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Tencent Advertising Technology
Tencent Advertising Technology
Jun 4, 2018 · Artificial Intelligence

Tencent Advertising Algorithm Competition: FFM Approach and Feature Engineering by the Wenqiang Ge Team

The Wenqiang Ge team, winners of the first week of the Tencent Advertising Algorithm Competition rematch, detail their FFM-based solution, including baseline adoption, feature engineering with discretized continuous values, cross‑feature handling, and tool choices such as Feather storage and the xlearn library for fast training.

Ensemble ModelingFFMFeather
0 likes · 4 min read
Tencent Advertising Algorithm Competition: FFM Approach and Feature Engineering by the Wenqiang Ge Team
ITPUB
ITPUB
Jun 3, 2018 · Big Data

Spark vs Hadoop: Which Distributed System Fits Your Data Needs?

An in‑depth comparison of Hadoop and Spark examines their architectures, performance, cost, security, and machine‑learning capabilities, helping readers decide which open‑source distributed processing platform best matches their batch, streaming, and analytical workloads.

Big DataCostHadoop
0 likes · 13 min read
Spark vs Hadoop: Which Distributed System Fits Your Data Needs?
MaGe Linux Operations
MaGe Linux Operations
May 30, 2018 · Artificial Intelligence

Master Python Speech Recognition: From Basics to Real-World Audio Transcription

This comprehensive guide walks you through the fundamentals of speech recognition, explains how Python’s SpeechRecognition library works, shows how to install and use various recognizer packages, process audio files and microphone input, handle noise, and troubleshoot common errors with clear code examples.

Audio ProcessingSpeechRecognitionVoice Transcription
0 likes · 18 min read
Master Python Speech Recognition: From Basics to Real-World Audio Transcription
21CTO
21CTO
May 28, 2018 · Artificial Intelligence

How to Ace AI Company Interviews: Proven Strategies and Resources

This guide shares practical advice from multiple AI interview experiences, covering how to build a standout profile, a curated list of target companies, interview techniques, motivation for meaningful work, and essential computer science, math, and machine‑learning fundamentals to help graduates secure AI roles.

AI InterviewData Sciencecareer advice
0 likes · 18 min read
How to Ace AI Company Interviews: Proven Strategies and Resources
Meitu Technology
Meitu Technology
May 23, 2018 · Artificial Intelligence

Machine Learning and Optimization Problems: Applications and Exploration

Meitu Technology’s technical salon on June 9, 2018 in Xiamen showcased how its AI‑driven deep ranking, video‑clustering, and data‑structure‑based optimization techniques improve personalization, recommendation and economic‑focused problem solving for billions of mobile users, targeting mid‑senior R&D and algorithm engineers.

Data StructuresRecommendation Systemsmachine learning
0 likes · 6 min read
Machine Learning and Optimization Problems: Applications and Exploration
AntTech
AntTech
May 22, 2018 · Artificial Intelligence

Unpack Local Model Interpretation for GBDT – Summary and Analysis

This article summarizes the Ant Financial paper presented at DASFAA 2018 that proposes a universal local explanation method for Gradient Boosting Decision Tree models, detailing the problem definition, the PMML‑based algorithm for attributing feature contributions, experimental validation on fraud detection data, and the practical benefits for model transparency and improvement.

GBDTModel InterpretationPMML
0 likes · 12 min read
Unpack Local Model Interpretation for GBDT – Summary and Analysis
UCloud Tech
UCloud Tech
May 21, 2018 · Artificial Intelligence

How Cloud Computing Accelerates AI Adoption: Insights from Think in Cloud 2018

The Think in Cloud 2018 conference in Beijing showcased how cloud platforms enable rapid AI deployment across sectors such as autonomous driving, intelligent customer service, and education, highlighting challenges, platform‑centric solutions, and real‑world case studies that illustrate the growing synergy between cloud computing and artificial intelligence.

AIAI applicationsEdge Computing
0 likes · 13 min read
How Cloud Computing Accelerates AI Adoption: Insights from Think in Cloud 2018
21CTO
21CTO
May 20, 2018 · Artificial Intelligence

Why Causal Reasoning Is the Missing Piece for Truly Intelligent AI

Judea Pearl, the 2011 Turing Award laureate, argues that modern AI is stuck in curve‑fitting and that true intelligence requires machines to understand cause and effect, a perspective he expands on through a series of insightful interview questions and answers.

AIDeep LearningJudea Pearl
0 likes · 11 min read
Why Causal Reasoning Is the Missing Piece for Truly Intelligent AI
UCloud Tech
UCloud Tech
May 15, 2018 · Artificial Intelligence

Cloud‑Powered AI & Blockchain: Key Takeaways from UCloud TIC 2018

The 2018 UCloud Think in Cloud conference showcased how cloud infrastructure accelerates AI applications in customer service, education, and embedded devices, while also presenting blockchain use cases and security challenges across finance, insurance, and IoT, highlighting practical solutions and emerging platforms.

AIUCloudcloud computing
0 likes · 10 min read
Cloud‑Powered AI & Blockchain: Key Takeaways from UCloud TIC 2018
Tencent Advertising Technology
Tencent Advertising Technology
May 14, 2018 · Artificial Intelligence

Tencent Advertising Algorithm Competition Weekly Champion Shares Data Processing, Feature Engineering, Model Training, and Optimization Strategies

The Nanjing University team '每天队员都想改一次名字' shares their winning approach in Tencent Advertising Algorithm Competition, covering data processing, feature engineering, model training techniques, and alternative optimization targets for AUC improvement, and discuss lessons learned from previous year's champion experience.

