The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 26, 2025

Filed:

Sep. 21, 2021
Applicant:

Beijing Baidu Netcom Science Technology Co., Ltd., Beijing, CN;

Inventors:

Qingqing Dang, Beijing, CN;

Kaipeng Deng, Beijing, CN;

Lielin Jiang, Beijing, CN;

Sheng Guo, Beijing, CN;

Xiaoguang Hu, Beijing, CN;

Chunyu Zhang, Beijing, CN;

Yanjun Ma, Beijing, CN;

Tian Wu, Beijing, CN;

Haifeng Wang, Beijing, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 18/21 (2023.01); G06F 18/23 (2023.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 18/217 (2023.01); G06F 18/23 (2023.01);
Abstract

Embodiments of the present disclosure provide a method and apparatus of training a model, an electronic device, a storage medium and a development system, which relate to a field of deep learning. The method may include calling a training preparation component to set at least a loss function and an optimization function for training the model, in response to determining that a training preparation instruction is received. The method further includes calling a training component to set a first data reading component, in response to determining that a training instruction is received. The first data reading component is configured to load a training data set for training the model. In addition, the method may further include training the model based on the training data set from the first data reading component, by using the loss function and the optimization function through the training component.


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