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1966 articles · Page 12 of 20
DataFunTalk
DataFunTalk
Jun 20, 2021 · Artificial Intelligence

Iterative Development and Applications of Meituan Takeaway Food Knowledge Graph

This article systematically introduces the architecture, iterative improvements, modeling techniques, and practical applications of Meituan's food knowledge graph, covering category taxonomy, standard dish names, basic and thematic attributes, health‑meal detection, dish entity alignment, and downstream recommendation and search use cases.

AIBERTFood Recommendation
0 likes · 18 min read
Iterative Development and Applications of Meituan Takeaway Food Knowledge Graph
Python Programming Learning Circle
Python Programming Learning Circle
Jun 19, 2021 · Artificial Intelligence

Template Notebook for Building Machine Learning Models with Scikit-learn

This notebook provides ready‑to‑use Python code templates for ten common machine‑learning algorithms—including linear regression, logistic regression, decision trees, Naïve Bayes, SVM, K‑Nearest Neighbors, K‑Means, Random Forest, PCA, and Gradient Boosting—showing how to import, train, evaluate, and predict with scikit‑learn.

AIClassificationmachine learning
0 likes · 8 min read
Template Notebook for Building Machine Learning Models with Scikit-learn
DataFunTalk
DataFunTalk
Jun 18, 2021 · Artificial Intelligence

Splicing Recall for Flight Ticket Search: Challenges, Algorithmic Solutions, and Online Impact

This article presents the technical exploration of splicing recall in flight ticket search at Alibaba's Fliggy, detailing the background, challenges, constrained routing and machine‑learning algorithms, the four‑step solution pipeline, and the resulting improvements in ticket availability and conversion rates.

Routing Algorithmflight searchmachine learning
0 likes · 13 min read
Splicing Recall for Flight Ticket Search: Challenges, Algorithmic Solutions, and Online Impact
Python Crawling & Data Mining
Python Crawling & Data Mining
Jun 14, 2021 · Big Data

Why Stanford’s Data Mining Tutorial Is the Ultimate Guide to Large‑Scale Data Mining

This article introduces the third edition of Stanford’s Data Mining Tutorial, highlighting its panoramic roadmap of data‑mining techniques for massive datasets, core features, comprehensive topic coverage, target audience, and supplementary resources while noting its popularity among students and professionals.

AlgorithmsDistributed ComputingStanford
0 likes · 11 min read
Why Stanford’s Data Mining Tutorial Is the Ultimate Guide to Large‑Scale Data Mining
DataFunTalk
DataFunTalk
Jun 12, 2021 · Artificial Intelligence

An Introduction to Machine Learning: Concepts, Learning Path, and Knowledge System

This article provides a comprehensive overview of machine learning, explaining core AI terminology, distinguishing statistics, statistical learning, and machine learning, outlining a three‑part learning roadmap covering mathematical foundations, algorithms, and Python programming practice, and offering curated resources for building a solid knowledge system.

AI FundamentalsLearning Roadmapdeep learning
0 likes · 8 min read
An Introduction to Machine Learning: Concepts, Learning Path, and Knowledge System
Big Data Technology & Architecture
Big Data Technology & Architecture
Jun 10, 2021 · Big Data

User Profiling: Concepts, Tag Classification, Tag‑System Construction, Applications and Implementation Steps

This article provides a comprehensive overview of user profiling, covering its definition, the five‑dimensional framework (goal, method, organization, standards, validation), various tag classifications, tag‑system architecture, modeling techniques, practical applications such as precise marketing and product innovation, and a step‑by‑step guide for building a profiling system using big‑data and AI methods.

Customer Segmentationbig datadata tagging
0 likes · 24 min read
User Profiling: Concepts, Tag Classification, Tag‑System Construction, Applications and Implementation Steps
Meituan Technology Team
Meituan Technology Team
Jun 10, 2021 · Artificial Intelligence

Deep Position-wise Interaction Network for CTR Prediction

The Meituan team introduces DPIN, a three‑module deep network that jointly models ads and their positions to mitigate position bias in CTR prediction, achieving up to 2.98% AUC improvement, 2.25% higher CTR and 2.15% RPM gains while keeping latency modest, and is applicable to broader ranking tasks.

AdvertisingCTR predictionDPIN
0 likes · 24 min read
Deep Position-wise Interaction Network for CTR Prediction
Alibaba Terminal Technology
Alibaba Terminal Technology
Jun 10, 2021 · Artificial Intelligence

How to Choose the Right JavaScript Machine Learning Framework for Front‑End Projects

This article outlines a four‑layer methodology for selecting JavaScript‑based machine‑learning tools—ranging from domain‑specific NLP libraries to deep‑learning, classic ML, and math‑statistical packages—providing code examples, installation commands, and practical tips for front‑end developers.

machine learningnlp.jsstdlib
0 likes · 11 min read
How to Choose the Right JavaScript Machine Learning Framework for Front‑End Projects
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Jun 8, 2021 · Artificial Intelligence

Why Edge‑Cloud Lifelong Learning Is the Next Frontier for AI

Edge‑cloud collaborative machine learning faces data latency, cost, compression, privacy, and heterogeneity challenges, prompting a shift from closed learning to lifelong learning that leverages cloud‑side knowledge bases and edge‑side incremental updates, as demonstrated by the Sedna platform’s thermal comfort prediction case study.

AICloud ComputingLifelong Learning
0 likes · 19 min read
Why Edge‑Cloud Lifelong Learning Is the Next Frontier for AI
58 Tech
58 Tech
Jun 7, 2021 · Artificial Intelligence

AI‑Driven CRM: Intelligent Opportunity Distribution and Sales Voice Assistant at 58.com

This article details how 58.com’s AI Lab applied machine‑learning, recommendation, search, speech and NLP technologies to transform its CRM system into an intelligent opportunity distribution platform and sales voice assistant, describing the underlying models, the "Michigan" workflow, AB‑testing results and future AI‑driven enhancements.

AB TestingAICRM
0 likes · 21 min read
AI‑Driven CRM: Intelligent Opportunity Distribution and Sales Voice Assistant at 58.com
DataFunTalk
DataFunTalk
Jun 2, 2021 · Artificial Intelligence

Industrial-Scale Graph Learning for JD Advertising: 9N GRAPH End‑to‑End Solution and BVSHG Model

This article introduces JD.com's 9N GRAPH industrialization framework for large‑scale graph algorithms in advertising, covering the challenges of e‑commerce recommendation, the end‑to‑end solution architecture, the BVSHG multi‑behavior heterogeneous GNN model, training pipelines, and observed business impact.