AUC optimizationModel TrainingTencent Advertising
0 likes · 5 min read
Tencent Advertising Algorithm Competition Weekly Champion Shares Data Processing, Feature Engineering, Model Training, and Optimization Strategies
360 Quality & Efficiency
360 Quality & Efficiency
May 11, 2018 · Artificial Intelligence

Common Engineering Algorithms and Their Testing Methods

This article introduces the most commonly used algorithms in engineering—recommendation, optimization, estimation, and classification—explains their typical application scenarios, and discusses various testing methods and evaluation metrics such as offline experiments, user surveys, A/B testing, and performance indicators like accuracy, coverage, diversity, and robustness.

algorithmevaluationmachine learning
0 likes · 12 min read
Common Engineering Algorithms and Their Testing Methods
Xianyu Technology
Xianyu Technology
May 10, 2018 · Artificial Intelligence

Mercari Price Prediction Using TFIDF, GRU, and Ensemble Models

By converting Mercari’s product titles, descriptions, and categorical data into TF‑IDF vectors and embeddings, training MLP and GRU networks, and ensembling them with weighted averaging, the authors achieve a 0.3873 RMSLE—matching the competition’s top score—and demonstrate the power of text‑only price prediction for C2C marketplaces.

GRUTFIDFensemble
0 likes · 8 min read
Mercari Price Prediction Using TFIDF, GRU, and Ensemble Models
Tencent Advertising Technology
Tencent Advertising Technology
May 7, 2018 · Artificial Intelligence

Choosing Mainstream CTR Models: LightGBM, FFM, and Deep Learning Approaches

The author, a graduate student and weekly champion of the Tencent advertising algorithm contest, shares practical guidance on selecting mainstream CTR models—including LightGBM, field‑aware factorization machines, and deep learning approaches—while offering tips on feature handling, hyper‑parameter settings, and resource‑efficient implementation.

CTRFFMLightGBM
0 likes · 5 min read
Choosing Mainstream CTR Models: LightGBM, FFM, and Deep Learning Approaches
Alibaba Cloud Developer
Alibaba Cloud Developer
May 7, 2018 · Artificial Intelligence

How Active PU Learning Boosts Cash‑Out Fraud Detection by 3×

This article presents an Active PU Learning framework that combines active learning with two‑step PU semi‑supervised learning to improve cash‑out fraud detection, reducing labeling costs, enhancing model performance, and achieving a three‑fold increase in identified fraudulent transactions compared to traditional unsupervised methods.

AIRisk DetectionSemi-supervised Learning
0 likes · 15 min read
How Active PU Learning Boosts Cash‑Out Fraud Detection by 3×
Efficient Ops
Efficient Ops
Apr 26, 2018 · Operations

How 360 Detects Network Anomalies with AI‑Powered Time‑Series Algorithms

This article explains how 360’s network operations team uses time‑series analysis, statistical thresholds, EWMA, dynamic limits, and machine‑learning models such as K‑Means and Isolation Forest to automatically detect, locate, and remediate traffic anomalies across massive data‑center exits.

AI OpsNetwork MonitoringTime Series
0 likes · 15 min read
How 360 Detects Network Anomalies with AI‑Powered Time‑Series Algorithms
Qunar Tech Salon
Qunar Tech Salon
Apr 26, 2018 · Artificial Intelligence

Understanding gcForest: Cascade Forest Structure and Multi‑grained Scanning for Representation Learning

The article explains how gcForest, an ensemble‑of‑decision‑tree model that mimics deep neural network hierarchies, uses cascade forests and multi‑grained sliding‑window scanning to achieve effective representation learning with fewer hyper‑parameters, especially on small datasets.

cascade forestensemble methodsgcForest
0 likes · 11 min read
Understanding gcForest: Cascade Forest Structure and Multi‑grained Scanning for Representation Learning
AntTech
AntTech
Apr 24, 2018 · Artificial Intelligence

Anomaly Detection with Partially Observed Anomalies: A Two‑Stage Semi‑Supervised Approach

This article summarizes a two‑stage method for anomaly detection when only a few labeled anomalies and many unlabeled instances are available, detailing problem formulation, isolation‑forest‑based scoring, clustering of anomalies, weighted multiclass modeling, experimental validation, and real‑world URL attack applications.

Pu-LearningSemi-supervised Learninganomaly detection
0 likes · 10 min read
Anomaly Detection with Partially Observed Anomalies: A Two‑Stage Semi‑Supervised Approach
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 23, 2018 · Fundamentals

Top Technical Books Recommended by Alibaba Experts for World Book Day

On World Book Day, nine Alibaba technology veterans share a curated list of essential technical books—covering software testing, design patterns, AI, machine learning, reinforcement learning, Rust, and database architecture—offering concise reasons why each title is valuable for developers and engineers.

Database ArchitectureDesign PatternsRust programming
0 likes · 10 min read
Top Technical Books Recommended by Alibaba Experts for World Book Day
Java Backend Technology
Java Backend Technology
Apr 20, 2018 · Artificial Intelligence

How Do Modern Recommendation Systems Balance Accuracy, Diversity, and Surprise?

This article explains the objectives, methods, architecture, and key algorithms of modern recommendation systems, covering popular, manual, related, and personalized approaches, the data pipeline, real‑time challenges, cold‑start handling, diversity, content quality, and exploration‑exploitation strategies.

Real-time ProcessingRecommendation Systemscollaborative filtering
0 likes · 15 min read
How Do Modern Recommendation Systems Balance Accuracy, Diversity, and Surprise?
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Apr 19, 2018 · Information Security

How Suning Built a Comprehensive Information Security Architecture

This article outlines Suning's evolution from a basic network operations unit to a sophisticated, multi‑layered security architecture that integrates organizational structure, protection platforms, risk management, big‑data threat perception, and continuous improvement to safeguard e‑commerce operations.

Big DataSecurity Architectureinformation security
0 likes · 10 min read
How Suning Built a Comprehensive Information Security Architecture
DataFunTalk
DataFunTalk
Apr 18, 2018 · Artificial Intelligence

Introduction to Search Engine Algorithm Systems: Ranking and Intent Recognition

This article provides a comprehensive overview of search engine algorithm systems, tracing their evolution from simple Bayesian and SVM models to modern deep learning approaches, and detailing the architecture, query analysis, ranking methods, click models, and recent advances such as reinforcement learning and adversarial networks.

