Follow
This article explains imgcook and various plug-ins to help with intelligent code generation.
The article shows how imgcook can help simplify the development process of apps for the visually impaired and improve the user experience.
This article introduces the open-source project iMove, including its features and implementation principles.
This article introduces the Alibaba Cloud Traffic Product Frontend Team's overall idea of D2C and the general solutions.
This article focuses on some practices of C2C solutions in Alibaba Cloud's Apsara Big Data Platform.
This article explains the similarities and differences between Low-Code and No-Code development and their benefits.
This blog discusses the usage of deep learning for recognizing UI components and introduces a correct method of using machine learning to solve various problems.
This blog discusses in depth about layout algorithms used by imgcook, focusing on page splitting, loop, and multi-status identification.
This post describes how imgcook recognizes design components and expresses them with front-end components for intelligent code generation.
This article will describe how imgcook optimizes data and model by building a complete closed-loop procedure for the icon model.
This article introduces the concept of semantics in D2C and elaborates further on various semantic decision models based on reinforcement learning and text classification.
This article is a part of the article series discussing how to generate frontend code from design files intelligently.
This article is a part of the article series discussing how to generate frontend code from design files intelligently.
This article is a part of the article series discussing how to generate frontend code from design files intelligently.
This article is a part of the article series discussing how to generate frontend code from design files intelligently.
This article is a part of the article series discussing how to generate frontend code from design files intelligently.
This article discusses how to generate frontend code from design files intelligently, covering image segmentation and content extraction from complex image backgrounds.
This article discusses business module recognition and explores the random forest and XGBoost models for intelligent frontend code generation.
This article discusses how to generate frontend code from design files intelligently, specifically on code generation for forms and tables.
This article uses the main ideas of the "READ: Recursive Autoencoders for Document Layout Generation" paper and discusses its implementation in frontend scenarios.