BVSHGJD.comLarge-Scale Graph
0 likes · 17 min read
Industrial-Scale Graph Learning for JD Advertising: 9N GRAPH End‑to‑End Solution and BVSHG Model
Python Programming Learning Circle
Python Programming Learning Circle
Jun 2, 2021 · Artificial Intelligence

Implementing Linear Regression from Scratch in Python

This tutorial walks through the complete process of building a linear regression model in Python from loading a housing price dataset, normalizing features, defining hypothesis, cost and gradient‑descent functions, visualising data and cost convergence, and testing predictions, with full source code provided.

Linear RegressionPythongradient descent
0 likes · 12 min read
Implementing Linear Regression from Scratch in Python
Architects Research Society
Architects Research Society
May 30, 2021 · Artificial Intelligence

Artificial Intelligence vs. Machine Learning: Definitions, History, and Key Differences

This article explains the origins, definitions, and evolving relationship between artificial intelligence and machine learning, highlighting their historical milestones, core concepts, and how modern applications like deep learning, neural networks, and recommendation systems illustrate their intertwined development.

AIDefinitionsTechnology
0 likes · 8 min read
Artificial Intelligence vs. Machine Learning: Definitions, History, and Key Differences
Architects Research Society
Architects Research Society
May 28, 2021 · Artificial Intelligence

Managing New Data to Power Artificial Intelligence and Building an AI Assembly Line

The article explains how enterprises must organize increasingly complex data—from structured sources to social media and IoT—to unlock AI value, describing data lakes, hybrid cloud strategies, and the concept of an AI assembly line that integrates tools, containers, and cross‑functional teams for scalable machine‑learning deployment.

Artificial Intelligencedata lakesmachine learning
0 likes · 7 min read
Managing New Data to Power Artificial Intelligence and Building an AI Assembly Line
Meituan Technology Team
Meituan Technology Team
May 27, 2021 · Artificial Intelligence

Iterative Development and Applications of Meituan Takeaway Food Knowledge Graph

The Meituan Takeaway Food Knowledge Graph iteratively builds a hierarchical tag taxonomy, standardizes dish names, extracts basic and theme attributes, aligns online‑offline entities using CNN‑CRF, BERT and hybrid models, and powers combo, interactive and search recommendations while planning scene‑specific tags and graph‑based personalization.

BERTFood RecommendationKnowledge Graph
0 likes · 19 min read
Iterative Development and Applications of Meituan Takeaway Food Knowledge Graph
Architects Research Society
Architects Research Society
May 23, 2021 · Big Data

Data Architecture Trends: From Chaos to an Organized Era – Insights from Anthony J. Algmin

The article reviews Anthony J. Algmin’s reflections on past data‑architecture predictions, current hot topics such as cloud, AI/ML, data governance, and real‑time analytics, and forecasts future trends including metadata management, blockchain, and the evolving role of data architects within enterprises.

Artificial Intelligencebig datadata architecture
0 likes · 13 min read
Data Architecture Trends: From Chaos to an Organized Era – Insights from Anthony J. Algmin
DataFunTalk
DataFunTalk
May 20, 2021 · Artificial Intelligence

Fundamentals and Nuances of CTR (Click‑Through Rate) Modeling

This article explains the theoretical foundations of CTR modeling, why click‑through rates are intrinsically unpredictable at the micro level, the simplifying assumptions that make binary classification feasible, and how evaluation metrics like AUC, contradictory samples, theoretical AUC bounds, and calibration affect model performance.

AUCAdvertisingCTR
0 likes · 18 min read
Fundamentals and Nuances of CTR (Click‑Through Rate) Modeling
DataFunTalk
DataFunTalk
May 19, 2021 · Artificial Intelligence

Causal Inference for Optimizing Advertising Budget Allocation in Fliggy Search CPC Ads

This article explains how causal inference techniques are applied to model the uplift effect of ad placement in Alibaba's Fliggy search CPC advertising, transforming budget allocation into a multi‑objective optimization problem and describing practical control methods, feature engineering, sample re‑sampling, model designs, uplift evaluation, and future research directions.

AdvertisingCausal InferenceUplift Modeling
0 likes · 18 min read
Causal Inference for Optimizing Advertising Budget Allocation in Fliggy Search CPC Ads
Tencent Advertising Technology
Tencent Advertising Technology
May 19, 2021 · Artificial Intelligence

Experience Sharing on Using Tencent TI-ONE Platform for Advertising Algorithm Competition

This article shares personal experiences and insights from using Tencent's TI-ONE machine learning platform in the 2020 Tencent Advertising Algorithm Competition, covering platform features, development modes, resource management, and lessons learned for future participants.

Advertising CompetitionGPU computingNotebook Mode
0 likes · 6 min read
Experience Sharing on Using Tencent TI-ONE Platform for Advertising Algorithm Competition
Intelligent Backend & Architecture
Intelligent Backend & Architecture
May 19, 2021 · Big Data

8 Real-World Big Data Analytics Scenarios and Essential Machine Learning Algorithms

This article outlines eight practical big‑data analytics use cases—from product recommendation and pricing to churn prediction—and introduces fundamental machine‑learning algorithms such as linear regression, decision trees, SVM, and random forests that power these applications.

Business IntelligenceData Analyticsmachine learning
0 likes · 17 min read
8 Real-World Big Data Analytics Scenarios and Essential Machine Learning Algorithms
Didi Tech
Didi Tech
May 18, 2021 · R&D Management

Growth Journeys of Didi Ride-Hailing Engineers: From New Graduates to Technical Leaders

The article follows three Didi Ride‑Hailing engineers who joined in 2016 as fresh PhDs, detailing how they leveraged machine‑learning, dynamic dispatch, and product‑line automation to rise from junior developers to technical leaders, highlighting the blend of hard coding, soft communication, rapid‑learning culture, and the team’s current senior‑role hiring drive.

DidiDispatch algorithmR&D management
0 likes · 10 min read
Growth Journeys of Didi Ride-Hailing Engineers: From New Graduates to Technical Leaders
DataFunTalk
DataFunTalk
May 17, 2021 · Artificial Intelligence

Comprehensive Overview of Machine Learning Model Evaluation Metrics

This article provides a comprehensive summary of machine learning model evaluation metrics, covering accuracy, precision, recall, F1, RMSE, ROC/AUC, KS test, and scoring cards, with explanations, formulas, code examples, and practical considerations for model performance assessment.