AILTRclick models
0 likes · 13 min read
Introduction to Search Engine Algorithm Systems: Ranking and Intent Recognition
Efficient Ops
Efficient Ops
Apr 17, 2018 · Artificial Intelligence

From Math to ML: My Path Through Recommendation, Security, and AIOps

This article chronicles the author’s transition from a mathematics background to machine learning, detailing early challenges, hands‑on projects in recommendation systems, security, and AIOps, and sharing practical insights on feature engineering, model evaluation, and large‑scale anomaly detection.

Recommendation Systemsaiopsanomaly detection
0 likes · 17 min read
From Math to ML: My Path Through Recommendation, Security, and AIOps
AntTech
AntTech
Apr 16, 2018 · Artificial Intelligence

Active PU Learning for Cash‑Out Fraud Detection in Alipay’s AlphaRisk Engine

This article presents an Active PU Learning framework that combines active learning with two‑step positive‑unlabeled learning to improve cash‑out fraud detection in Alipay’s fifth‑generation risk engine, AlphaRisk, achieving three‑fold identification gains over unsupervised methods while reducing labeling costs.

Semi-supervised Learningactive learningfraud detection
0 likes · 14 min read
Active PU Learning for Cash‑Out Fraud Detection in Alipay’s AlphaRisk Engine
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 16, 2018 · Artificial Intelligence

How Alibaba’s Deep Learning Transformed CTR Prediction: From MLR to Multi‑Interest Networks

This article recounts Alibaba‑Mama researcher Jing Shi’s presentation on the evolution of deep learning for click‑through‑rate (CTR) estimation, covering the shift from handcrafted features and linear models to piecewise linear MLR, end‑to‑end neural networks, multi‑interest user modeling, and large‑scale distributed training challenges.

AdvertisingCTR predictionDeep Learning
0 likes · 16 min read
How Alibaba’s Deep Learning Transformed CTR Prediction: From MLR to Multi‑Interest Networks
Tencent Cloud Developer
Tencent Cloud Developer
Apr 10, 2018 · Artificial Intelligence

Emerging AI Topics: Hallucinations Linked to Serotonin and AI Applications for Crime Prevention

Scientists suggest that mimicking the brain’s serotonin‑driven neuromodulation could give AI systems human‑like reasoning and emotional processing, while researchers at USC’s AI Lab are applying advanced machine‑learning patrol‑randomization and risk‑assessment algorithms, such as the ARMOR platform, to predict and prevent crimes like poaching.

AI ethicsCrime Preventionartificial intelligence
0 likes · 6 min read
Emerging AI Topics: Hallucinations Linked to Serotonin and AI Applications for Crime Prevention
21CTO
21CTO
Apr 9, 2018 · Artificial Intelligence

How E‑Commerce Platforms Build Effective Product Recommendation Systems

This article explains the fundamentals and advanced techniques of e‑commerce product recommendation systems, covering conventional and personalized approaches, user profiling, data collection, storage, modeling, the three‑stage pipeline of preprocessing, recall and ranking, as well as system architecture, challenges, and key algorithms such as LR and GBDT.

data pipelinee‑commercemachine learning
0 likes · 17 min read
How E‑Commerce Platforms Build Effective Product Recommendation Systems
AntTech
AntTech
Apr 9, 2018 · Artificial Intelligence

Practical Guide to Modeling Stability: Feature PSI, Model PSI, and Monitoring Techniques

This article explains the importance of modeling stability, describes how to assess feature and model stability using the Population Stability Index (PSI), provides step‑by‑step calculation methods, and shares practical monitoring practices such as rank mapping and daily SQL‑based checks.

Model MonitoringModelingPSI
0 likes · 9 min read
Practical Guide to Modeling Stability: Feature PSI, Model PSI, and Monitoring Techniques
MaGe Linux Operations
MaGe Linux Operations
Apr 8, 2018 · Artificial Intelligence

Master Python Data Mining & Machine Learning: From Preprocessing to Classification

This comprehensive tutorial walks you through Python data mining and machine learning fundamentals, covering data preprocessing techniques, common classification algorithms, an Iris flower classification case study, and practical tips for selecting the right algorithm, all illustrated with clear code examples and visualizations.

Classification AlgorithmsNaive BayesPython
0 likes · 22 min read
Master Python Data Mining & Machine Learning: From Preprocessing to Classification
Meituan Technology Team
Meituan Technology Team
Mar 29, 2018 · Artificial Intelligence

AI-Powered Smart Assistant for Meituan Delivery Riders

Meituan’s AI‑powered Rider Smart Assistant uses voice‑based interaction, real‑time routing, ETA prediction and massive GPS data to solve NP‑hard dispatch problems, cut manual phone calls, shorten order‑acceptance latency and rider wait times, and deliver safer, faster, more efficient same‑city logistics for riders and customers.

AILogisticsVoice Assistant
0 likes · 22 min read
AI-Powered Smart Assistant for Meituan Delivery Riders
MaGe Linux Operations
MaGe Linux Operations
Mar 29, 2018 · Artificial Intelligence

Master Python’s Top Data Analysis & AI Libraries with Hands‑On Code

This article introduces Python’s essential features for data analysis and mining, then reviews the most widely used libraries—NumPy, SciPy, Matplotlib, Pandas, Scikit‑Learn, Keras, and Gensim—each accompanied by concise code examples that demonstrate their core capabilities.

KerasPythondata analysis
0 likes · 14 min read
Master Python’s Top Data Analysis & AI Libraries with Hands‑On Code
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 28, 2018 · Artificial Intelligence

How Tree‑Based Deep Match Revolutionizes Large‑Scale Recommendation Systems

This article introduces the Tree‑based Deep Match (TDM) framework, which uses a novel max‑heap tree structure to enable efficient, hierarchical retrieval over massive candidate sets, allowing any advanced deep learning model to improve matching accuracy, recall, and novelty in industrial recommendation systems.

