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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.
This article guides you through the implementation of your own frontend intelligent project and use of machine learning to address programming problems.
This article focuses on ways to obtain information and how imgcook has helped on the journey of generating more accurate code.
This article introduces Pipcook and discusses how it can be optimized and used for an intelligent frontend.
This article discusses how to implement Deep Learning, Sample Generation, Model Training, Model Compression, and Conversion with TensorFlow.
This article describes how Pipcook is integrated with TensorFlow and how the underlying models of tfjs-node are used to build a machine learning pipeline.
This article explores how AI empowers Design-to-Code (D2C) in mid-end and backend scenarios.
