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58UXD
Nov 20, 2024 · Artificial Intelligence

How AI Is Transforming UI Design: Benefits, Challenges, and Real‑World Tools

This article examines how artificial intelligence reshapes UI design by boosting efficiency, enabling personalized experiences, and supporting data‑driven decisions, while also confronting limits such as understanding complex business logic, lacking creative nuance, and adapting to industry‑specific standards, illustrated through the Uizard tool.

AIMachine LearningUizard
0 likes · 6 min read
How AI Is Transforming UI Design: Benefits, Challenges, and Real‑World Tools
Alimama Tech
Alimama Tech
Nov 13, 2024 · Artificial Intelligence

DeepString: Alibaba's Anti‑Fraud Platform Using Large Models for Real‑Time Traffic Detection

Alibaba's anti-fraud platform DeepString uses large unsupervised models to detect abnormal traffic in real time across multiple advertising products, combining a foundation model for event mining, anomaly measurement, and an alignment model for online filtering, reducing reliance on manual labeling and domain expertise.

Large ModelsMachine Learningalgorithm framework
0 likes · 19 min read
DeepString: Alibaba's Anti‑Fraud Platform Using Large Models for Real‑Time Traffic Detection
Tencent Advertising Technology
Tencent Advertising Technology
Nov 8, 2024 · Artificial Intelligence

Optimizing Real-Time Bidding: Machine Learning Approaches for Bid Shading and Winning Price Prediction

This article explores advanced machine learning techniques for optimizing bid shading in real-time advertising auctions, introducing a mixed censorship multi-task learning framework and a cost-effective active learning strategy to accurately predict winning price distributions and overcome sample selection bias.

Auction MechanismsBid ShadingMachine Learning
0 likes · 16 min read
Optimizing Real-Time Bidding: Machine Learning Approaches for Bid Shading and Winning Price Prediction
JD Cloud Developers
JD Cloud Developers
Nov 6, 2024 · Artificial Intelligence

How Data Science Powers JD’s Logistics, Finance, and Healthcare Innovations

This article explains the fundamentals of data science, its key components, and showcases how JD applies it across e‑commerce, finance, healthcare, and logistics, while also reviewing past innovations, common project pitfalls, and future directions such as quantum computing and supply‑chain digital twins.

Artificial IntelligenceHealthcareMachine Learning
0 likes · 21 min read
How Data Science Powers JD’s Logistics, Finance, and Healthcare Innovations
Architects' Tech Alliance
Architects' Tech Alliance
Nov 1, 2024 · Artificial Intelligence

Master Machine Learning: Core Concepts, Algorithms, and Evaluation Explained

This comprehensive guide walks through the fundamentals of artificial intelligence, machine learning and deep learning, explains the three essential elements of ML, outlines its historical milestones, details core techniques, workflow, key terminology, algorithm families, model evaluation metrics, bias‑variance trade‑offs, validation strategies, and practical model‑selection guidelines.

AlgorithmsArtificial IntelligenceMachine Learning
0 likes · 19 min read
Master Machine Learning: Core Concepts, Algorithms, and Evaluation Explained
php Courses
php Courses
Oct 23, 2024 · Artificial Intelligence

Data Dimensionality Reduction and Feature Extraction with PHP

This article explains the concepts of data dimensionality reduction and feature extraction in machine learning and demonstrates how to implement them in PHP using the PHP‑ML library, including installation, data preprocessing, PCA-based reduction, and feature extraction with token vectorization and TF‑IDF.

Machine LearningPCAPHP-ML
0 likes · 5 min read
Data Dimensionality Reduction and Feature Extraction with PHP
AntTech
AntTech
Oct 16, 2024 · Artificial Intelligence

Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering (SEPTA Framework)

The paper introduces the SEPTA framework, which converts knowledge graphs into a subgraph vector database and employs graph‑text alignment via bidirectional contrastive learning to improve subgraph retrieval and knowledge fusion for commonsense question answering, demonstrating strong performance across five benchmark datasets.

Knowledge GraphsMachine LearningSEPTA
0 likes · 4 min read
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering (SEPTA Framework)
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 15, 2024 · Artificial Intelligence

How DPO Simplifies RLHF: A Deep Dive into Direct Preference Optimization

This article breaks down how Direct Preference Optimization (DPO) mathematically reduces the two‑stage RLHF pipeline into a single‑stage SFT process, explains the underlying loss transformations, and discusses DPO's practical limitations and trade‑offs for large language model alignment.

DPODirect Preference OptimizationMachine Learning
0 likes · 9 min read
How DPO Simplifies RLHF: A Deep Dive into Direct Preference Optimization
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Oct 11, 2024 · Artificial Intelligence

Harmonized Speculative Sampling (HASS): Aligning Training and Decoding for Efficient Large Language Model Inference

HASS aligns training and decoding contexts and objectives for speculative sampling, using harmonized objective distillation and multi-step context alignment, achieving 2.81–4.05× speedup and 8%–20% improvement over EAGLE‑2 while preserving generation quality in real-world deployments at Xiaohongshu.

AIHASSInference Acceleration
0 likes · 11 min read
Harmonized Speculative Sampling (HASS): Aligning Training and Decoding for Efficient Large Language Model Inference
Architecture Development Notes
Architecture Development Notes
Oct 11, 2024 · Artificial Intelligence

Can Rust Replace Python for Data Science? Exploring Performance and Safety

While Python dominates data analysis and machine learning with its ease of use, Rust offers memory safety and near‑C performance; this article examines their respective strengths, the challenges of rewriting the Python interpreter in Rust, and how combining both can boost library speed and reliability.

Machine LearningMemory SafetyPython
0 likes · 6 min read
Can Rust Replace Python for Data Science? Exploring Performance and Safety
DevOps
DevOps
Oct 8, 2024 · Artificial Intelligence

Top 20+ Retrieval‑Augmented Generation (RAG) Interview Questions and Answers

This article presents over twenty essential Retrieval‑Augmented Generation (RAG) interview questions with detailed answers, covering fundamentals, applications, architecture, training, limitations, ethical considerations, and integration, offering AI enthusiasts and job candidates a comprehensive guide to mastering RAG concepts.

AI InterviewMachine LearningNLP
0 likes · 15 min read
Top 20+ Retrieval‑Augmented Generation (RAG) Interview Questions and Answers
Python Programming Learning Circle
Python Programming Learning Circle
Oct 7, 2024 · Fundamentals

50 Classic Python Libraries You Can Master Quickly

This article presents a curated list of fifty essential Python libraries spanning data analysis, scientific computing, visualization, machine learning, web development, database access, testing, and utilities, providing brief descriptions to help developers quickly identify and master the most useful tools in the Python ecosystem.