Deep Learninglarge-scale recommendationmachine learning
0 likes · 27 min read
How Tree‑Based Deep Match Revolutionizes Large‑Scale Recommendation Systems
21CTO
21CTO
Mar 21, 2018 · Artificial Intelligence

What Jiang Zemin Predicted About AI and SaaS in 2008 – A 10‑Year Retrospective

In this article the author revisits Jiang Zemin’s 2008 paper on China’s information‑technology industry, highlighting his early predictions about machine learning, GPU research and SaaS, and reflecting on how those insights have proved prescient a decade later.

AI predictionsGPU researchIT industry
0 likes · 4 min read
What Jiang Zemin Predicted About AI and SaaS in 2008 – A 10‑Year Retrospective
Architects' Tech Alliance
Architects' Tech Alliance
Mar 16, 2018 · Operations

How Machine Learning Powers Intelligent Operations: Real‑World Baidu Case Studies

This article examines Baidu's practical applications of machine‑learning‑driven intelligent operations, detailing three real‑world scenarios, the challenges of KPI anomaly labeling, the design of an automated detection framework, evaluation results across multiple datasets, and broader insights for scaling AIOps in production environments.

BaiduCase StudyOperations Automation
0 likes · 16 min read
How Machine Learning Powers Intelligent Operations: Real‑World Baidu Case Studies
Architecture Digest
Architecture Digest
Mar 16, 2018 · Artificial Intelligence

Essential Cheat Sheets for Machine Learning and Deep Learning Researchers

This article introduces a GitHub repository that compiles comprehensive cheat sheets covering key Python libraries such as Keras, NumPy, Pandas, SciPy, Matplotlib, Scikit-learn, and others, providing quick reference resources to help beginners and researchers efficiently navigate machine learning and deep learning workflows.

AIDeep LearningPython
0 likes · 5 min read
Essential Cheat Sheets for Machine Learning and Deep Learning Researchers
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 15, 2018 · Artificial Intelligence

How Deep Learning Transforms Knowledge Graph Relation Extraction

This article reviews the evolution from rule‑based DeepDive methods to deep‑learning approaches such as PCNNs and attention‑enhanced models for relation extraction, presents experimental results on the NYT dataset, discusses practical challenges in large‑scale deployment, and outlines future research directions.

Attention MechanismDeep LearningKnowledge Graph
0 likes · 14 min read
How Deep Learning Transforms Knowledge Graph Relation Extraction
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 14, 2018 · Artificial Intelligence

DeepDive Powers Knowledge Graph Relation Extraction for Shenma Search

This article explains how Alibaba’s Shenma Search team builds and refines a large‑scale knowledge graph using open information extraction, detailing relation‑extraction techniques, distant supervision challenges, and the DeepDive system’s architecture, custom Chinese NLP pipeline, iterative improvements, and empirical results across millions of triples.

DeepDiveKnowledge Graphdistant supervision
0 likes · 28 min read
DeepDive Powers Knowledge Graph Relation Extraction for Shenma Search
Tencent Cloud Developer
Tencent Cloud Developer
Mar 13, 2018 · Artificial Intelligence

TensorFlow MNIST Tutorial: Environment Setup, Softmax Regression, and CNN Implementation

This beginner‑friendly TensorFlow tutorial by Chen Yidong walks readers through Windows environment setup, explains TensorFlow’s graph‑execution model, and demonstrates both softmax linear regression and a deep convolutional neural network for MNIST, while also covering utility scripts, TensorBoard visualization, and CPU/GPU or multi‑GPU deployment.

CNNGPUMNIST
0 likes · 13 min read
TensorFlow MNIST Tutorial: Environment Setup, Softmax Regression, and CNN Implementation
Architects' Tech Alliance
Architects' Tech Alliance
Mar 9, 2018 · Artificial Intelligence

Master Machine Learning Basics: From PCA to KNN Explained with Visual Demos

An in‑depth, visual guide walks readers through the fundamentals of machine learning—distinguishing supervised from unsupervised approaches, explaining dimensionality reduction with PCA, detailing clustering techniques such as hierarchical clustering, K‑Means and DBSCAN, and summarizing core regression and classification algorithms including linear regression, SVM, decision trees, logistic regression, Naïve Bayes, and KNN.

Unsupervised Learningclassificationclustering
0 likes · 11 min read
Master Machine Learning Basics: From PCA to KNN Explained with Visual Demos
Hulu Beijing
Hulu Beijing
Mar 6, 2018 · Artificial Intelligence

Understanding WGANs: From GAN Pitfalls to Wasserstein Solutions

This article explains the shortcomings of traditional GANs, introduces the Wasserstein GAN (WGAN) as a remedy using the Earth‑Mover distance, describes the theoretical motivations, outlines the algorithmic steps and constraints, and provides illustrative diagrams and references for deeper study.

Deep LearningGenerative Adversarial NetworksWGAN
0 likes · 11 min read
Understanding WGANs: From GAN Pitfalls to Wasserstein Solutions
Architecture Digest
Architecture Digest
Feb 24, 2018 · Artificial Intelligence

Eight Neural Network Architectures Every Machine Learning Researcher Should Know

This article explains why machine learning is essential for complex tasks, defines neural networks, outlines three reasons to study them, and provides concise overviews of eight fundamental neural network architectures—including perceptron, CNN, RNN, LSTM, Hopfield, Boltzmann machines, deep belief networks, and deep autoencoders—grouped by their structural categories.

AI architecturesCNNDeep Learning
0 likes · 23 min read
Eight Neural Network Architectures Every Machine Learning Researcher Should Know
Architecture Digest
Architecture Digest
Feb 14, 2018 · Artificial Intelligence

Comparative Analysis and Optimization of Machine Learning Models on the UCI Census Income Dataset

This article walks through a complete machine‑learning workflow on the UCI Census Income dataset, covering data exploration, preprocessing (including log‑transformation and scaling), model training with Naïve Bayes, Decision Tree and SVM, performance evaluation, hyper‑parameter tuning via grid search, feature importance analysis, and feature selection, providing code snippets and visualizations.

