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Machine Learning

1959 articles · Page 18 of 20
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 ethicsArtificial IntelligenceCrime Prevention
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.

Machine LearningModel MonitoringModeling
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.

KNNMachine LearningNaive Bayes
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.

AIMachine LearningVoice 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.

KerasMachine LearningPandas
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 LearningMachine Learninglarge-scale recommendation
0 likes · 27 min read
How Tree‑Based Deep Match Revolutionizes Large‑Scale Recommendation Systems
ThinkingAgent
ThinkingAgent
Mar 22, 2018 · Artificial Intelligence

A Step‑by‑Step Guide to the Standard Machine Learning Workflow

The article outlines the seven‑stage machine‑learning pipeline—from data acquisition and preparation through model selection, training, evaluation, parameter tuning, and deployment—highlighting the critical role of feature engineering, which typically consumes over 80% of project time.

EvaluationMachine LearningWorkflow
0 likes · 3 min read
A Step‑by‑Step Guide to the Standard Machine Learning Workflow
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.

AIOpsBaiduMachine Learning
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 LearningLibraries
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 GraphMachine Learning
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.

ClusteringMachine LearningSupervised Learning
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 NetworksMachine Learning
0 likes · 11 min read
Understanding WGANs: From GAN Pitfalls to Wasserstein Solutions
ThinkingAgent
ThinkingAgent
Mar 4, 2018 · Artificial Intelligence

A Minimalist Guide to Machine Learning with Scikit‑Learn

The author reflects on six months of learning machine learning, explains that the core techniques are simple while real difficulty lies in applying them to specific problems, and shows how scikit‑learn can quickly address classification, regression, clustering, and dimensionality‑reduction tasks before discussing the need for deeper model and hyper‑parameter tuning.

Machine LearningModel Selectionclassification
0 likes · 3 min read
A Minimalist Guide to Machine Learning with Scikit‑Learn
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.

Machine LearningModel EvaluationPython
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.

ClusteringEnsemble MethodsMachine Learning
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 DataMachine LearningMonte Carlo
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 testingBayesianMachine Learning
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 DivergenceMachine Learningcross entropy
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.

AIOpsMachine Learninganomaly detection
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.

Artificial IntelligenceBig DataE‑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 testingMachine LearningOffline Testing
0 likes · 4 min read
Fundamentals of Recommendation Engines: User Profiling, Data Classification, and Testing Methods
Architecture Digest
Architecture Digest
Feb 3, 2018 · Artificial Intelligence

Understanding Entropy, Joint Entropy, Conditional Entropy, Relative Entropy, and Cross Entropy

This article explains the concepts of entropy, joint entropy, conditional entropy, relative entropy (KL divergence) and cross‑entropy, illustrating their definitions, mathematical formulas, intuitive interpretations, and relationships through simple probability examples and visual diagrams.

KL DivergenceMachine Learningcross entropy
0 likes · 14 min read
Understanding Entropy, Joint Entropy, Conditional Entropy, Relative Entropy, and Cross Entropy
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.

Machine LearningRandom Forestdatacenter networks
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 AnalysisEvaluationMachine Learning
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 implementationEnterprise AIIoT
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-RankMachine LearningNDCG
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.

CtripMachine LearningRecommendation Systems
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.

E‑commerceMachine Learningconsumer behavior
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.

EvaluationMachine LearningReal-time Training
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.

Machine LearningMonitoringRoot 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.

Machine Learningad retrievale-commerce advertising
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.

AIMachine Learningcloud platform
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.

AIOpsBig DataCloud 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 LearningMachine Learning
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.

AIGitHubMachine Learning
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.

Collaborative FilteringMachine LearningRecommendation Systems
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.

AlgorithmsArtificial IntelligenceMachine Learning
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.

Machine Learningattack 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.

AdvertisingMachine Learningcoarse ranking
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.

AlgorithmMachine LearningRecommendation Systems
0 likes · 11 min read
How User Profiling Powers Modern Recommendation Systems
JD Tech
JD Tech
Nov 30, 2017 · Artificial Intelligence

Interview with JD Infrastructure Chief Architect He Xiaofeng on Real‑time Computing and Product Data Mining

He Xiaofeng, JD Mall Infrastructure chief architect, discusses his role in building a real‑time computing platform, applying streaming frameworks, machine learning, and knowledge‑graph techniques to product data mining, improve search accuracy, and outline future research directions.

JD.comKnowledge GraphMachine Learning
0 likes · 5 min read
Interview with JD Infrastructure Chief Architect He Xiaofeng on Real‑time Computing and Product Data Mining
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.

Machine LearningNode.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.

AIAIOpsMachine Learning
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.

Artificial IntelligenceDeep LearningMachine 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.

Machine LearningNLPPython
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.

Machine Learningdata sciencejob market
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.

DefinitionMachine LearningModel
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.