AUCKSRecall
0 likes · 19 min read
Comprehensive Overview of Machine Learning Model Evaluation Metrics
Beijing SF i-TECH City Technology Team
Beijing SF i-TECH City Technology Team
May 17, 2021 · Artificial Intelligence

AIOps Overview: Concepts, Applications, and Case Studies

This article provides a comprehensive overview of AIOps, covering its definition, evolution from manual to AI-driven operations, core capabilities, and real-world applications in capacity prediction, anomaly detection, and alarm merging, illustrated with case studies from a food‑retail giant and internal logistics.

AIOpsArtificial IntelligenceCapacity Prediction
0 likes · 13 min read
AIOps Overview: Concepts, Applications, and Case Studies
DataFunTalk
DataFunTalk
May 15, 2021 · Artificial Intelligence

Multi‑Interest Recall Techniques in iQIYI Short‑Video Recommendation

The article reviews the evolution of iQIYI's short‑video recommendation recall pipeline, detailing multi‑interest recall methods such as clustering‑based recall, MOE‑based recall, single‑activation multi‑interest networks, regularization strategies, dynamic capacity handling, and multimodal extensions, and discusses their impact on recommendation performance.

RecallTransformeriQIYI
0 likes · 15 min read
Multi‑Interest Recall Techniques in iQIYI Short‑Video Recommendation
iQIYI Technical Product Team
iQIYI Technical Product Team
May 14, 2021 · Artificial Intelligence

Performance Optimization of TensorFlow Feature Columns in Recommendation Systems

The article details how iQIYI doubled online inference speed and cut p99 latency by over 50% in TensorFlow‑based CTR recommendation models by replacing costly string‑based integer hashing, removing redundant dense‑sparse conversions, and deduplicating user features for efficient broadcasting, demonstrating that modest Feature Column tweaks can yield major production gains.

Feature ColumnsPerformance OptimizationTensorFlow
0 likes · 11 min read
Performance Optimization of TensorFlow Feature Columns in Recommendation Systems
DataFunTalk
DataFunTalk
May 13, 2021 · Artificial Intelligence

Continuous Causal Forest: Extending Uplift Modeling to Multivariate and Continuous Treatments

This article introduces the Continuous Causal Forest, a novel uplift modeling approach that expands binary treatment effect estimation to handle multivariate and continuous treatment variables, demonstrates its construction, evaluates its performance on ride‑hailing pricing strategies, and discusses its advantages, limitations, and future research directions.

Causal InferencePricing StrategyUplift Modeling
0 likes · 9 min read
Continuous Causal Forest: Extending Uplift Modeling to Multivariate and Continuous Treatments
Python Crawling & Data Mining
Python Crawling & Data Mining
May 10, 2021 · Fundamentals

Master NumPy: Turn Math Formulas into Python Code

This article explains how to use Python's NumPy library to translate common mathematical formulas—such as powers, roots, absolute values, vector and matrix operations—into concise, executable code, covering setup, basic operations, and practical examples for data analysis and machine learning.

Data AnalysisNumPyPython
0 likes · 11 min read
Master NumPy: Turn Math Formulas into Python Code
Python Programming Learning Circle
Python Programming Learning Circle
May 8, 2021 · Artificial Intelligence

Top 10 New Features in Scikit‑learn 0.24

The article reviews the most important additions in scikit‑learn 0.24, including faster hyper‑parameter search methods, ICE plots, histogram‑based boosting improvements, new feature‑selection tools, polynomial‑feature approximations, a semi‑supervised classifier, MAPE metric, enhanced OneHotEncoder and OrdinalEncoder handling, and a more flexible RFE interface.

Pythondata preprocessingfeature selection
0 likes · 8 min read
Top 10 New Features in Scikit‑learn 0.24
DataFunTalk
DataFunTalk
May 8, 2021 · Artificial Intelligence

Attribute‑Level Sentiment Analysis for E‑commerce: Tasks, Challenges, and System Design

This article presents a comprehensive overview of sentiment analysis in user‑generated content, detailing document‑, sentence‑, and aspect‑level tasks, defining the Aspect Sentiment Triplet Extraction problem for e‑commerce reviews, describing a three‑stage pipeline with pre‑training, multi‑domain modeling and attribute normalization, and reporting significant business improvements such as 400% CTR lift, while also discussing data imbalance, annotation scarcity, and future research directions.

Natural Language ProcessingPretrainingSentiment Analysis
0 likes · 15 min read
Attribute‑Level Sentiment Analysis for E‑commerce: Tasks, Challenges, and System Design
NiuNiu MaTe
NiuNiu MaTe
May 2, 2021 · Fundamentals

How to Master Python Quickly: A Complete Learning Roadmap for 2024

This guide explains why Python is essential, presents a step‑by‑step learning roadmap covering beginner basics, backend web development, web crawling, data analysis, and machine learning, and provides curated resources and project links to help learners progress efficiently.

Backend DevelopmentData AnalysisLearning Roadmap
0 likes · 8 min read
How to Master Python Quickly: A Complete Learning Roadmap for 2024
DataFunTalk
DataFunTalk
May 1, 2021 · Artificial Intelligence

How to Evaluate Machine Learning Model Performance Before Production Deployment

This tutorial walks through a practical case of predicting employee attrition, demonstrating how to assess and compare machine‑learning models using ROC AUC, confusion matrices, precision‑recall trade‑offs, and the Evidently library to generate performance dashboards, helping choose the best model for production.

HR attritionROC AUCevidently
0 likes · 17 min read
How to Evaluate Machine Learning Model Performance Before Production Deployment
JD Tech
JD Tech
Apr 30, 2021 · Artificial Intelligence

Smart DMP: A Next‑Generation Intelligent Targeting System for E‑commerce Advertising

This article reviews the limitations of traditional DMP and AI‑driven intelligent targeting in e‑commerce, introduces JD.com's Smart DMP framework that combines merchant intent with high‑relevance modeling, and presents experimental results showing over 15% CTR improvement and widespread merchant adoption.

AIAdvertisingSmart DMP
0 likes · 9 min read
Smart DMP: A Next‑Generation Intelligent Targeting System for E‑commerce Advertising
DataFunTalk
DataFunTalk
Apr 24, 2021 · Artificial Intelligence

Intelligent Advertising Delivery System and Techniques: From Budget‑Constrained Bidding to Multi‑Channel Optimization

This article systematically introduces Alibaba's advertising intelligence platform, covering the evolution from basic CPM/CPC models to advanced OCPC/OCPM, budget‑constrained bidding, multi‑constraint bidding, sequence‑based long‑term value bidding, multi‑channel allocation, and the AI‑driven Smart Bidding product, highlighting algorithmic foundations, practical implementations, and performance gains.