Model EvaluationPythondata preprocessing
0 likes · 24 min read
Comparative Analysis and Optimization of Machine Learning Models on the UCI Census Income Dataset
Architecture Digest
Architecture Digest
Feb 13, 2018 · Artificial Intelligence

Overview of Common Machine Learning Models: Characteristics, Advantages, and Disadvantages

This article provides a concise overview of fifteen widely used machine learning models—including decision trees, random forests, k‑means, KNN, EM, linear and logistic regression, Naive Bayes, Apriori, Boosting, GBDT, SVM, neural networks, HMM, and CRF—detailing their features, strengths, weaknesses, and typical application scenarios.

Neural Networksclassificationclustering
0 likes · 12 min read
Overview of Common Machine Learning Models: Characteristics, Advantages, and Disadvantages
Architecture Digest
Architecture Digest
Feb 11, 2018 · Artificial Intelligence

Recent Advances in Bayesian Machine Learning: Foundations, Non‑Parametric Methods, and Large‑Scale Applications

This article reviews recent progress in Bayesian machine learning, covering foundational theory, non‑parametric approaches such as Dirichlet and Indian buffet processes, regularized Bayesian inference, and scalable techniques for big‑data environments including stochastic variational methods, distributed algorithms, and hardware acceleration.

Big DataMonte CarloVariational Inference
0 likes · 23 min read
Recent Advances in Bayesian Machine Learning: Foundations, Non‑Parametric Methods, and Large‑Scale Applications
dbaplus Community
dbaplus Community
Feb 8, 2018 · Artificial Intelligence

Unlocking Data Value: A Practical Guide to Bayesian Theorem and Its Applications

This article explains the fundamentals of Bayes' theorem, shows how to compute prior, likelihood, and posterior probabilities, demonstrates Bayesian A/B testing with Python code, introduces Bayesian networks for causal inference, and discusses the role of Bayesian methods in machine learning and data‑driven decision making.

AB testingBayesianStatistical Modeling
0 likes · 11 min read
Unlocking Data Value: A Practical Guide to Bayesian Theorem and Its Applications
21CTO
21CTO
Feb 7, 2018 · Artificial Intelligence

Demystifying Entropy: From Basic Concepts to Cross‑Entropy and KL Divergence

This article explains entropy, joint entropy, conditional entropy, and related measures such as KL divergence and cross‑entropy, using intuitive coin‑flip examples and mathematical formulas to show how they quantify uncertainty and information in probability distributions.

KL divergencecross entropyentropy
0 likes · 14 min read
Demystifying Entropy: From Basic Concepts to Cross‑Entropy and KL Divergence
Efficient Ops
Efficient Ops
Feb 6, 2018 · Operations

Hybrid Learning Beats Thresholds: Anomaly Detection for Millions of KPI Curves

The article recounts the author’s 2017‑onward journey building an intelligent operations platform at Tencent, detailing challenges such as legacy thresholds, AIOps talent shortage, and lack of frameworks, and explains how a two‑stage hybrid unsupervised‑supervised model was devised to automatically detect anomalies across millions of KPI time‑series, enabling scalable root‑cause analysis and cost optimization.

OperationsTime Seriesaiops
0 likes · 7 min read
Hybrid Learning Beats Thresholds: Anomaly Detection for Millions of KPI Curves
Hulu Beijing
Hulu Beijing
Feb 6, 2018 · Artificial Intelligence

Modeling Chinese Word Segmentation with Hidden Markov Models

This article explains how Hidden Markov Models can be used to model Chinese word segmentation, covering the underlying Markov process, model parameters, basic HMM problems, and both supervised and unsupervised training methods.

Chinese Word SegmentationHidden Markov Modelmachine learning
0 likes · 8 min read
Modeling Chinese Word Segmentation with Hidden Markov Models
Java Backend Technology
Java Backend Technology
Feb 6, 2018 · Artificial Intelligence

How JD Built a Scalable AI-Powered Recommendation Engine for E‑Commerce

This article details JD's evolution from rule‑based recommendations to a multi‑screen, AI‑driven personalization platform, describing its system architecture, data pipelines, feature services, and key technologies that enable real‑time, user‑centric product suggestions across the e‑commerce ecosystem.

Big Dataartificial intelligencee‑commerce
0 likes · 20 min read
How JD Built a Scalable AI-Powered Recommendation Engine for E‑Commerce
360 Quality & Efficiency
360 Quality & Efficiency
Feb 5, 2018 · Artificial Intelligence

Fundamentals of Recommendation Engines: User Profiling, Data Classification, and Testing Methods

The article explains the core concepts of recommendation engines—user profiling and data classification—describes how large‑scale data processing tools are used to build models, and outlines common offline and A/B testing approaches for evaluating recommendation performance.

AB testingdata classificationmachine learning
0 likes · 4 min read
Fundamentals of Recommendation Engines: User Profiling, Data Classification, and Testing Methods
Hulu Beijing
Hulu Beijing
Feb 1, 2018 · Artificial Intelligence

Understanding GANs: Theory, Minimax Game, and Training Challenges

This article introduces Generative Adversarial Networks (GANs), explains their minimax formulation, value function, Jensen‑Shannon divergence, common variants, and practical training issues such as gradient saturation, while also previewing the next topic on Hidden Markov Models.

Deep LearningGANGenerative Adversarial Networks
0 likes · 11 min read
Understanding GANs: Theory, Minimax Game, and Training Challenges
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Jan 30, 2018 · Operations

Can You Predict Switch Failures Before They Happen? Inside PreFix’s ML Approach

This article reviews the PreFix system, which uses machine‑learning on datacenter switch logs to predict hardware failures ahead of time, detailing its design, feature extraction, random‑forest model, experimental validation across multiple switch models, and its broader applicability to disk failure prediction.