Machine Learningdata-sciencelibraries
0 likes · 7 min read
50 Classic Python Libraries You Can Master Quickly
Zhuanzhuan Tech
Zhuanzhuan Tech
Sep 26, 2024 · Artificial Intelligence

Pricing Strategy and Model Evolution for Second‑Hand Phone Auctions in ZhaiZhai TOB Marketplace

This article examines the characteristics of ZhaiZhai's B2B auction scenario, defines core pricing metrics, presents a step‑by‑step methodology for determining optimal starting prices, reviews early practices and their shortcomings, and details the current modular machine‑learning model architecture that improves transaction rates and reduces price premiums for second‑hand smartphones.

Machine LearningOperationsPrice Optimization
0 likes · 29 min read
Pricing Strategy and Model Evolution for Second‑Hand Phone Auctions in ZhaiZhai TOB Marketplace
Ctrip Technology
Ctrip Technology
Sep 23, 2024 · Frontend Development

Intelligent Alert Attribution System for Ctrip Hotel Frontend: Design, Implementation, and Outcomes

This article details the design and deployment of an intelligent alert attribution system for Ctrip Hotel's front‑end, describing the background challenges, the unified data pool, weighted alert rules, three attribution algorithms, achieved improvements in accuracy and troubleshooting speed, and future enhancement plans.

AlertFrontendMachine Learning
0 likes · 18 min read
Intelligent Alert Attribution System for Ctrip Hotel Frontend: Design, Implementation, and Outcomes
Data Thinking Notes
Data Thinking Notes
Sep 19, 2024 · Artificial Intelligence

Why AI Has Only a Seven-Year History—and What AI+ Means for the Future

In this speech, Wang Jian reflects on the evolution of artificial intelligence, arguing that modern AI is fundamentally different from its early concepts, emphasizing the pivotal roles of data, models, and infrastructure, and exploring the transformative impact of AI+, transformers, and cloud platforms on future innovation.

AI infrastructureAI+Artificial Intelligence
0 likes · 18 min read
Why AI Has Only a Seven-Year History—and What AI+ Means for the Future
DataFunSummit
DataFunSummit
Sep 18, 2024 · Artificial Intelligence

Multi‑Scenario Modeling for NetEase Cloud Music Recommendation: Architecture, Challenges, and Results

This article presents NetEase Cloud Music's multi‑scenario recommendation modeling work, covering background, overall system architecture, key modules such as unified and private domain networks, modeling objectives and difficulties, experimental results, future outlook, and a detailed Q&A session.

AIMachine LearningNetEase Cloud Music
0 likes · 13 min read
Multi‑Scenario Modeling for NetEase Cloud Music Recommendation: Architecture, Challenges, and Results
21CTO
21CTO
Sep 13, 2024 · Artificial Intelligence

Boost Your Development Workflow: 7 AI Tools Every Developer Should Try

Discover seven AI-powered tools—including GitHub Copilot, Tabnine, ChatGPT, Figma plugins, DALL·E, AI testing suites, and Code Snippets AI—that can streamline coding, design, and testing, helping developers work faster, reduce repetitive tasks, and focus on creative problem‑solving.

AI toolsMachine LearningTesting Automation
0 likes · 8 min read
Boost Your Development Workflow: 7 AI Tools Every Developer Should Try
DataFunSummit
DataFunSummit
Sep 12, 2024 · Cloud Native

Design and Implementation of a Next‑Generation Multi‑Protocol Unstructured Storage System for Machine Learning

This article presents the challenges of storing massive machine‑learning datasets, evaluates existing storage solutions, and details the design of OrangeFS—a cloud‑native, multi‑protocol, multi‑tenant unstructured storage system that integrates object and file interfaces, optimizes metadata services, supports hot upgrades, and provides robust scalability and reliability for AI workloads.

Cloud NativeMachine LearningMulti-Protocol
0 likes · 24 min read
Design and Implementation of a Next‑Generation Multi‑Protocol Unstructured Storage System for Machine Learning
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 12, 2024 · Artificial Intelligence

Intelligent Compute Allocation in Advertising: Value Quantification, Elastic Elimination, and Dynamic Optimization

iQIYI’s ad engine team introduced an intelligent compute allocation system that quantifies traffic value and unified compute cost, uses elastic elimination and a dynamic allocation framework to maximize revenue under fixed compute limits, delivering over 30% inventory growth, modest consumption rise, and near‑perfect availability.

Machine LearningOptimizationPID control
0 likes · 11 min read
Intelligent Compute Allocation in Advertising: Value Quantification, Elastic Elimination, and Dynamic Optimization
Alimama Tech
Alimama Tech
Sep 11, 2024 · Artificial Intelligence

A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective

The paper introduces a coupled generative adversarial framework that merges biased observational with unbiased experimental data to create a bias‑free dataset for causal inference, enabling robust treatment‑effect estimation under collider bias from an out‑of‑distribution perspective, and demonstrates superior bias reduction on three public advertising datasets.

Generative Adversarial NetworksMachine Learningcausal inference
0 likes · 10 min read
A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective
DataFunSummit
DataFunSummit
Sep 11, 2024 · Artificial Intelligence

Weak Supervision Machine Learning in Ant Group Business Scenarios

This article presents an overview of weak supervision machine learning techniques applied to Ant Group’s business scenarios, covering an introduction to weak supervision, challenges of modeling with scarce or noisy labels, detailed methodologies for cross‑domain causal effect estimation, multi‑source noisy label denoising, and real‑world application examples.

Machine LearningWeak Supervisioncausal inference
0 likes · 18 min read
Weak Supervision Machine Learning in Ant Group Business Scenarios
Model Perspective
Model Perspective
Sep 10, 2024 · Artificial Intelligence

Why Cross-Entropy Is the Key Loss Function for Classification Models

This article explains how loss functions evaluate model performance, contrasts regression’s mean squared error with classification’s cross‑entropy, describes one‑hot encoding and softmax outputs, and shows why higher predicted probabilities for the correct class yield lower loss, highlighting applications in image, language, and speech tasks.