AdvertisingMulti-Channelbidding
0 likes · 32 min read
Intelligent Advertising Delivery System and Techniques: From Budget‑Constrained Bidding to Multi‑Channel Optimization
Python Crawling & Data Mining
Python Crawling & Data Mining
Apr 24, 2021 · Fundamentals

Discover 140+ Must‑Know Python Libraries for Data Science & AI

The article presents a comprehensive guide to Python's built‑in functions, standard libraries, and third‑party packages across file I/O, web scraping, databases, data cleaning, statistical analysis, machine learning, visualization, and more, rating each with stars and offering a free e‑book collection for readers.

Data AnalysisLibrariesPython
0 likes · 32 min read
Discover 140+ Must‑Know Python Libraries for Data Science & AI
MaGe Linux Operations
MaGe Linux Operations
Apr 23, 2021 · Artificial Intelligence

Why Python Dominates Machine Learning and AI Development

Python has become the go‑to language for AI and machine learning across startups and enterprises because of its rapid prototyping, flexible syntax, readability, extensive libraries like NumPy, SciPy, scikit‑learn, Pandas, Keras, and powerful visualization tools, making development faster, scalable, and easier to maintain.

Artificial IntelligenceData SciencePython
0 likes · 8 min read
Why Python Dominates Machine Learning and AI Development
iQIYI Technical Product Team
iQIYI Technical Product Team
Apr 23, 2021 · Artificial Intelligence

How iQIYI’s Multi‑Interest Recall Transforms Video Recommendation

This article analyzes iQIYI’s evolution of multi‑interest recall techniques—from clustering‑based PinnerSage to MOE and single‑activation models—showing how extracting multiple user interests improves recall diversity, mitigates filter bubbles, and boosts key performance metrics in short‑video recommendation.

iQIYImachine learningmulti-interest recall
0 likes · 16 min read
How iQIYI’s Multi‑Interest Recall Transforms Video Recommendation
Amap Tech
Amap Tech
Apr 23, 2021 · Artificial Intelligence

How AI Powers Gaode’s Route Planning and Navigation: Inside the Tech

The 17‑minute video replay of Gaode's Technology Open Day showcases Cui Hengbin’s deep‑dive into AI‑driven route planning, covering pre‑trip, in‑trip, and arrival phases, road‑condition prediction, ETA estimation, dynamic traffic mining, and references to award‑winning research papers.

AITraffic Predictionmachine learning
0 likes · 2 min read
How AI Powers Gaode’s Route Planning and Navigation: Inside the Tech
DataFunTalk
DataFunTalk
Apr 22, 2021 · Artificial Intelligence

Governance Algorithms for O2O Platforms: Challenges, Framework, and Model Exploration

This article presents Didi's comprehensive governance algorithm system for O2O platforms, detailing business background, technical challenges, a three‑stage algorithmic framework, model innovations such as small‑sample learning, multi‑task and transfer learning, and extensive feature engineering including multimodal and streaming features.

O2O platformsfeature engineeringgovernance algorithms
0 likes · 15 min read
Governance Algorithms for O2O Platforms: Challenges, Framework, and Model Exploration
DataFunTalk
DataFunTalk
Apr 17, 2021 · Artificial Intelligence

Personalized Re-ranking for Recommendation (ResSys'19)

This article introduces a personalized re‑ranking model for recommendation systems, explaining the limitations of traditional point‑wise ranking, describing the PRM architecture with input, encoding, and output layers using multi‑head attention and pre‑trained personalization features, and presenting experimental results and future extensions.

AttentionCTRTransformer
0 likes · 7 min read
Personalized Re-ranking for Recommendation (ResSys'19)
DataFunSummit
DataFunSummit
Apr 15, 2021 · Artificial Intelligence

Call for Papers: 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021)

The 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021) invites submissions on deep‑learning systems, data representation, and user modeling for large‑scale sparse data, with a submission deadline of May 10 2021 and results announced on June 10 2021.

KDDSparse DataWorkshop
0 likes · 6 min read
Call for Papers: 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021)
TAL Education Technology
TAL Education Technology
Apr 15, 2021 · Big Data

Global Feature Pool Architecture and Workflow for Data‑Driven Growth

The article describes a unified global feature pool architecture that standardizes offline and real‑time feature production, management, and service layers using Hive, Spark, Flink, Kafka, MySQL, and Hologres to break data silos, improve algorithm development efficiency, and boost growth business performance.

data pipelinedata platformfeature engineering
0 likes · 7 min read
Global Feature Pool Architecture and Workflow for Data‑Driven Growth
58 Tech
58 Tech
Apr 12, 2021 · Artificial Intelligence

AI + CRM and Recommendation Recall & Ranking Practices Presented at ML‑Summit 2021

The ML‑Summit 2021 in Beijing featured two AI‑focused talks—one on applying AI to CRM for boosting enterprise performance and another on systematic recommendation recall and ranking optimization—each presented by senior engineers from 58.com, with detailed abstracts and speaker biographies.

AICRMML‑Summit
0 likes · 5 min read
AI + CRM and Recommendation Recall & Ranking Practices Presented at ML‑Summit 2021
DataFunTalk
DataFunTalk
Apr 12, 2021 · Artificial Intelligence

Comprehensive Survey of Graph Neural Networks: 15 Key Review Papers and Resources

This article compiles and summarizes fifteen influential survey papers on Graph Neural Networks, covering their models, applications, datasets, benchmarks, challenges, and future directions, while providing links to the original PDFs and highlighting distinctions between small and large-scale graph learning.

deep learninggraph learningmachine learning
0 likes · 20 min read
Comprehensive Survey of Graph Neural Networks: 15 Key Review Papers and Resources
DataFunTalk
DataFunTalk
Mar 23, 2021 · Artificial Intelligence

Explainability in Graph Neural Networks: A Taxonomic Survey

This article surveys recent advances in graph neural network explainability, systematically categorizing instance‑level and model‑level methods, reviewing datasets, evaluation metrics, and proposing new benchmark graph datasets for interpretable GNN research, and highlighting future research directions.

GNNbenchmark datasetsexplainability
0 likes · 40 min read
Explainability in Graph Neural Networks: A Taxonomic Survey
OPPO Kernel Craftsman
OPPO Kernel Craftsman
Mar 19, 2021 · Databases

Learned Index Structures: Applying Machine Learning to Database Indexing

Learned Index Structures replace conventional B‑Tree and Bloom filter indexes with hierarchical machine‑learning models that predict key positions directly, reducing search overhead, but require careful model selection, training, and handling of updates, making them a promising yet still experimental alternative to traditional database indexing techniques.