Random Forestdatacenter networkslog analysis
0 likes · 12 min read
Can You Predict Switch Failures Before They Happen? Inside PreFix’s ML Approach
Architecture Digest
Architecture Digest
Jan 30, 2018 · Artificial Intelligence

Overview of Toutiao's Recommendation System: Architecture, Content Analysis, User Tagging, Evaluation, and Content Safety

This article presents a comprehensive overview of Toutiao's recommendation system, detailing its three‑dimensional modeling approach, real‑time training pipeline, feature engineering, content and user analysis techniques, evaluation methodology, and the extensive content‑safety mechanisms employed to ensure reliable and responsible information distribution.

Content Safetycontent analysisevaluation
0 likes · 19 min read
Overview of Toutiao's Recommendation System: Architecture, Content Analysis, User Tagging, Evaluation, and Content Safety
21CTO
21CTO
Jan 27, 2018 · Artificial Intelligence

How to Overcome Real-World AI Implementation Challenges and Unlock Business Value

This article explores the growing complexity of AI adoption, the need for customized predictive solutions, and practical steps for enterprises to integrate machine learning without over‑hauling development teams, using IoT predictive‑maintenance as a concrete example.

AI implementationData ScienceEnterprise AI
0 likes · 8 min read
How to Overcome Real-World AI Implementation Challenges and Unlock Business Value
vivo Internet Technology
vivo Internet Technology
Jan 22, 2018 · Artificial Intelligence

Learning to Rank: From Regression to Search Ranking and Evaluation Methods

Learning to rank reframes search as a machine‑learning problem that optimizes document ordering rather than numeric prediction, using relevance metrics such as NDCG and feature‑based scoring functions, and comparing point‑wise, pair‑wise (RankSVM) and list‑wise (ListNet) approaches while stressing that proper error definition and feature selection matter more than the specific algorithm.

Learning-to-RankNDCGPairwise
0 likes · 16 min read
Learning to Rank: From Regression to Search Ranking and Evaluation Methods
21CTO
21CTO
Jan 18, 2018 · Artificial Intelligence

How Ctrip Scales Personalized Travel Recommendations: From Recall to Ranking

This article details Ctrip's end‑to‑end personalized recommendation system for travel, covering data collection, candidate recall methods, ranking models, feature engineering practices, and future directions, illustrating how millions of users receive tailored travel suggestions.

CtripRecommendation SystemsTravel
0 likes · 17 min read
How Ctrip Scales Personalized Travel Recommendations: From Recall to Ranking
Ctrip Technology
Ctrip Technology
Jan 18, 2018 · Artificial Intelligence

AI Algorithm Practices and Data Platform Architecture at Ping An Bank

The article presents Ping An Bank's AI-driven data platform, covering business background, architectural layers, algorithmic applications such as customer segmentation, portrait, business forecasting, and graph analysis, and shares practical insights on platform design, model deployment, and the role of data product managers.

AIBankingCustomer Segmentation
0 likes · 13 min read
AI Algorithm Practices and Data Platform Architecture at Ping An Bank
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 17, 2018 · Operations

How Alibaba Uses AI Models to Optimize Double 11 Consumer Benefits

Alibaba leverages multiple machine‑learning models—including spending forecasts, discount‑sensitivity, spread‑ability, category‑preference, and churn prediction—to intelligently allocate shopping vouchers and red packets during Double 11, boosting consumer engagement, merchant sales, and overall platform GMV.

consumer behaviore‑commercemachine learning
0 likes · 9 min read
How Alibaba Uses AI Models to Optimize Double 11 Consumer Benefits
21CTO
21CTO
Jan 16, 2018 · Artificial Intelligence

Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation

This article provides a comprehensive overview of Toutiao's recommendation system, covering its three‑dimensional modeling approach, feature engineering, real‑time training pipeline, recall strategies, user‑tag generation, evaluation methodology, and content‑safety mechanisms.

Content SafetyReal-time Trainingevaluation
0 likes · 18 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation
dbaplus Community
dbaplus Community
Jan 15, 2018 · Operations

How JD Finance Achieves Real-Time Capacity Assessment and Smart Alerting

This article explains JD Finance's operational challenges in a rapidly expanding micro‑service environment and presents a comprehensive approach that combines offline and online load testing, precise capacity calculations, and intelligent root‑cause alert analysis using both rule‑based and machine‑learning techniques.

Load TestingOperationsRoot Cause Analysis
0 likes · 15 min read
How JD Finance Achieves Real-Time Capacity Assessment and Smart Alerting
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 12, 2018 · Artificial Intelligence

How Alibaba’s New AI-Powered Ad Retrieval Model Redefined E‑Commerce Sponsored Search

Alibaba’s latest AI-driven ad retrieval framework, unveiled at WWW 2018, replaces keyword‑based search with a user‑behavior heterogeneous graph and machine‑learning models, delivering personalized, high‑efficiency ad matching that boosts ROI for advertisers, improves user experience, and enhances platform revenue.

ad retrievale-commerce advertisingheterogeneous graph
0 likes · 9 min read
How Alibaba’s New AI-Powered Ad Retrieval Model Redefined E‑Commerce Sponsored Search
Ctrip Technology
Ctrip Technology
Jan 4, 2018 · Artificial Intelligence

Intelligent Cloud Customer Service Platform: Overview, Architecture, and Key AI Models

This article presents the design, architecture, and several AI-driven models—including user intent detection, group supervision, content extraction, knowledge graph construction, and self‑service QA—of Ctrip's intelligent cloud customer service platform, highlighting its impact on service efficiency and business automation.

AIcloud platformmachine learning
0 likes · 7 min read
Intelligent Cloud Customer Service Platform: Overview, Architecture, and Key AI Models
Architects' Tech Alliance
Architects' Tech Alliance
Dec 28, 2017 · Operations

Intelligent Operations: Machine‑Learning‑Based AIOps – Lecture Summary by Prof. Pei Dan

In this lecture, Prof. Pei Dan of Tsinghua University outlines the evolution of intelligent operations from rule‑based automation to machine‑learning‑driven AIOps, discusses data, feedback loops, and practical challenges, and calls for stronger collaboration between industry and academia to accelerate research and deployment.