Machine LearningSoftmaxclassification
0 likes · 5 min read
Why Cross-Entropy Is the Key Loss Function for Classification Models
Python Programming Learning Circle
Python Programming Learning Circle
Sep 4, 2024 · Artificial Intelligence

Building an Automatic Math Grading System with Python: Data Generation, CNN Training, Image Segmentation, and Result Feedback

This tutorial explains how to create an automatic math‑grading tool in Python by generating synthetic digit images, training a small CNN on the data, segmenting handwritten equations with projection techniques, recognizing characters, evaluating the expressions, and overlaying the results back onto the original image.

AutomationCNNMachine Learning
0 likes · 30 min read
Building an Automatic Math Grading System with Python: Data Generation, CNN Training, Image Segmentation, and Result Feedback
php Courses
php Courses
Sep 4, 2024 · Artificial Intelligence

Anomaly Detection and Outlier Handling Using PHP and Machine Learning

This article explains how to detect and handle outliers in data using PHP, covering statistical Z-Score detection and the Isolation Forest algorithm, and provides sample code for both detection and subsequent removal or replacement of anomalous values to improve data quality.

Anomaly DetectionIsolation ForestMachine Learning
0 likes · 6 min read
Anomaly Detection and Outlier Handling Using PHP and Machine Learning
DataFunSummit
DataFunSummit
Sep 3, 2024 · Artificial Intelligence

Metric Attribution on Internet Platforms: Concepts, Methods, and Tool Applications

This article explains metric attribution for internet platforms, covering its definition, a three‑step analytical framework, deterministic and probabilistic methods such as metric decomposition, machine‑learning models with SHAP values, case studies, and a practical tool that guides users through attribution analysis.

Internet PlatformsMachine LearningSHAP
0 likes · 15 min read
Metric Attribution on Internet Platforms: Concepts, Methods, and Tool Applications
DataFunTalk
DataFunTalk
Sep 2, 2024 · Artificial Intelligence

Exploring Graph Foundation Models: Concepts, Techniques, and Future Directions

This article introduces graph foundation models, explains their relationship with large language models, reviews recent advances in graph neural networks and representation learning, presents the authors' own research on PT‑HGNN, Specformer and GraphTranslator, and discusses challenges, future research directions, and a Q&A session.

Large Language ModelsMachine Learningfoundation models
0 likes · 23 min read
Exploring Graph Foundation Models: Concepts, Techniques, and Future Directions
JD Retail Technology
JD Retail Technology
Aug 28, 2024 · Industry Insights

How JD Retail Secures E‑Commerce with AI‑Driven Content Compliance

This article examines JD Retail's content compliance platform, detailing user‑facing problems, business‑level audit responsibilities, key performance metrics, operational workflows, and a technical case study on detecting price over‑pricing using comparable‑price models and large‑scale price prediction.

ComplianceIndustry InsightsMachine Learning
0 likes · 10 min read
How JD Retail Secures E‑Commerce with AI‑Driven Content Compliance
Model Perspective
Model Perspective
Aug 27, 2024 · Fundamentals

How Mathematics Solves Murder Mysteries: From Galois to Network Theory

This article explores how mathematical concepts—from Galois theory and radian angles to distance‑decay functions and network theory—have been creatively applied to criminal investigations, illustrating real‑world cases of murder, serial killings, and terrorism, and highlighting the growing role of machine‑learning models in crime prediction.

Machine Learningcrime predictioncriminology
0 likes · 8 min read
How Mathematics Solves Murder Mysteries: From Galois to Network Theory
JD Retail Technology
JD Retail Technology
Aug 26, 2024 · Artificial Intelligence

Preference-oriented Diversity Model Based on Mutual Information for E-commerce Search Re-ranking (SIGIR 2024)

This article introduces PODM‑MI, a preference‑oriented diversity model that uses mutual information and variational Gaussian representations to jointly optimize accuracy and diversity in e‑commerce search re‑ranking, and reports significant online A/B test improvements on JD.com.

DiversityMachine LearningPreference Modeling
0 likes · 10 min read
Preference-oriented Diversity Model Based on Mutual Information for E-commerce Search Re-ranking (SIGIR 2024)
DataFunTalk
DataFunTalk
Aug 25, 2024 · Artificial Intelligence

Learning at Serving Time (LAST): An Online Learning Approach for Real‑Time Re‑ranking in Recommendation Systems

This article introduces LAST, a novel online learning method that updates ranking models instantly at serving time without waiting for user feedback, addressing the latency and stability challenges of real‑time re‑ranking in industrial recommendation pipelines and demonstrating its superiority through offline and online experiments.

Machine LearningOnline Learningreal-time
0 likes · 11 min read
Learning at Serving Time (LAST): An Online Learning Approach for Real‑Time Re‑ranking in Recommendation Systems
Model Perspective
Model Perspective
Aug 24, 2024 · Fundamentals

Why Vectors Are the Secret Sauce Behind Modern AI and Everyday Tech

Vectors, mathematical objects capturing magnitude and direction, serve as a versatile tool for representing multidimensional data, enabling everything from economic indicators and navigation cues to deep-learning feature extraction, similarity measures, and applications like music recognition, smart chatbots, and image search.

Machine Learningdata representationsimilarity
0 likes · 9 min read
Why Vectors Are the Secret Sauce Behind Modern AI and Everyday Tech
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Aug 23, 2024 · Artificial Intelligence

Xiaohongshu REDtech Live: Presentation of Recent Top‑Conference Papers (Recruitment Session)

On August 24, 2024, Xiaohongshu’s technical team will livestream a four‑hour REDtech session across WeChat Channels, its recruitment account, and Bilibili, showcasing recent top‑conference papers—from ACL and CVPR to ICLR and AAAI—covering innovations such as KV‑cache compression, zero‑shot image generation, early‑stopping self‑consistency, negative‑sample‑aware distillation, and real‑time nearest‑neighbor search, while allowing live interaction and offering surprise merchandise.

AIConference PapersMachine Learning
0 likes · 18 min read
Xiaohongshu REDtech Live: Presentation of Recent Top‑Conference Papers (Recruitment Session)
Open Source Tech Hub
Open Source Tech Hub
Aug 22, 2024 · Artificial Intelligence

Unlock AI Power in PHP: A Hands‑On Guide to TransformersPHP

TransformersPHP brings Hugging Face’s Transformer models to PHP, enabling developers to run thousands of pre‑trained NLP models locally for tasks like text generation, summarisation, and translation, with simple installation, ONNX‑based execution, and a Python‑like pipeline API.