Learned IndexStaged Modeldatabase
0 likes · 11 min read
Learned Index Structures: Applying Machine Learning to Database Indexing
58 Tech
58 Tech
Mar 17, 2021 · Artificial Intelligence

Practical Applications of OCR Technology in 58 Information Security Scenarios: Layout Analysis

This article presents the practical deployment of OCR technology within 58’s information‑security workflows, focusing on layout‑analysis techniques for document and credential recognition, detailing rule‑based, template‑matching, object‑detection, and image‑segmentation methods, their implementation steps, experimental results, advantages, limitations, and future directions.

Document RecognitionOCRlayout analysis
0 likes · 18 min read
Practical Applications of OCR Technology in 58 Information Security Scenarios: Layout Analysis
DeWu Technology
DeWu Technology
Mar 12, 2021 · Industry Insights

How Do Recommendation Systems Rank Items? A Deep Dive into Models and Strategies

This article explains the architecture and ranking process of modern recommendation systems, covering the two-stage pipeline of candidate generation and ranking, the evolution from rule‑based methods to logistic regression, GBDT, wide‑and‑deep, and deep learning models, and discusses challenges such as feature non‑linearity, multi‑objective optimization, and the need for post‑ranking interventions.

GBDTLogistic Regressiondeep learning
0 likes · 15 min read
How Do Recommendation Systems Rank Items? A Deep Dive into Models and Strategies
Baidu Intelligent Testing
Baidu Intelligent Testing
Mar 10, 2021 · Artificial Intelligence

End-to-End Consistency Assurance for Click‑Through Rate Models: Methodology, Implementation, and Reporting

This article presents a comprehensive model quality assurance framework for click‑through‑rate (CTR) prediction, detailing the challenges of data and logic inconsistency, defining consistency goals, describing a full‑stack verification pipeline—including online data capture, offline sample alignment, multi‑stage q‑value comparison, and automated reporting—and sharing practical deployment experiences and results.

CTRdata governancemachine learning
0 likes · 19 min read
End-to-End Consistency Assurance for Click‑Through Rate Models: Methodology, Implementation, and Reporting
DevOps
DevOps
Mar 10, 2021 · Artificial Intelligence

Ant Financial's Intelligent Middle Platform: AI Applications, Data Infrastructure, and Security Practices

This article presents Ant Financial's intelligent middle platform, detailing AI use cases such as risk control, wealth management, lending, marketing, insurance, and customer service, alongside the AI capability map, data foundation architecture, annotation workflows, security measures, and the overall impact on fintech innovation.

Data Infrastructureannotationdata security
0 likes · 8 min read
Ant Financial's Intelligent Middle Platform: AI Applications, Data Infrastructure, and Security Practices
DataFunTalk
DataFunTalk
Mar 9, 2021 · Artificial Intelligence

Introduction to Common Machine Learning Algorithms with Python Implementations

This article introduces the three main categories of machine learning—supervised, unsupervised, and reinforcement learning—detailing common algorithms such as Linear Regression, Logistic Regression, Naive Bayes, K‑Nearest Neighbors, Decision Trees, Random Forests, SVM, K‑Means, and PCA, and provides concise Python code examples using scikit‑learn for each.

PythonUnsupervised Learningmachine learning
0 likes · 18 min read
Introduction to Common Machine Learning Algorithms with Python Implementations
DataFunTalk
DataFunTalk
Mar 4, 2021 · Artificial Intelligence

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

This article presents the design and implementation of interactive recommendation and travel‑theme recommendation in Alibaba's Fliggy app, covering background, user demand classification, real‑time interest capture, various recall strategies, ranking models, multi‑task learning, and engineering tricks to improve CTR and user experience.

AIFliggyinteractive recommendation
0 likes · 16 min read
Interactive Recommendation and Travel Theme Recommendation in the Fliggy App
Baidu Intelligent Testing
Baidu Intelligent Testing
Mar 3, 2021 · Artificial Intelligence

Quality Scoring Model: Intelligent Test Grading and Risk Assessment for Software Delivery

This article introduces a quality scoring model that leverages structured development and testing data to objectively assess project risk, automate test grading, and enable data‑driven decisions for test execution and release, thereby improving delivery efficiency and reducing manual evaluation errors.

Data‑Driven Testingmachine learningquality scoring
0 likes · 24 min read
Quality Scoring Model: Intelligent Test Grading and Risk Assessment for Software Delivery
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Feb 26, 2021 · Artificial Intelligence

Inside Toutiao's Transparent Real-Time Recommendation Engine

This article details how Toutiao's senior algorithm architect designs a transparent recommendation system, covering system overview, three-dimensional feature modeling, real-time training pipelines, recall strategies, content analysis, user tagging, evaluation methods, and content safety measures.

Content AnalysisReal-time Trainingcontent safety
0 likes · 17 min read
Inside Toutiao's Transparent Real-Time Recommendation Engine
Suning Technology
Suning Technology
Feb 25, 2021 · Operations

How to Optimize O2O Delivery Fulfillment for Maximum Efficiency?

This article analyzes the rapid growth of O2O home‑delivery, examines the challenges of delivery fulfillment, compares third‑party and self‑built rider models, and presents hybrid, batch‑ordering, and AI‑driven optimization strategies to reduce costs and boost efficiency.

O2OOperationsdelivery
0 likes · 10 min read
How to Optimize O2O Delivery Fulfillment for Maximum Efficiency?
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 24, 2021 · Artificial Intelligence

How Alibaba’s ICBU Algorithm Team Transformed E‑Commerce in 2020

This article reviews the 2020 achievements of Alibaba.com’s ICBU algorithm team, explaining the evolving role of algorithm engineers, the fundamentals of e‑commerce algorithms, the team’s three‑pillar workflow of Understanding, Growth, and Matching, and the technical breakthroughs that drove business impact and future directions.

AlibabaOptimizationalgorithm
0 likes · 28 min read
How Alibaba’s ICBU Algorithm Team Transformed E‑Commerce in 2020
DataFunTalk
DataFunTalk
Feb 24, 2021 · Artificial Intelligence

Multi‑Objective Ranking in Kuaishou Short‑Video Recommendation: System Design and Online Results

This article details Kuaishou's multi‑objective ranking pipeline for short‑video recommendation, covering manual score fusion, GBDT ensemble, Learn‑to‑Rank, online auto‑tuning, ensemble sorting, reinforcement‑learning rerank, and on‑device rerank, and reports their impact on DAU, watch time and user interaction.