GBDTMachine LearningRecommendation Systems
0 likes · 13 min read
iQIYI Recommendation System: Architecture, Model Evolution, and Ranking Strategies
MaGe Linux Operations
MaGe Linux Operations
Nov 3, 2017 · Artificial Intelligence

Google AutoML Beats Researchers: AI Writes Superior Machine‑Learning Code

Google’s AutoML system has begun generating machine‑learning code that outperforms the very researchers who created it, achieving record‑high accuracy on image‑recognition tasks and demonstrating a self‑replicating AI that challenges the notion of human superiority in programming.

AI code generationArtificial IntelligenceAutoML
0 likes · 4 min read
Google AutoML Beats Researchers: AI Writes Superior Machine‑Learning Code
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.

AlgorithmsMachine LearningSupervised 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.

Deep LearningMachine Learningdata science
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.

AIMachine LearningStorage 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 IndustryArtificial IntelligenceMachine Learning
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.

DiscoverMachine LearningR
0 likes · 11 min read
How to Build a Customer Churn Warning Model with R and Discover
Architects' Tech Alliance
Architects' Tech Alliance
Oct 17, 2017 · Artificial Intelligence

AI Learning Resources and Architecture Overview – A Curated Collection

This article presents a comprehensive collection of AI, machine learning, and deep learning learning materials, including historical overviews, technology architectures, application domains, and numerous downloadable resources such as tutorials, datasets, and code links, aimed at guiding enthusiasts and professionals in intelligent operations.

Deep LearningMachine LearningResources
0 likes · 16 min read
AI Learning Resources and Architecture Overview – A Curated Collection
iQIYI Technical Product Team
iQIYI Technical Product Team
Oct 13, 2017 · Industry Insights

How iQIYI Built a Cloud‑Native Risk Control Platform to Stop Credential Stuffing

iQIYI’s security cloud team designed a data‑driven, cloud‑native risk control platform that unifies threat detection, rule management, and security knowledge across membership, video, e‑commerce and payment services, achieving sub‑5 ms latency, 24 billion daily requests, and near‑complete elimination of machine credential‑stuffing attacks.

Cloud securityMachine Learningdata-driven security
0 likes · 17 min read
How iQIYI Built a Cloud‑Native Risk Control Platform to Stop Credential Stuffing
Meituan Technology Team
Meituan Technology Team
Oct 12, 2017 · Artificial Intelligence

Machine Learning Q&A: Data Imputation, Feature Selection, Recommendation Systems and More

The article answers ten machine‑learning questions, explaining how to impute missing behavior data, extract and select features, describe Meituan‑Dianping’s recommendation pipeline, suggest a beginner learning path, clarify L1 sparsity, recommend TextCNN for text, discuss search‑ranking sample bias, label generation for wide‑deep models, the shift to deep‑learning video detection, and the use of factorization machines for CTR with open‑source examples.

Deep LearningL1 RegularizationMachine Learning
0 likes · 7 min read
Machine Learning Q&A: Data Imputation, Feature Selection, Recommendation Systems and More
Hujiang Technology
Hujiang Technology
Oct 12, 2017 · Artificial Intelligence

An Overview of Machine Learning and Deep Learning: Definitions, Concepts, and Core Techniques

This article provides a comprehensive introduction to machine learning and deep learning, covering their definitions, classifications, key algorithms, neural network structures, core concepts such as generalization and regularization, and typical architectures like CNN and RNN, illustrated with numerous diagrams.

CNNMachine LearningRNN
0 likes · 22 min read
An Overview of Machine Learning and Deep Learning: Definitions, Concepts, and Core Techniques
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Oct 12, 2017 · Artificial Intelligence

Which Machine Learning Skills Will Be Most In‑Demand in the Next 3‑5 Years?

The article explains that industrial AI needs specialists who can apply machine‑learning models to specific domains, outlines essential fundamentals such as regression, classification, neural networks, data visualization, and unsupervised learning, and offers practical career advice for students and early‑career professionals seeking to transition into machine‑learning roles.

Industrial AIMachine Learningcareer advice
0 likes · 11 min read
Which Machine Learning Skills Will Be Most In‑Demand in the Next 3‑5 Years?
Hujiang Technology
Hujiang Technology
Oct 11, 2017 · Artificial Intelligence

An Overview of Machine Learning and Deep Learning: Definitions, Core Concepts, and Typical Architectures

This article provides a comprehensive introduction to machine learning and deep learning, covering their definitions, differences, key concepts such as generalization, regularization, and overfitting, as well as typical algorithms and network architectures like CNN and RNN, illustrated with numerous diagrams.

AlgorithmsMachine Learningneural networks
0 likes · 22 min read
An Overview of Machine Learning and Deep Learning: Definitions, Core Concepts, and Typical Architectures
Architects' Tech Alliance
Architects' Tech Alliance
Oct 10, 2017 · Artificial Intelligence

An Overview of a Three-Day Introductory TensorFlow Tutorial

This article introduces TensorFlow, its origins and capabilities, and summarizes a three‑day hands‑on tutorial covering installation, basic models, convolutional and recurrent neural networks, and practical code examples for deep learning practitioners.