AIMachine LearningNLP
0 likes · 8 min read
Unlock AI Power in PHP: A Hands‑On Guide to TransformersPHP
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 22, 2024 · Artificial Intelligence

How RECom Accelerates Recommendation Model Inference on GPUs

The RECom compiler introduces a subgraph‑parallel fusion technique and symbolic shape handling to dramatically speed up GPU inference of deep recommendation models with massive embedding columns, achieving up to 6.61× lower latency and 1.91× higher throughput than TensorFlow baselines, while eliminating redundant computations.

CompilerGPU optimizationMachine Learning
0 likes · 10 min read
How RECom Accelerates Recommendation Model Inference on GPUs
DataFunSummit
DataFunSummit
Aug 21, 2024 · Artificial Intelligence

Causal Debiasing in Ant Group Marketing Recommendation: Data Fusion and Backdoor Adjustment

This article introduces causal debiasing techniques for Ant Group's marketing recommendation systems, detailing background biases, causal graph analysis, a meta‑learning data‑fusion model (MDI), backdoor‑adjustment methods, extensive experiments on public and internal datasets, and real‑world deployment results.

Ant GroupData FusionMachine Learning
0 likes · 16 min read
Causal Debiasing in Ant Group Marketing Recommendation: Data Fusion and Backdoor Adjustment
Baidu Geek Talk
Baidu Geek Talk
Aug 21, 2024 · Artificial Intelligence

Step-by-Step PCA Face Recognition with PaddlePaddle

This article walks through using PaddlePaddle's linear algebra API to vectorize face images, load the ORL dataset, implement PCA for dimensionality reduction, and evaluate a simple face‑recognition classifier, providing full code, installation steps, and experimental results.

Machine LearningPCAPaddlePaddle
0 likes · 11 min read
Step-by-Step PCA Face Recognition with PaddlePaddle
DaTaobao Tech
DaTaobao Tech
Aug 19, 2024 · Frontend Development

Challenges and Solutions in AI-Powered Front-End Code Generation for B2C Platforms

The article details how Taobao’s AI team automated repetitive UI tasks for B2C front‑end development, achieving a 15 % efficiency gain across five projects, and outlines key challenges—prompt cost, low OCR accuracy, hallucinations, excess nodes, and customization variance—along with practical solutions such as a dedicated evaluation platform, OCR translation, model upgrades, prompt segmentation, output simplification, and a reusable component library.

AIFrontendMachine Learning
0 likes · 9 min read
Challenges and Solutions in AI-Powered Front-End Code Generation for B2C Platforms
DataFunSummit
DataFunSummit
Aug 18, 2024 · Artificial Intelligence

Challenges and Solutions in Recommendation AB Testing on Xiaohongshu's Experiment Platform

The article examines the key challenges of recommendation AB testing at Xiaohongshu—including change stability, single‑experiment precision, and multi‑strategy packaging—and presents a series of engineering and statistical solutions such as SDK‑based AB architecture, virtual PreAA experiments, CUPED/DID adjustments, and reverse experiments to improve reliability and metric impact.

AB testingCUPEDExperiment Platform
0 likes · 15 min read
Challenges and Solutions in Recommendation AB Testing on Xiaohongshu's Experiment Platform
21CTO
21CTO
Aug 17, 2024 · Artificial Intelligence

Understanding Large Language Models: Training, Uses, and a Llama 3 Code Demo

This article explains what large language models (LLMs) are, how they are trained, their diverse applications across industries, the challenges they face, and provides a practical Python example using Replicate to run Meta's Llama 3‑70b‑instruct model.

AILLMLarge Language Model
0 likes · 11 min read
Understanding Large Language Models: Training, Uses, and a Llama 3 Code Demo
Baidu Geek Talk
Baidu Geek Talk
Aug 14, 2024 · Artificial Intelligence

Sparse Tensor Basics in PaddlePaddle

The article explains how to use PaddlePaddle’s sparse computing features—including basic sparse tensor formats, creation and manipulation of sparse tensors, and building and training sparse neural networks such as a sparse ResNet—to improve memory efficiency and accelerate training on large, zero‑rich datasets.

AICOO FormatCSR Format
0 likes · 22 min read
Sparse Tensor Basics in PaddlePaddle
Python Programming Learning Circle
Python Programming Learning Circle
Aug 12, 2024 · Artificial Intelligence

Common Python Libraries for Computer Vision Projects

This article introduces and compares ten widely used Python libraries for computer vision, including Pillow, OpenCV, Mahotas, Scikit‑Image, TensorFlow Image, PyTorch Vision, SimpleCV, Imageio, Albumentations, and timm, highlighting their features, typical use cases, and providing code examples for each.

Machine LearningOpenCVPython
0 likes · 10 min read
Common Python Libraries for Computer Vision Projects
DataFunTalk
DataFunTalk
Aug 9, 2024 · Artificial Intelligence

Modeling User Propagation Ability for Social Recommendation and Influence Maximization in Games

This article presents a comprehensive study on leveraging user propagation ability metrics for friend recommendation and influence maximization in gaming environments, introducing a conversion‑funnel‑aware diffusion model, novel influence‑maximization variants, efficient greedy algorithms, and extensive offline and online experiments that demonstrate significant performance gains over traditional methods.

GamingMachine Learninggraph algorithms
0 likes · 16 min read
Modeling User Propagation Ability for Social Recommendation and Influence Maximization in Games
Meituan Technology Team
Meituan Technology Team
Aug 8, 2024 · Artificial Intelligence

BlackPearl Team Wins All Three Tracks of KDD 2024 OAG‑Challenge Cup with Large‑Model Solutions

The BlackPearl team from Meituan’s Dazhong Dianping division swept all three KDD 2024 OAG‑Challenge Cup tracks—WhoIsWho, PST, and AQA—by deploying innovative large‑model techniques such as iterative text clustering, graft‑learning‑enhanced BERT RAG pipelines, and a Boosting LLM‑for‑Vector search, and have released the code publicly on GitHub.

Academic DisambiguationKDD CupLarge Language Model
0 likes · 4 min read
BlackPearl Team Wins All Three Tracks of KDD 2024 OAG‑Challenge Cup with Large‑Model Solutions
Baidu Geek Talk
Baidu Geek Talk
Aug 7, 2024 · Artificial Intelligence

Detecting Time‑Series Anomalies in Embedding Space: A Practical AI Approach

This article presents an embedding‑based method for time‑series anomaly detection in security and anti‑cheat scenarios, explains how to vectorise logs, sample and compute distribution features, details implementation code, and validates the approach with four synthetic experiments showing precision‑recall improvements at day and hour granularity.