Kuaishoumachine learningmulti-objective ranking
0 likes · 21 min read
Multi‑Objective Ranking in Kuaishou Short‑Video Recommendation: System Design and Online Results
AntTech
AntTech
Feb 24, 2021 · Artificial Intelligence

Ant Group's Self‑Developed Graph Neural Network Research: GeniePath and Bandit Sampler

This article introduces the fundamentals of graph neural networks, explains their expressive power for relational risk identification, and details Ant Group's innovations—including the GeniePath architecture and a bandit‑based sampling optimizer—that achieve superior performance on benchmark datasets.

GNNGeniePathGraph Neural Networks
0 likes · 7 min read
Ant Group's Self‑Developed Graph Neural Network Research: GeniePath and Bandit Sampler
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Feb 23, 2021 · Artificial Intelligence

How Meituan Built a One‑Stop Machine Learning Platform for Delivery Optimization

This article explains how Meituan’s delivery business has transitioned from data online to AI‑driven decision making by building a comprehensive, one‑stop machine learning platform that includes model management, data graph, feature store, AB testing, and a machine‑learning definition language to accelerate algorithm iteration and reduce operational costs.

AB TestingAI platformDelivery Logistics
0 likes · 5 min read
How Meituan Built a One‑Stop Machine Learning Platform for Delivery Optimization
Xianyu Technology
Xianyu Technology
Feb 23, 2021 · Artificial Intelligence

Pricing Guidance System for Xianyu Secondhand Marketplace

The Xianyu pricing guidance system blends new‑product market values with depreciation factors derived from usage, condition and category attributes—extracted via real‑time text mining and image analysis—to recommend dynamic price ranges adjusted for supply‑demand and seller urgency, currently covering 60% of listings with over 65% overall accuracy.

Data Analysise-commercemachine learning
0 likes · 6 min read
Pricing Guidance System for Xianyu Secondhand Marketplace
21CTO
21CTO
Feb 22, 2021 · Artificial Intelligence

How to Strengthen an Algorithm Engineer’s Real‑World Impact: Tech, Business, and Soft Skills

The article outlines a three‑dimensional framework—technical, business, and soft‑skill competencies—that algorithm engineers need to master in order to successfully deliver machine‑learning solutions in production environments, offering practical advice on data handling, model evaluation, stakeholder communication, and personal development.

Business Analysisdata engineeringmachine learning
0 likes · 15 min read
How to Strengthen an Algorithm Engineer’s Real‑World Impact: Tech, Business, and Soft Skills
Taobao Frontend Technology
Taobao Frontend Technology
Feb 22, 2021 · Artificial Intelligence

How Pipcook Bridges Front‑End Development and Machine Learning with AI

This article introduces Pipcook, a machine‑learning framework designed for front‑end developers, explains its architecture and integration with TensorFlow.js, Boa, and Node.js, and discusses how it lowers the barrier to building intelligent front‑end applications through pipelines, plugins, and cloud‑native deployment.

AINode.jsPipcook
0 likes · 24 min read
How Pipcook Bridges Front‑End Development and Machine Learning with AI
DataFunTalk
DataFunTalk
Feb 21, 2021 · Artificial Intelligence

Advances in Pre‑Ranking for Large‑Scale Advertising: The COLD Framework and Its Technical Evolution

This article reviews the development history, technical routes, and recent breakthroughs of pre‑ranking (coarse ranking) in large‑scale advertising systems, focusing on Alibaba's COLD (Computing‑power‑cost‑aware Online and Lightweight Deep) framework, its model design, engineering optimizations, experimental results, and future research directions.

AdvertisingCOLDSystem Optimization
0 likes · 20 min read
Advances in Pre‑Ranking for Large‑Scale Advertising: The COLD Framework and Its Technical Evolution
DevOps
DevOps
Feb 9, 2021 · Operations

Choosing Between DataOps, MLOps, and AIOps: A Guide for Data Teams

The article examines how data teams can select the appropriate Ops framework—DataOps, MLOps, or AIOps—by comparing their origins, principles, responsibilities, and tooling, and stresses that cultural principles outweigh technology choices for efficient delivery of data and machine‑learning products.

AIOpsDataOpsDevOps
0 likes · 12 min read
Choosing Between DataOps, MLOps, and AIOps: A Guide for Data Teams
Efficient Ops
Efficient Ops
Feb 7, 2021 · Artificial Intelligence

How NLP Transforms Big Data Operations: Real-World AIOps Case Studies

This article explores the intersection of natural language processing and operations, outlines common text‑handling challenges, and presents three concrete AIOps case studies—log Q&A, anomaly detection, and ticket recommendation—while reflecting on a closed‑loop AI workflow and future research directions.

AIOpsNLPbig data
0 likes · 9 min read
How NLP Transforms Big Data Operations: Real-World AIOps Case Studies
DataFunSummit
DataFunSummit
Feb 7, 2021 · Artificial Intelligence

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

This article explains how Fliggy combines interactive recommendation with travel‑theme recommendation, detailing the underlying algorithms, user‑demand classification, real‑time interest capture, recall strategies, multi‑task learning for CTR prediction, and engineering tricks that improve personalization and click‑through rates.

AlibabaFliggyinteractive recommendation
0 likes · 17 min read
Interactive Recommendation and Travel Theme Recommendation in the Fliggy App
Alibaba Terminal Technology
Alibaba Terminal Technology
Feb 3, 2021 · Frontend Development

How Front-End AI Inference Engines Achieve Real-Time Smart Recognition

This article explains on‑device machine learning concepts, compares front‑end inference engines such as TensorFlow.js, ONNX.js and WebDNN across CPU, WASM and WebGL, and presents practical optimization techniques like vectorization, memory layout, graph fusion and mixed‑precision to boost performance for real‑time applications.

Inference Enginefrontendmachine learning
0 likes · 11 min read
How Front-End AI Inference Engines Achieve Real-Time Smart Recognition
DataFunSummit
DataFunSummit
Feb 2, 2021 · Artificial Intelligence

A Comprehensive Overview of Common CTR Prediction Models and Their Evolution

This article systematically reviews the evolution of click‑through‑rate (CTR) prediction models—from early distributed linear models like logistic regression, through automated feature engineering with GBDT+LR, various factorization‑machine variants, embedding‑MLP shallow modifications, dual‑tower combinations, and advanced explicit feature‑cross networks—highlighting each model’s structure, advantages, limitations, and comparative insights.

