This tutorial walks you through the entire process of creating a browser‑based AI version of the classic "draw‑and‑guess" game, covering model selection, dataset preparation, CNN training with TensorFlow.js, model integration into a Vue front‑end, performance optimizations, and deployment steps.
This article details a complete engineering solution for bringing on‑device AI to WeChat mini‑programs, comparing TensorFlow.js and WeChat native inference, covering model conversion, package‑size optimization, integration steps, performance metrics, and a hybrid strategy that boosts recommendation click‑through rates by 30%.
This article introduces the core concepts of tokenizers and embeddings in large language models, explains how they convert text into numeric IDs and dense vectors, compares different tokenization strategies, and provides practical JavaScript and TensorFlow.js code examples for beginners.
This article explains the architecture of an image‑search system that extracts feature vectors, stores them in a vector database, and performs similarity queries, then proposes an optimized design that offloads feature extraction to a lightweight MobileNet model running in the browser, reducing latency, server load, and component complexity.
This guide demonstrates how to build a web‑based real‑time dog detector that accesses the phone camera via the browser, processes video frames with TensorFlow.js and a pre‑trained COCO‑SSD MobileNet V2 model, and plays an audio alert when a dog is recognized, all deployed on an Android device using Termux.
This article introduces TensorFlow.js, explains its origin and core features, and provides step‑by‑step instructions for using it in browsers, Node.js, and WeChat mini‑programs, including package installation, sample code, and practical tips for front‑end developers.
Adobe’s new web‑based Photoshop demonstrates how modern browser APIs—such as WebAssembly, OPFS, Service Workers, Lit‑based Web Components, and TensorFlow.js—enable a complex, graphics‑intensive application to run efficiently across platforms while offering AI‑powered features and future‑proof performance optimizations.
This article explains how to design, implement, and evaluate a mouse gesture recognition system using machine learning and geometric analysis, covering data preprocessing, model training with TensorFlow.js, cosine‑similarity matching, performance optimizations, and extensions to three‑dimensional VR/AR environments.
This article walks through the fundamentals of artificial intelligence, explaining linear and logistic regression, loss functions, gradient descent, and neural network basics, illustrated with TensorFlow.js code examples, visual analogies, and practical demos, helping readers grasp core concepts and their real‑world applications.
Alibaba Front‑End Intelligent Technology combines PipCook, DataCook, and imgcook to enable data‑driven UI generation, on‑device AI inference via WASM‑Rust‑SIMD and WebGPU, and applications such as code IntelliSense and design‑to‑code, while outlining a roadmap toward unified AI‑powered interfaces for commerce.
This article introduces WebGPU—a powerful yet still experimental web graphics API—showing how major frameworks like Three.js and Babylon.js adopt it for high‑performance 3D rendering, how TensorFlow.js leverages it for massive deep‑learning speedups, and provides hands‑on code examples from framework usage to raw WebGPU programming.
This article examines high‑performance web computing for TensorFlow.js models, comparing tfjs and ant‑tfjs on WebGL, Wasm, and GPU backends, and details a series of optimizations—including pre‑encoding, shader handling, graph fusion, vectorization, and memory layout—that double inference speed on mobile devices.
This article explains how AI is extending into front‑end development, defines edge AI, outlines its application scenarios, discusses advantages and limitations, reviews web‑based inference frameworks and hardware acceleration, and details model compression techniques for deploying AI directly in browsers.
Discover how Pipcook 2.0 dramatically speeds up machine‑learning workflows for web developers—cutting installation to under 20 seconds, enabling rapid model training, prediction, and deployment via concise JSON pipelines, with step‑by‑step guidance, code snippets, and practical examples for image and text classification.
This article explains a four‑level methodology for choosing JavaScript‑based machine‑learning tools, demonstrates practical code examples ranging from NLP with nlp.js to deep‑learning with TensorFlow.js, traditional ML with mljs, and statistical computing with stdlib, and shows how to run them entirely in the browser.
This article explains how Pipcook, a machine‑learning framework designed for front‑end developers, integrates TensorFlow.js, Boa, and Node.js to automate code generation, simplify model training, and enable intelligent front‑end applications while outlining its architecture, performance considerations, and future roadmap.
From the long‑awaited React 17 release and CRA 4.0 updates to Rust’s emerging role in WebAssembly, new CSS layout functions, Node 15 features, TensorFlow.js tutorials, serverless trends, and the rise of fast JavaScript linters, this roundup surveys the latest front‑end and related technologies.
This guide explains the fundamentals of tensors in TensorFlow.js—including scalars, vectors, and higher‑dimensional tensors—demonstrates how to convert real‑world data such as the Titanic dataset into tensors, and shows how to build, compile, and train a simple neural network model using appropriate layers, loss functions, and optimizers.
Learn how to create a browser‑compatible MNIST image classification model using TensorFlow.js, covering data preprocessing with sprite images, model construction, training, and evaluation, while providing complete JavaScript code examples and practical tips for handling ArrayBuffer, DataView, and visualization.
This tutorial walks you through using Pipcook’s text‑creation pipeline to train a TensorFlow.js LSTM model that generates Chinese poems from a given seed, covering plugin configuration, GPU training, model evaluation, and Node.js inference.
This article explains how to implement a web‑browser face‑recognition authentication system by capturing video streams with WebRTC, detecting faces with face‑api.js, converting images to Base64, and leveraging Baidu AI services for identity verification and liveness detection.
Jason Mayes, a Google web engineer, open‑sourced a TensorFlow.js demo that removes people from live webcam video in real time using a lightweight MobileNet model, with only about 200 lines of code, and provides GitHub and CodePen links for experimentation.
During pandemic remote work, this guide shows engineers how to use a webcam, TensorFlow.js pose detection, and simple web APIs to monitor posture, automate meal and water reminders, and generate audio alerts, turning everyday health risks into a programmable personal wellness system.
This article explains how Alibaba’s merchant mini‑program platform integrates Modiface’s AR makeup try‑on engine using JavaScript, MNN and TensorFlow.js, detailing the architecture, real‑time camera handling, inference acceleration, rendering capabilities, and future evolution of AR features for brands.
This article explores how artificial intelligence is influencing frontend development, detailing recent breakthroughs, practical frameworks like TensorFlow.js, real‑world applications, advantages, limitations, and emerging solutions for deploying machine learning models directly in browsers.