Anomaly DetectionEmbeddingMachine Learning
0 likes · 12 min read
Detecting Time‑Series Anomalies in Embedding Space: A Practical AI Approach
DataFunTalk
DataFunTalk
Aug 7, 2024 · Artificial Intelligence

Multi-Scenario Modeling for NetEase Cloud Music Recommendation: Architecture, Challenges, and Results

This article presents NetEase Cloud Music's multi‑scenario recommendation modeling work, detailing background, overall system architecture, key modules, modeling goals, technical difficulties, performance improvements, future outlook, and a comprehensive Q&A session that addresses practical deployment challenges.

AB testingAIMachine Learning
0 likes · 14 min read
Multi-Scenario Modeling for NetEase Cloud Music Recommendation: Architecture, Challenges, and Results
Open Source Linux
Open Source Linux
Aug 6, 2024 · Artificial Intelligence

What Is AI? A Beginner’s Guide to Definitions, Types, and Real‑World Impact

This article explains what artificial intelligence (AI) is, how it differs from traditional programming, outlines its main categories, introduces machine learning, deep learning, neural network models such as CNN, RNN, and Transformer, describes large models and GPT, and discusses AI’s wide‑range applications and societal implications.

AIAI applicationsArtificial Intelligence
0 likes · 16 min read
What Is AI? A Beginner’s Guide to Definitions, Types, and Real‑World Impact
Python Programming Learning Circle
Python Programming Learning Circle
Jul 27, 2024 · Artificial Intelligence

Numpy‑ML: A Pure NumPy Implementation of Machine Learning Algorithms

The Numpy‑ML project, created by UC Berkeley’s David Bourgin, provides a comprehensive pure‑NumPy implementation of over 30 machine‑learning algorithms—including probabilistic models, neural‑network layers, optimizers, and reinforcement‑learning agents—along with extensive data‑preprocessing utilities, all in a single open‑source repository.

AIAlgorithmsMachine Learning
0 likes · 6 min read
Numpy‑ML: A Pure NumPy Implementation of Machine Learning Algorithms
iQIYI Technical Product Team
iQIYI Technical Product Team
Jul 26, 2024 · Artificial Intelligence

Optimizing Advertising Feature Evaluation Process with the Opal Machine Learning Platform

By migrating iQIYI’s advertising feature‑evaluation workflow to the Opal machine‑learning platform, the team replaced a manual, engineer‑heavy process with a unified, automated pipeline that cut evaluation cycles from five days to 1.5 days, tripling iteration speed while lowering barriers and improving consistency for future feature optimization.

Feature EvaluationMachine LearningOpal Platform
0 likes · 6 min read
Optimizing Advertising Feature Evaluation Process with the Opal Machine Learning Platform
Meituan Technology Team
Meituan Technology Team
Jul 25, 2024 · Artificial Intelligence

Selected Meituan Papers Accepted at KDD 2024: Summaries of Five Long Papers

Meituan’s five long papers accepted at KDD 2024 introduce a dual‑intent model for search‑recommendation, a joint auction mechanism for ads, a robust ATE estimator for heavy‑tailed metrics, a decision‑focused causal learning framework for marketing, and an efficient on‑demand order‑pooling system for real‑time courier assignments.

Controlled ExperimentsKDD 2024Machine Learning
0 likes · 12 min read
Selected Meituan Papers Accepted at KDD 2024: Summaries of Five Long Papers
DataFunSummit
DataFunSummit
Jul 25, 2024 · Artificial Intelligence

LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control

This article presents the latest advances from the Chinese Academy of Sciences in graph machine learning for user behavior risk control, introducing the LOGIN framework that leverages large language models as consultants to iteratively enhance GNN training, and demonstrates its effectiveness through extensive experiments on homogeneous and heterogeneous graph benchmarks.

Large Language ModelsMachine Learninggraph neural networks
0 likes · 14 min read
LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control
Baidu Tech Salon
Baidu Tech Salon
Jul 23, 2024 · Artificial Intelligence

Linear Algebra Fundamentals and PaddlePaddle Applications

The article reviews core linear algebra concepts—vectors, matrices, eigenvalues, and transformations—and demonstrates how PaddlePaddle’s paddle.linalg API enables practical tasks such as least‑squares regression, image compression via SVD, PCA‑based dimensionality reduction, and broader machine‑learning, graphics, cryptography, and optimization applications.

Machine LearningPCAPaddlePaddle
0 likes · 10 min read
Linear Algebra Fundamentals and PaddlePaddle Applications
21CTO
21CTO
Jul 23, 2024 · Artificial Intelligence

What Is Agentic AI? How Autonomous Agents Boost Productivity and Transform Industries

Agentic AI, also known as autonomous AI agents, enables systems to perceive environments, make decisions, act, and continuously learn, offering higher productivity, smarter decision‑making, and industry‑wide transformation across sectors such as customer service, healthcare, finance, and manufacturing.

AI automationAI frameworksMachine Learning
0 likes · 13 min read
What Is Agentic AI? How Autonomous Agents Boost Productivity and Transform Industries
Architect
Architect
Jul 19, 2024 · Artificial Intelligence

Can Machine Learning Beat the Odds? A Deep Dive into Football Match Prediction

This article presents a data‑driven football match prediction system that extracts match features, builds machine‑learning models—including linear, SVM, random forest, and deep neural networks—and evaluates their accuracy on European league data, then analyzes betting strategies, limitations, and extensions to stock forecasting.

Artificial IntelligenceMachine Learningdata mining
0 likes · 24 min read
Can Machine Learning Beat the Odds? A Deep Dive into Football Match Prediction
DataFunSummit
DataFunSummit
Jul 19, 2024 · Artificial Intelligence

Risk Control in the Bulk Commodity Industry: Data‑Driven Solutions and Credit‑Risk Modeling by Ant Group

This article presents Ant Group's data‑driven approach to digital transformation and risk control in the bulk commodity sector, covering background challenges, data‑application pain points, core capabilities, credit‑risk models, data‑asset construction, indicator frameworks, and secure data integration for B2B scenarios.

Machine Learningcommodity industrycredit risk
0 likes · 14 min read
Risk Control in the Bulk Commodity Industry: Data‑Driven Solutions and Credit‑Risk Modeling by Ant Group
Sohu Tech Products
Sohu Tech Products
Jul 17, 2024 · Artificial Intelligence

How Weak Supervision Powers Ant Group’s Real‑World AI Challenges

This article presents a comprehensive technical overview of weak‑supervision machine learning at Ant Group, covering its fundamentals, cross‑domain causal effect estimation, strategies for scarce or noisy labels, novel framework components, experimental validation, and practical application scenarios.