CTR predictionclick-through ratefactorization machines
0 likes · 28 min read
A Comprehensive Overview of Common CTR Prediction Models and Their Evolution
Architects' Tech Alliance
Architects' Tech Alliance
Jan 29, 2021 · Artificial Intelligence

Comprehensive Overview of Machine Learning: Types, Industry Chain, and Key Technologies

This article provides a detailed introduction to machine learning, covering its definition, learning modes such as supervised, unsupervised and reinforcement learning, shallow versus deep learning, the full industry chain from AI chips to cloud and big‑data services, and the major open‑source frameworks and platforms driving the field.

AI chipsUnsupervised Learningbig data
0 likes · 11 min read
Comprehensive Overview of Machine Learning: Types, Industry Chain, and Key Technologies
DataFunTalk
DataFunTalk
Jan 29, 2021 · Artificial Intelligence

Content Embedding Practices and Challenges at Hulu

This article presents Hulu's multi‑layered approach to content understanding and embedding, describing tag‑based graph embeddings, metadata‑BERT enhancements, multimodal video/audio feature aggregation, and various applications such as similarity search, ranking, cold‑start retrieval, and collection modeling, while also discussing current limitations and open research questions.

Hulucontent embeddinggraph embeddings
0 likes · 12 min read
Content Embedding Practices and Challenges at Hulu
MaGe Linux Operations
MaGe Linux Operations
Jan 28, 2021 · Fundamentals

Unlock the Power of NumPy: Visual Guide to Arrays and Operations

This article provides a visual, step‑by‑step introduction to NumPy’s core concepts—vectors, matrices, higher‑dimensional arrays, creation, indexing, arithmetic, broadcasting, and common functions—helping developers and researchers understand how the library works and apply it efficiently in Python data‑science workflows.

Array OperationsData ScienceNumPy
0 likes · 18 min read
Unlock the Power of NumPy: Visual Guide to Arrays and Operations
Meituan Technology Team
Meituan Technology Team
Jan 28, 2021 · Artificial Intelligence

Trajectory Prediction Algorithm for Autonomous Vehicles: Winning Solutions in NeurIPS 2020 INTERPRET Challenge

Meituan’s unmanned delivery team secured first place in the Generalizability track and second in the Regular track of the NeurIPS 2020 INTERPRET trajectory‑prediction challenge by employing a mixed‑attention graph‑transformer with dual‑channel GRU and adaptive map processing, achieving ADEs of 0.5339 m and 0.1912 m respectively.

Autonomous VehiclesGraph Neural NetworkNeurIPS
0 likes · 15 min read
Trajectory Prediction Algorithm for Autonomous Vehicles: Winning Solutions in NeurIPS 2020 INTERPRET Challenge
DataFunTalk
DataFunTalk
Jan 23, 2021 · Artificial Intelligence

Feature Engineering: Mapping Raw Data to Machine‑Learning Features and Best Practices

This article explains how feature engineering transforms raw data into numerical representations for machine‑learning models, covering mapping of numeric and categorical values, one‑hot and multi‑hot encoding, sparse representations, scaling, handling outliers, binning, data quality checks, and feature interactions to capture non‑linear relationships.

data preprocessingencodingfeature engineering
0 likes · 20 min read
Feature Engineering: Mapping Raw Data to Machine‑Learning Features and Best Practices
58 Tech
58 Tech
Jan 22, 2021 · Artificial Intelligence

AI + CRM: Improving Enterprise Performance and Efficiency

This article describes how 58.com’s AI Lab integrated machine‑learning and recommendation techniques into its CRM system, redesigning sales workflows, introducing the “Michigan” model, and deploying XGBoost and MMoE models to boost key metrics such as transfer rate and 60‑second effective call rate, achieving significant performance gains.

AICRMmachine learning
0 likes · 20 min read
AI + CRM: Improving Enterprise Performance and Efficiency
21CTO
21CTO
Jan 20, 2021 · Databases

Why Time Series Databases Are the Future of Your Data

Time series databases let you retain full historical records, enabling analysis, visualization, machine learning and automation across domains like finance, weather and IoT, and the article explains why they’re essential, how they differ from traditional databases, and how to start using them.

Time Seriesdatabasesmachine learning
0 likes · 7 min read
Why Time Series Databases Are the Future of Your Data
DataFunTalk
DataFunTalk
Jan 18, 2021 · Artificial Intelligence

Graph Algorithm Design and Optimization for Detecting Black Market Users in Virtual Networks

This article presents a comprehensive overview of using graph representation learning and clustering, particularly GraphSAGE and its optimizations, to identify and mitigate black‑market (malicious) accounts in virtual networks, discussing background, objectives, challenges such as isolation and heterogeneity, and evaluation results.

Graph AlgorithmsGraphSAGEblack market detection
0 likes · 13 min read
Graph Algorithm Design and Optimization for Detecting Black Market Users in Virtual Networks
MaGe Linux Operations
MaGe Linux Operations
Jan 17, 2021 · Artificial Intelligence

Top 10 Must‑Know Python Libraries of 2020 (Plus Bonus Picks)

This article presents the 2020 Python library ranking, explaining the selection criteria and highlighting ten standout libraries—ranging from CLI tools like Typer and Rich to AI‑focused frameworks such as Hydra, PyTorch Lightning, Hummingbird, and HiPlot—plus several honorable mentions.

AICLIData Science
0 likes · 13 min read
Top 10 Must‑Know Python Libraries of 2020 (Plus Bonus Picks)
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Jan 15, 2021 · Artificial Intelligence

Recommendation System Architecture and Engineering Overview

This article presents a comprehensive overview of a recommendation system, covering its business background, purpose, detailed engineering architecture—including data sources, computation, storage, online learning, service and access layers—and discusses key challenges, module design, and practical reflections.

AB TestingTensorFlowdata engineering
0 likes · 14 min read
Recommendation System Architecture and Engineering Overview
DataFunTalk
DataFunTalk
Jan 15, 2021 · Artificial Intelligence

Zhihu Search Text Relevance Evolution and BERT Knowledge Distillation Practices

This talk by Zhihu search algorithm engineer Shen Zhan details the evolution of text relevance models from TF‑IDF/BM25 to deep semantic matching and BERT, explains the challenges of deploying BERT at scale, and describes practical knowledge‑distillation techniques that improve both online latency and offline storage while maintaining search quality.

