep Learning Optimizers: Momentum, RMSProp, and Adam I don’t believe you won’t understand Information Entropy after reading this An Overview of Deep Learning Optimization Methods – Gradient Descent Object Detection vs Object Tracking
Produced by Big Data Digest Compiled by: Shijin Tian, Ni Ni, Hu Jia, Yun Zhou In many machine learning labs, machines have undergone thousands of hours of training. During this process, researchers often take many detours and fix many bugs, but it is certain that the experience and knowledge gained during the research process are … Read more