AIMachine LearningWeak Supervision
0 likes · 18 min read
How Weak Supervision Powers Ant Group’s Real‑World AI Challenges
Continuous Delivery 2.0
Continuous Delivery 2.0
Jul 15, 2024 · Artificial Intelligence

Safely Repairing Broken Builds with Machine Learning

Google's research demonstrates that a machine‑learning model trained on build logs and code snapshots can automatically suggest safe, high‑quality fixes for broken builds, boosting developer productivity by about two percent without introducing detectable security risks.

Build AutomationML-assisted debuggingMachine Learning
0 likes · 10 min read
Safely Repairing Broken Builds with Machine Learning
DataFunSummit
DataFunSummit
Jul 14, 2024 · Artificial Intelligence

Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This article surveys recent advances in applying causal inference to recommender systems, presenting three lines of work—causal embedding for interest‑conformity disentanglement, contrastive learning for long‑term and short‑term interest separation, and adversarial debiasing of duration bias in short‑video recommendation—along with experimental validation and insights.

Machine Learningbias mitigationcausal inference
0 likes · 24 min read
Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations
DataFunTalk
DataFunTalk
Jul 14, 2024 · Artificial Intelligence

Time Series and Machine Learning – An Overview and Book Introduction

The article introduces the rapid rise of large language models, the abundance of time‑series data in many sectors, and explains how combining machine‑learning and deep‑learning techniques with time‑series analysis has become a research hotspot, culminating in a new book that systematically covers theory, methods, and real‑world applications.

AIAnomaly DetectionMachine Learning
0 likes · 10 min read
Time Series and Machine Learning – An Overview and Book Introduction
Python Programming Learning Circle
Python Programming Learning Circle
Jul 12, 2024 · Artificial Intelligence

Building a Simple Neural Network from Scratch in Python

This article walks through constructing a basic neural network using only Python and NumPy, explains the underlying concepts such as neurons, training cycles, sigmoid activation, and weight‑adjustment formulas, and provides complete, runnable code with sample inputs and outputs.

Artificial IntelligenceMachine LearningNeural Network
0 likes · 9 min read
Building a Simple Neural Network from Scratch in Python
Ximalaya Technology Team
Ximalaya Technology Team
Jul 12, 2024 · Artificial Intelligence

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

In real‑time bidding advertising, a multi‑path recall framework quickly filters billions of ads using parallel non‑personalized and personalized strategies—such as hot‑item rules, collaborative‑filtering, skip‑gram vectors, and GraphSAGE embeddings—while respecting targeting constraints, before a ranking stage optimizes eCPM, with effectiveness measured offline and online and future extensions planned with large language models.

AdvertisingGraph Neural NetworkMachine Learning
0 likes · 18 min read
Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems
DataFunTalk
DataFunTalk
Jul 12, 2024 · Artificial Intelligence

Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications

This article presents a comprehensive overview of weak supervision machine learning techniques applied to Ant Group's business problems, covering theoretical foundations, cross‑domain causal effect estimation, noisy‑label denoising frameworks, experimental results, and practical use cases such as risk modeling and marketing interventions.

Machine LearningWeak Supervisioncausal inference
0 likes · 16 min read
Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications
AntTech
AntTech
Jul 11, 2024 · Information Security

Enhancing Fraud Transaction Detection via Unlabeled Suspicious Records (GIANTESS Framework)

The paper presents GIANTESS, a novel semi‑supervised fraud detection framework that leverages online‑identified suspicious transactions to augment the feature space, generating pseudo‑labels for out‑of‑distribution samples and employing a hybrid loss to improve detection of covert fraudulent activities, achieving notable recall gains on real‑world datasets.

GIANTESSMachine LearningSemi-supervised Learning
0 likes · 6 min read
Enhancing Fraud Transaction Detection via Unlabeled Suspicious Records (GIANTESS Framework)
Python Programming Learning Circle
Python Programming Learning Circle
Jul 9, 2024 · Artificial Intelligence

Principal Component Analysis (PCA) with Python: Theory and Practical Example on the Breast Cancer Dataset

This article explains the fundamentals of Principal Component Analysis (PCA), demonstrates its application on the Breast Cancer Wisconsin dataset using Python code, and shows how scaling, PCA transformation, scree plots, and feature-group comparisons can reveal data structure and improve predictive modeling.

Breast Cancer DatasetData visualizationMachine Learning
0 likes · 11 min read
Principal Component Analysis (PCA) with Python: Theory and Practical Example on the Breast Cancer Dataset
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 7, 2024 · Artificial Intelligence

Daily and Sports Activities Dataset: Description, Preprocessing Pipeline, and CNN Classification Results

This article introduces the Daily_and_Sports_Activities sensor dataset, details its structure and characteristics, provides a Python preprocessing pipeline with sliding‑window segmentation and Z‑score normalization, and reports CNN training results achieving 87.93% accuracy on activity classification.

CNNMachine LearningUCI
0 likes · 9 min read
Daily and Sports Activities Dataset: Description, Preprocessing Pipeline, and CNN Classification Results
Ops Development & AI Practice
Ops Development & AI Practice
Jul 6, 2024 · Artificial Intelligence

How Backpropagation Powers Modern Deep Learning: A Deep Dive

This article explains the backpropagation algorithm—its origins, mathematical basis, step‑by‑step workflow, importance for efficient neural network training, and widespread applications in image recognition, natural language processing, and recommendation systems.

BackpropagationMachine Learningdeep learning
0 likes · 6 min read
How Backpropagation Powers Modern Deep Learning: A Deep Dive
21CTO
21CTO
Jul 5, 2024 · Artificial Intelligence

15 Real-World Ways Companies Leverage Large Language Models

This article explores fifteen detailed examples of how major companies across sectors—from streaming and e‑commerce to transportation and social platforms—are harnessing large language models to improve search, personalize communications, detect fraud, and enhance operational efficiency.

AI case studiesEnterprise AILLM applications
0 likes · 9 min read
15 Real-World Ways Companies Leverage Large Language Models
DataFunSummit
DataFunSummit
Jul 5, 2024 · Artificial Intelligence

Building and Applying a User Profile Tagging System: Practices and Insights

This article presents a comprehensive overview of constructing and deploying a user and item profiling tag system at Qunar, covering tag taxonomy, integration challenges, technical architectures, algorithmic methods such as classification, recommendation, knowledge‑graph and causal inference, as well as real‑time streaming, ID‑mapping, and practical applications in marketing, attribution and A/B testing.