Big Dataaiopscloud computing
0 likes · 10 min read
Intelligent Operations: Machine‑Learning‑Based AIOps – Lecture Summary by Prof. Pei Dan
AntTech
AntTech
Dec 22, 2017 · Artificial Intelligence

Transfer Learning: Concepts, Challenges, and Recent Research Highlights from CIKM 2017

This article reviews the key concepts, challenges, and recent research on transfer learning presented at CIKM 2017, covering instance, feature, parameter, and relation‑based methods, supervised and unsupervised deep TL approaches, and transitive transfer learning with associated loss formulations and optimization strategies.

AI researchDeep Learningmachine learning
0 likes · 9 min read
Transfer Learning: Concepts, Challenges, and Recent Research Highlights from CIKM 2017
21CTO
21CTO
Dec 21, 2017 · Artificial Intelligence

How Ordinary Programmers Can Transform Into AI Engineers: Real Success Stories

This article explores whether regular programmers should switch to AI engineering, presents three detailed real‑world transition cases, outlines step‑by‑step learning paths, essential resources, and practical advice for mastering machine learning and deep learning technologies.

AIDeep Learningcareer transition
0 likes · 17 min read
How Ordinary Programmers Can Transform Into AI Engineers: Real Success Stories
ITPUB
ITPUB
Dec 19, 2017 · Artificial Intelligence

Top 20 Open‑Source Python Machine‑Learning Projects on GitHub

This article surveys the 20 most active Python machine‑learning repositories on GitHub, summarizing each project's core capabilities, typical use cases, and providing direct links for developers interested in exploring open‑source AI tools.

AIGitHubPython
0 likes · 9 min read
Top 20 Open‑Source Python Machine‑Learning Projects on GitHub
Architecture Digest
Architecture Digest
Dec 17, 2017 · Artificial Intelligence

Introduction to User Behavior and Collaborative Filtering in Recommendation Systems

This article explains user behavior concepts and feedback types, introduces collaborative filtering methods including user‑based, item‑based and latent factor models, discusses similarity measures, power‑law distributions, and practical considerations such as negative sampling, providing a comprehensive overview for building recommendation systems.

Recommendation Systemscollaborative filteringlatent factor model
0 likes · 9 min read
Introduction to User Behavior and Collaborative Filtering in Recommendation Systems
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Dec 5, 2017 · Artificial Intelligence

10 Must‑Know Machine Learning Algorithms for Engineers

From foundational concepts to practical examples, this guide walks engineers through ten essential supervised and unsupervised machine‑learning algorithms—decision trees, Naïve Bayes, linear regression, logistic regression, SVM, ensemble methods, clustering, PCA, SVD, and ICA—explaining their theory, real‑world uses, and why they matter.

AlgorithmsData ScienceModel Evaluation
0 likes · 11 min read
10 Must‑Know Machine Learning Algorithms for Engineers
Qunar Tech Salon
Qunar Tech Salon
Dec 5, 2017 · Information Security

Machine Learning Practices for Web Attack Detection at Ctrip

This article describes Ctrip’s evolution from rule‑based web attack detection to a Spark‑powered machine‑learning system, detailing the Nile architecture, data collection, feature engineering with TF‑IDF, model training, evaluation metrics, online deployment, and future enhancements for information security.

Web Securityattack detectionbinary classification
0 likes · 17 min read
Machine Learning Practices for Web Attack Detection at Ctrip
Meituan Technology Team
Meituan Technology Team
Dec 1, 2017 · Artificial Intelligence

Meituan-Dianping DSP Advertising Coarse Ranking Mechanisms and Scenario‑Based Targeting

Meituan‑Dianping’s DSP coarse‑ranking filters large ad candidate sets by scoring ads with user‑profile, weather, and keyword scenario models—using frequent‑itemset mining, AdaBoost, and TF/IDF—then aggregates these scores via a linear‑regression model to select high‑relevance ads for fine‑ranking, boosting click‑through and conversion rates.

Advertisingcoarse rankingkeyword targeting
0 likes · 23 min read
Meituan-Dianping DSP Advertising Coarse Ranking Mechanisms and Scenario‑Based Targeting
Baixing.com Technical Team
Baixing.com Technical Team
Nov 30, 2017 · Artificial Intelligence

How User Profiling Powers Modern Recommendation Systems

This article explains what user profiling is, why it’s crucial for recommendation systems, outlines key dimensions such as personal attributes, status, and interests, describes algorithms like classification and autoregressive models, and details offline and real‑time computation methods, evaluation techniques, and practical examples.

Recommendation Systemsalgorithmdata mining
0 likes · 11 min read
How User Profiling Powers Modern Recommendation Systems
Node Underground
Node Underground
Nov 24, 2017 · Artificial Intelligence

Build Your First Node.js Face Recognition App with opencv4nodejs

This article introduces how to leverage the opencv4nodejs Node.js module—binding OpenCV’s full API—to develop a face detection and recognition application, highlighting the CPU‑intensive nature of computer‑vision tasks, the limitations of JavaScript, and the availability of synchronous and asynchronous examples.

Computer VisionNode.jsOpenCV
0 likes · 2 min read
Build Your First Node.js Face Recognition App with opencv4nodejs
Efficient Ops
Efficient Ops
Nov 23, 2017 · Artificial Intelligence

How to Turn AIOps from Hype into Reality: A Practical Roadmap

In this comprehensive talk, Pei Dan outlines the technical and strategic roadmap for bringing AIOps to production, explains the challenges of anomaly detection, fault localization, root‑cause analysis and prediction, and demonstrates how to decompose complex operations problems into AI‑solvable tasks.

AIOperationsaiops
0 likes · 21 min read
How to Turn AIOps from Hype into Reality: A Practical Roadmap
Meituan Technology Team
Meituan Technology Team
Nov 23, 2017 · Artificial Intelligence

O2O Machine Learning Applications Seminar

The O2O Machine Learning Applications Seminar, featuring experts from Meituan‑Dianping and Alibaba, explores real‑world ML implementations for online‑to‑offline services, including online learning for search, Alibaba’s Ali Xiaomi intelligent assistant, deep‑learning‑driven recommendation systems, and advertising algorithms such as CTR and CVR optimization, sharing practical insights and best practices.