BERTknowledge distillationmachine learning
0 likes · 14 min read
Zhihu Search Text Relevance Evolution and BERT Knowledge Distillation Practices
21CTO
21CTO
Jan 11, 2021 · Artificial Intelligence

How to Build a Recommendation System from Scratch: Key Concepts and Strategies

This article explains the fundamentals of recommendation systems, covering data collection, user and content profiling, system architecture, algorithmic pipelines such as recall, filtering, ranking, and evaluation metrics, while also discussing practical challenges like echo chambers and long‑term user value.

algorithmevaluationmachine learning
0 likes · 16 min read
How to Build a Recommendation System from Scratch: Key Concepts and Strategies
DataFunTalk
DataFunTalk
Jan 8, 2021 · Artificial Intelligence

Deconstructing E‑commerce Recommendation Systems: Architecture, Challenges, and Strategies

This article provides a comprehensive overview of e‑commerce recommendation systems, detailing their end‑to‑end workflow, key challenges such as multi‑scenario objectives and data loops, core components like recall and ranking, model evolution, feature engineering, evaluation metrics, and practical considerations for building a healthy, multi‑objective recommendation ecosystem.

e-commercemachine learningpersonalization
0 likes · 17 min read
Deconstructing E‑commerce Recommendation Systems: Architecture, Challenges, and Strategies
DataFunTalk
DataFunTalk
Jan 7, 2021 · Artificial Intelligence

User Preference Mining and Modeling Practices at Beike

This article introduces the concept of user preference mining, discusses challenges such as accurate expression, interpretability, and high-dimensional preferences, reviews statistical and model-based approaches including weighting, decay, XGBoost, DNN, LSTM, Seq4Rec, and Deep Interest Network, and describes their practical implementation at Beike.

BeikeEmbeddingLSTM
0 likes · 19 min read
User Preference Mining and Modeling Practices at Beike
Architects Research Society
Architects Research Society
Jan 6, 2021 · Artificial Intelligence

DVC: Data Version Control for Machine Learning Projects

DVC is an open‑source data version control system that extends Git to manage large machine‑learning models, datasets, and pipelines, enabling reproducible experiments, low‑friction branching, metric tracking, and seamless collaboration across various storage backends.

DVCML Pipelinesmachine learning
0 likes · 9 min read
DVC: Data Version Control for Machine Learning Projects
DataFunTalk
DataFunTalk
Jan 3, 2021 · Artificial Intelligence

iQIYI Machine Learning Platform: Development History, Features, and Practical Experience

This article details the evolution of iQIYI's machine learning platform—from its early Javis‑based deep‑learning system to three major versions that introduced visual workflow, distributed scheduling, auto‑tuning, large‑scale training support, model management, and online prediction—while sharing practical lessons and a real anti‑cheat use case.

Platformbig datahyperparameter tuning
0 likes · 13 min read
iQIYI Machine Learning Platform: Development History, Features, and Practical Experience
DataFunSummit
DataFunSummit
Dec 29, 2020 · Artificial Intelligence

Graph Neural Networks for Recommendation: Principles, Frameworks, and Tencent Practice

This article introduces graph neural networks, explains their fundamentals and GraphSAGE/DGI algorithms, and demonstrates how Tencent applies them to recommendation scenarios such as video and WeChat content, highlighting network construction, feature engineering, sampling and aggregation techniques, and practical performance gains.

DGIGraphSAGETencent
0 likes · 8 min read
Graph Neural Networks for Recommendation: Principles, Frameworks, and Tencent Practice
Amap Tech
Amap Tech
Dec 24, 2020 · Artificial Intelligence

Advancing Mobile Navigation Accuracy: Lessons from the IPIN2020 Competition and VDR Technology

The Wuhan‑Amap team won IPIN2020’s vehicle‑navigation track by using big‑data mining and neural‑network‑enhanced Vehicle‑Dead Reckoning to fuse smartphone GNSS, IMU, and barometer data, overcoming GPS outages and sensor limitations, and demonstrating that machine‑learning‑driven inertial navigation can achieve vehicle‑grade accuracy on consumer phones.

Indoor positioningVDRmachine learning
0 likes · 8 min read
Advancing Mobile Navigation Accuracy: Lessons from the IPIN2020 Competition and VDR Technology
JD Tech Talk
JD Tech Talk
Dec 18, 2020 · Artificial Intelligence

Model Online Inference System: Architecture, Components, and Deployment Strategies

This article examines the challenges of moving machine‑learning models from offline training to online serving, proposes a modular architecture—including model gateway, data source gateway, business service center, monitoring, and RPC components—to enable rapid model deployment, version management, traffic mirroring, gray‑release, and real‑time monitoring.

machine learningmodel servingmonitoring
0 likes · 10 min read
Model Online Inference System: Architecture, Components, and Deployment Strategies
DataFunTalk
DataFunTalk
Dec 18, 2020 · Artificial Intelligence

Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions

This article explains the evolution of federated learning, contrasts Google’s cross‑device horizontal approach with China’s cross‑silo vertical implementations, analyzes their security vulnerabilities, and demonstrates how secure multi‑party computation—including differential privacy, secure aggregation, and secret‑sharing techniques—can address these challenges while highlighting performance trade‑offs.

Differential PrivacySecure Aggregationcross-silo
0 likes · 18 min read
Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions
TAL Education Technology
TAL Education Technology
Dec 17, 2020 · Artificial Intelligence

Web Front‑End Intelligent Computing: Concepts, Implementation, and Applications

This article explains how AI technologies are transitioning from labs to the web, covering neural network fundamentals, the distinction between cloud and edge intelligence, implementation pipelines, offline model optimization, online inference backends like WebGL and WASM, and practical web front‑end AI use cases.

Web AIfrontendmachine learning
0 likes · 10 min read
Web Front‑End Intelligent Computing: Concepts, Implementation, and Applications
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 16, 2020 · Artificial Intelligence

How to Detect and Prevent Advertising Fraud with Advanced AI Techniques

This article explains the scale of online ad fraud, outlines common advertising billing models, describes how fake traffic generates revenue, defines invalid clicks, and presents a comprehensive anti‑fraud system that combines rule‑based methods, feature engineering, and AI models such as TextCNN, BiLSTM, BERT, Wide&Deep and GraphSage to identify and block fraudulent ad clicks.

AIAd FraudAdvertising
0 likes · 33 min read
How to Detect and Prevent Advertising Fraud with Advanced AI Techniques
Python Programming Learning Circle
Python Programming Learning Circle
Dec 16, 2020 · Artificial Intelligence

Linear Regression Theory and Python Implementation with Iris and Boston Datasets

This article explains the fundamentals of linear regression, including regression formulas, loss functions, and error metrics, and provides complete Python code using scikit‑learn to perform both simple and multiple linear regression on the Iris and Boston housing datasets, along with model evaluation and visualization.

Data ScienceLinear RegressionPython
0 likes · 7 min read
Linear Regression Theory and Python Implementation with Iris and Boston Datasets