AB testingData EngineeringMachine Learning
0 likes · 21 min read
Building and Applying a User Profile Tagging System: Practices and Insights
Ops Development & AI Practice
Ops Development & AI Practice
Jul 4, 2024 · Artificial Intelligence

Discriminative vs Generative Models: When to Use Each in AI

The article explains the fundamental differences between discriminative and generative models, detailing their learning objectives, typical algorithms, key characteristics, example implementations, and practical application scenarios, helping readers choose the appropriate model for classification or data‑generation tasks.

AIDiscriminative ModelsGenerative Models
0 likes · 6 min read
Discriminative vs Generative Models: When to Use Each in AI
Tencent Cloud Developer
Tencent Cloud Developer
Jul 4, 2024 · Artificial Intelligence

Football Match Outcome Prediction and Betting Strategy Using Machine Learning

The study combines team statistics and bookmaker odds with machine‑learning models—including Poisson, regression, Bayesian, SVM, Random Forest, DNN, and LSTM—to predict football match outcomes, identify confidence‑based betting intervals that yield profit, and suggests extensions to broader data, features, and financial trading.

Machine LearningRandom Forestdata mining
0 likes · 23 min read
Football Match Outcome Prediction and Betting Strategy Using Machine Learning
Ops Development & AI Practice
Ops Development & AI Practice
Jul 3, 2024 · Artificial Intelligence

How Do Artificial Neural Networks Mirror Animal Brains? An In‑Depth Overview

This article explains the fundamental concepts and architecture of artificial neural networks, describes their learning process, compares them with biological neural systems, and highlights both the similarities and key differences in structure, learning mechanisms, flexibility, and energy efficiency.

Artificial IntelligenceBiological InspirationMachine Learning
0 likes · 7 min read
How Do Artificial Neural Networks Mirror Animal Brains? An In‑Depth Overview
Continuous Delivery 2.0
Continuous Delivery 2.0
Jul 2, 2024 · Artificial Intelligence

Dynamic Integrated Developer Activity (DIDACT): Large Sequence Models for Software Development

The article introduces DIDACT, a large‑scale multitask machine‑learning framework that trains on the full software‑development workflow—including edits, builds, reviews, and tool interactions—to create AI assistants that can predict and suggest developer actions throughout the coding process.

AI for CodeLarge Language ModelsMachine Learning
0 likes · 11 min read
Dynamic Integrated Developer Activity (DIDACT): Large Sequence Models for Software Development
Python Programming Learning Circle
Python Programming Learning Circle
Jun 27, 2024 · Artificial Intelligence

Eight Python Libraries to Accelerate Data Science and Machine Learning Workflows

This article introduces eight Python libraries—Optuna, ITMO_FS, Shap-hypetune, PyCaret, floWeaver, Gradio, Terality, and Torch-Handle—that streamline data science tasks such as hyperparameter optimization, feature selection, model building, visualization, and rapid prototyping, helping users save coding time and improve productivity.

AutomationMachine LearningPython
0 likes · 11 min read
Eight Python Libraries to Accelerate Data Science and Machine Learning Workflows
Ops Development & AI Practice
Ops Development & AI Practice
Jun 26, 2024 · Fundamentals

Why Jupyter Notebooks Revolutionized Data Science and Machine Learning

This article explores the origins, key innovations, and lasting impact of Jupyter notebooks, highlighting how their multi‑language support, interactive computing, reproducibility, and extensibility have transformed data exploration, collaboration, education, and research in modern data science and machine learning.

Interactive ComputingJupyterMachine Learning
0 likes · 5 min read
Why Jupyter Notebooks Revolutionized Data Science and Machine Learning
JD Tech Talk
JD Tech Talk
Jun 25, 2024 · Artificial Intelligence

Understanding Large Language Models: From Parameters to Transformer Architecture

This article explains the fundamental concepts behind large language models, including their two-file structure, training process, neural network basics, perceptron examples, weight and threshold calculations, the TensorFlow Playground, and a detailed walkthrough of the Transformer architecture with tokenization, positional encoding, self‑attention, normalization, and feed‑forward layers.

AILarge Language ModelsMachine Learning
0 likes · 20 min read
Understanding Large Language Models: From Parameters to Transformer Architecture
JavaEdge
JavaEdge
Jun 23, 2024 · Artificial Intelligence

Mapping the Generative AI Landscape: From Infrastructure to Applications

This article provides a comprehensive overview of the generative AI industry, detailing its upstream foundation layer, midstream large‑model and tool layers, downstream application scenarios, and an extensive glossary of models, techniques, platforms, and concepts.

AI ArchitectureIndustry OverviewMachine Learning
0 likes · 12 min read
Mapping the Generative AI Landscape: From Infrastructure to Applications
DataFunSummit
DataFunSummit
Jun 22, 2024 · Artificial Intelligence

Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice

This article introduces causal inference fundamentals, distinguishes correlation from causation, reviews major methodological streams, and demonstrates how uplift and gain models—implemented with T‑learner, S‑learner, and tree‑based approaches—can be applied to user growth and marketing scenarios, including evaluation metrics and future challenges.

A/B testingMachine LearningMarketing Analytics
0 likes · 14 min read
Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice
Continuous Delivery 2.0
Continuous Delivery 2.0
Jun 19, 2024 · Artificial Intelligence

Google Smart Paste: AI‑Powered Context‑Aware Adjustments for Pasted Code

Google's Smart Paste uses generative AI to automatically adapt pasted code to its surrounding context, reducing manual edits and improving developer productivity, as demonstrated by extensive internal studies involving tens of thousands of engineers and detailed model training, calibration, and user‑experience evaluations.

AI code assistanceGoogleMachine Learning
0 likes · 9 min read
Google Smart Paste: AI‑Powered Context‑Aware Adjustments for Pasted Code
Continuous Delivery 2.0
Continuous Delivery 2.0
Jun 18, 2024 · Artificial Intelligence

Google's ML‑Enhanced Code Completion Improves Developer Productivity

Google's research demonstrates that integrating a transformer‑based machine‑learning model with a rule‑based semantic engine for code completion reduces developers' coding iteration time by 6%, increases accepted suggestions to 25‑34%, and completes over 3% of code, highlighting significant productivity gains across multiple programming languages.