Deep LearningO2OOnline Learning
0 likes · 5 min read
O2O Machine Learning Applications Seminar
MaGe Linux Operations
MaGe Linux Operations
Nov 22, 2017 · Artificial Intelligence

Top 15 Python Libraries Every Data Scientist Should Master in 2017

This article surveys the most essential Python packages for data science in 2017, covering core scientific computing, data manipulation, visualization, machine learning, deep learning, natural language processing, and web scraping, and explains why each library remains indispensable for modern analysts.

Data ScienceNLPPython
0 likes · 13 min read
Top 15 Python Libraries Every Data Scientist Should Master in 2017
Architects' Tech Alliance
Architects' Tech Alliance
Nov 20, 2017 · Artificial Intelligence

Understanding the Evolution and Differences of AI, Machine Learning, and Deep Learning

This article explains the origins and development of artificial intelligence, clarifies the relationships and distinctions among AI, machine learning, and deep learning, and uses several illustrative diagrams to help readers quickly grasp how these three hot AI technologies are connected and differ from each other.

AIDeep Learningmachine learning
0 likes · 4 min read
Understanding the Evolution and Differences of AI, Machine Learning, and Deep Learning
21CTO
21CTO
Nov 15, 2017 · Artificial Intelligence

Which Programming Language Wins the Machine Learning Job Market? Data‑Driven Insights

An analysis of Indeed.com job‑trend data reveals how programming languages like Python, Java, R, C++, Scala and Julia rank in popularity for machine‑learning and data‑science positions, highlighting growth patterns and offering guidance on language selection based on career goals.

Data Sciencejob marketmachine learning
0 likes · 6 min read
Which Programming Language Wins the Machine Learning Job Market? Data‑Driven Insights
21CTO
21CTO
Nov 15, 2017 · Artificial Intelligence

What Is Machine Learning? Core Concepts Explained Simply

This article introduces the fundamental concepts of machine learning, defining the terms "machine" and "learning," presenting Tom Mitchell's formal definition, outlining the roles of learners and predictors, and contrasting machine‑learning programs with traditional software through clear diagrams.

DefinitionModellearning process
0 likes · 4 min read
What Is Machine Learning? Core Concepts Explained Simply
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 10, 2017 · Artificial Intelligence

iQIYI Recommendation System: Architecture, Model Evolution, and Ranking Strategies

The iQIYI recommendation system combines a two‑stage pipeline of recall and ranking, evolving from logistic regression to a GBDT‑FM‑DNN ensemble, using online feature storage, extensive feature engineering, and configurable strategies to deliver personalized video suggestions while addressing feature drift and multi‑objective business goals.

GBDTRecommendation Systemsdeep neural networks
0 likes · 13 min read
iQIYI Recommendation System: Architecture, Model Evolution, and Ranking Strategies
21CTO
21CTO
Nov 1, 2017 · Artificial Intelligence

Essential Machine Learning Algorithms: From Decision Trees to ICA Explained

This article introduces the most common machine learning algorithms, covering supervised methods such as decision trees, Naive Bayes, linear regression, logistic regression, SVM, and ensemble techniques, as well as unsupervised approaches like clustering, PCA, SVD, and ICA, with practical examples and visual illustrations.

AlgorithmsUnsupervised Learningmachine learning
0 likes · 10 min read
Essential Machine Learning Algorithms: From Decision Trees to ICA Explained
21CTO
21CTO
Oct 31, 2017 · Artificial Intelligence

Machine Learning vs Deep Learning: Key Differences, Examples, and Future Trends

This article explains the fundamental concepts of machine learning and deep learning, compares their data and hardware dependencies, feature processing, problem‑solving approaches, execution time, and interpretability, and outlines real‑world applications and future development trends.

Data ScienceDeep LearningNeural Networks
0 likes · 13 min read
Machine Learning vs Deep Learning: Key Differences, Examples, and Future Trends
Efficient Ops
Efficient Ops
Oct 30, 2017 · Artificial Intelligence

How AI Predicts Disk Failures: Turning Reactive Storage into Proactive Reliability

This article explains why traditional passive disk‑failure handling is insufficient, describes a machine‑learning engine that combines SMART data with workload analysis to forecast disk lifespan with over 96% accuracy, and outlines the operational benefits of proactive failure management.

AIPredictive MaintenanceStorage Reliability
0 likes · 6 min read
How AI Predicts Disk Failures: Turning Reactive Storage into Proactive Reliability
21CTO
21CTO
Oct 20, 2017 · Artificial Intelligence

Google AutoML Writes Code Faster Than Humans – AI Beats Programmers

Google's AutoML system can automatically generate and improve machine‑learning code, outperforming human researchers with record‑high accuracy on image‑recognition tasks and demonstrating that AI‑driven self‑replicating programs can surpass programmers in just a few hours.

AI code generationAutoMLGoogle AI
0 likes · 3 min read
Google AutoML Writes Code Faster Than Humans – AI Beats Programmers
21CTO
21CTO
Oct 20, 2017 · Artificial Intelligence

How Pornhub’s New AI Identifies Adult Stars in Videos

Pornhub unveiled an AI model that uses computer‑vision techniques to automatically recognize and tag over ten thousand adult performers, allowing users to search more precisely while also involving human reviewers to verify and improve the system’s accuracy.

Adult IndustryComputer Visionartificial intelligence
0 likes · 5 min read
How Pornhub’s New AI Identifies Adult Stars in Videos
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Oct 20, 2017 · Artificial Intelligence

How to Build a Customer Churn Warning Model with R and Discover

This article demonstrates a step‑by‑step workflow for constructing a churn prediction model using R in Discover, covering data loading, preprocessing, feature extraction, labeling, random‑forest training, prediction, and evaluation to help businesses proactively retain high‑value customers.

DiscoverRRandom Forest
0 likes · 11 min read
How to Build a Customer Churn Warning Model with R and Discover