IDEMachine LearningTransformer
0 likes · 6 min read
Google's ML‑Enhanced Code Completion Improves Developer Productivity
DataFunTalk
DataFunTalk
Jun 15, 2024 · Artificial Intelligence

DataFunSummit2024 Recommendation System Architecture Summit Overview

The DataFunSummit2024 Recommendation System Architecture Summit invites participants to explore cutting‑edge advances in large‑model recommendation, training and inference optimization, feature engineering, multi‑task modeling, and graph‑based techniques through a series of expert talks and panel discussions from leading industry and academic researchers.

AILarge ModelsMachine Learning
0 likes · 33 min read
DataFunSummit2024 Recommendation System Architecture Summit Overview
php Courses
php Courses
Jun 13, 2024 · Artificial Intelligence

Using PHP for Data Dimensionality Reduction and Feature Extraction

This article explains the importance of data dimensionality reduction and feature extraction in machine learning, and provides a step‑by‑step guide with PHP code examples—including library installation, data preprocessing, PCA‑based reduction, and feature selection techniques—demonstrating how to handle large datasets efficiently.

Machine LearningPCAPHP
0 likes · 6 min read
Using PHP for Data Dimensionality Reduction and Feature Extraction
21CTO
21CTO
Jun 12, 2024 · Artificial Intelligence

How Alan Turing’s Legacy Fuels Today’s AI Revolution

This article chronicles Alan Turing’s groundbreaking work—from the invention of the Turing machine and his wartime code‑breaking feats to the birth of the Turing test—showing how his ideas continue to shape modern artificial intelligence, large language models, and the broader tech culture.

Alan TuringArtificial IntelligenceMachine Learning
0 likes · 10 min read
How Alan Turing’s Legacy Fuels Today’s AI Revolution
Qunar Tech Salon
Qunar Tech Salon
Jun 12, 2024 · Artificial Intelligence

Design and Implementation of Qunar Flight Ticket Intelligent Alert (Radar) System

This article presents a comprehensive analysis and engineering of Qunar's flight‑ticket intelligent pre‑warning (Radar) system, covering the business need, value analysis, architectural redesign, feature extraction, indicator classification, accuracy quantification, multi‑algorithm anomaly detection, automatic parameter tuning, observed effects, and future plans to incorporate large‑model techniques.

Anomaly DetectionMachine LearningOperations
0 likes · 17 min read
Design and Implementation of Qunar Flight Ticket Intelligent Alert (Radar) System
DataFunTalk
DataFunTalk
Jun 11, 2024 · Artificial Intelligence

Guide to Fine‑Tuning OpenAI Models for Improved Performance

This guide explains how to fine‑tune OpenAI’s pre‑trained models, covering data preparation, environment setup, API usage, code examples, hyper‑parameter tuning, monitoring, and best practices to achieve better performance with less data and compute resources.

AI modelsAPIMachine Learning
0 likes · 16 min read
Guide to Fine‑Tuning OpenAI Models for Improved Performance
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 11, 2024 · Artificial Intelligence

Mastering Retrieval‑Augmented Generation: Challenges, Paradigms, and Engineering Best Practices

This article explores Retrieval‑Augmented Generation (RAG) by outlining its background, inherent challenges such as knowledge limits and hallucinations, describing the Naïve, Advanced, and Modular RAG paradigms, and presenting practical engineering strategies for pre‑retrieval, retrieval, and post‑retrieval optimization.

Artificial IntelligenceKnowledge retrievalMachine Learning
0 likes · 25 min read
Mastering Retrieval‑Augmented Generation: Challenges, Paradigms, and Engineering Best Practices
DataFunSummit
DataFunSummit
Jun 7, 2024 · Artificial Intelligence

Understanding Feature Engineering for Risk Control Systems and Building an Easy-to-Use Feature Platform

Feature engineering, the process of creating input variables for machine learning models, is crucial for banking risk control; this article explains the concepts of features, variables, and metrics, outlines challenges in real‑time feature pipelines, and proposes a practical architecture and best practices for building an efficient, low‑code feature platform.

Machine Learningfeature engineeringplatform design
0 likes · 10 min read
Understanding Feature Engineering for Risk Control Systems and Building an Easy-to-Use Feature Platform
Java Tech Enthusiast
Java Tech Enthusiast
Jun 7, 2024 · Fundamentals

Engineer Builds GPU from Scratch in Two Weeks

In just two weeks, engineer Adam Majmudar designed and implemented a minimalist GPU called tiny‑gpu—complete with a custom 11‑instruction ISA, Verilog RTL, and verified via OpenLane—sharing the open‑source project on GitHub, earning thousands of stars, and preparing it for fabrication through Tiny Tapeout 7, showcasing how modern tools make DIY chip design increasingly accessible.

EDAGPUMachine Learning
0 likes · 8 min read
Engineer Builds GPU from Scratch in Two Weeks
DataFunSummit
DataFunSummit
Jun 4, 2024 · Artificial Intelligence

Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems

This article details eBay's practical experience integrating multimodal data and graph neural networks into its recommendation pipeline, covering pain‑point analysis, a twin‑tower multimodal embedding model with triplet loss and TransH, engineering design, experimental results, and key takeaways for future AI‑driven product development.

EmbeddingGNNGraph Neural Network
0 likes · 19 min read
Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems
DataFunSummit
DataFunSummit
Jun 2, 2024 · Artificial Intelligence

Construction and Application of a User Profile Tag System: Methods, Platforms, and Use Cases

This article presents a comprehensive overview of building a user profile tag system—including tag taxonomy, platform architecture, construction methods, update cycles, access patterns, common algorithmic tags, and real‑world applications such as marketing, metric attribution, and A/B testing—illustrated with examples and a detailed Q&A session from a data‑mining senior manager at Qunar.

AB testingMachine Learningcausal inference
0 likes · 21 min read
Construction and Application of a User Profile Tag System: Methods, Platforms, and Use Cases
DataFunSummit
DataFunSummit
Jun 1, 2024 · Artificial Intelligence

Graph Foundation Models: Concepts, Progress, and Future Directions

This article provides a comprehensive overview of Graph Foundation Models (GFMs), covering their definition, key characteristics, historical development of graph machine learning, recent research trends such as PT‑HGNN, Specformer, and GraphTranslator, and discusses future challenges and research directions.

Large Language ModelsMachine Learningfoundation models
0 likes · 23 min read
Graph Foundation Models: Concepts, Progress, and Future Directions