AIDeep LearningMachine Learning
0 likes · 5 min read
An Overview of a Three-Day Introductory TensorFlow Tutorial
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Oct 9, 2017 · Artificial Intelligence

A Pragmatic Roadmap to Master Machine Learning: Courses, Resources, and Tips

The author shares a step‑by‑step self‑learning plan for machine learning, covering essential linear‑algebra refreshers, foundational algorithm courses, hands‑on coding tutorials in MATLAB and Python, advanced deep‑learning studies with CS231n, and a curated list of reference links and GitHub notes.

Deep LearningMachine Learninglinear algebra
0 likes · 8 min read
A Pragmatic Roadmap to Master Machine Learning: Courses, Resources, and Tips
Tongcheng Travel Technology Center
Tongcheng Travel Technology Center
Sep 29, 2017 · Big Data

Evolution of Monitoring Architecture and Traffic Alert Algorithms at Tongcheng Travel

This article describes how Tongcheng Travel’s monitoring system evolved from a monolithic design to a distributed and big‑data‑based architecture, introducing real‑time processing with Storm, machine‑learning‑enhanced alerts, and a multivariate linear regression model that dramatically improves traffic anomaly detection accuracy.

Big DataMachine LearningMonitoring
0 likes · 10 min read
Evolution of Monitoring Architecture and Traffic Alert Algorithms at Tongcheng Travel
Architects' Tech Alliance
Architects' Tech Alliance
Sep 28, 2017 · Artificial Intelligence

Gartner 2017 Top 10 Strategic Technology Trends: AI, Smart Apps, IoT, VR/AR, Digital Twin, Blockchain and More

Gartner's 2017 report outlines ten strategic technology trends—including artificial intelligence, smart applications, intelligent objects, immersive VR/AR, digital twins, blockchain, conversational systems, mesh services, digital platforms, and adaptive security—that together drive an intelligence‑centric, digitally connected future for enterprises.

AIBlockchainDigital Twin
0 likes · 12 min read
Gartner 2017 Top 10 Strategic Technology Trends: AI, Smart Apps, IoT, VR/AR, Digital Twin, Blockchain and More
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 22, 2017 · Artificial Intelligence

iQIYI NLP Team: Research Topics, Progress, and Applications in Video Services

The iQIYI NLP team applies lexical analysis, knowledge‑graph construction, tag recommendation, query understanding, voice‑assistant semantics, sentiment mining, and box‑office/view‑count prediction—leveraging weakly labeled data, CRF/CNN‑CRF models and deep learning—to enhance video comprehension, recommendation, search and commercial services across the platform.

Machine LearningNLPRecommendation Systems
0 likes · 13 min read
iQIYI NLP Team: Research Topics, Progress, and Applications in Video Services
Hujiang Technology
Hujiang Technology
Sep 20, 2017 · Artificial Intelligence

Fundamentals and Algorithms of Recommender Systems

This article explains why recommender systems were created, describes the problem of information overload, introduces core algorithms such as popularity, content‑based, collaborative filtering and hybrid methods, and illustrates each with a six‑user/book example and a Netflix case study.

Collaborative FilteringMachine Learningcontent-based filtering
0 likes · 17 min read
Fundamentals and Algorithms of Recommender Systems
Architecture Digest
Architecture Digest
Sep 15, 2017 · Artificial Intelligence

Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations

This article explains the objectives of recommendation systems, compares popular recommendation approaches, details the components and algorithms of personalized recommendation pipelines, and discusses practical challenges such as real‑time processing, freshness, cold‑start, diversity, content quality, and surprise handling.

Cold StartData PipelineEvaluation
0 likes · 15 min read
Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations
JD Retail Technology
JD Retail Technology
Sep 13, 2017 · Artificial Intelligence

Machine Learning Applications for Product Data Quality and Knowledge Graph Construction at JD.com

At the 2nd China Big Data International Summit 2017, JD’s chief architect presented how machine‑learning techniques are applied across e‑commerce to improve product data quality, ensure compliance, resolve image‑text mismatches, automate category identification, restructure titles, and build a multi‑dimensional product knowledge graph.

Artificial IntelligenceKnowledge GraphMachine Learning
0 likes · 9 min read
Machine Learning Applications for Product Data Quality and Knowledge Graph Construction at JD.com
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 28, 2017 · Artificial Intelligence

Can Machine Learning Predict Baby Life Stages to Boost E‑Commerce Recommendations?

This paper introduces a dynamic fusion algorithm that leverages multi‑dimensional logistic regression and rich consumer behavior features to infer infants' life stages from parental e‑commerce actions, demonstrating significant accuracy improvements over memory‑less baselines across multiple months of Taobao data.

E‑commerceMachine Learningconsumer behavior
0 likes · 13 min read
Can Machine Learning Predict Baby Life Stages to Boost E‑Commerce Recommendations?
Qunar Tech Salon
Qunar Tech Salon
Aug 22, 2017 · Artificial Intelligence

Sliding Window and SVM Based Web Crawler Detection System Design

This article describes a flexible web crawler identification approach that combines sliding‑window data collection with Support Vector Machine classification, detailing the underlying concepts, feature extraction, system architecture, client‑server interaction, and deployment steps for practical use.

Client‑ServerMachine LearningSVM
0 likes · 7 min read
Sliding Window and SVM Based Web Crawler Detection System Design
Efficient Ops
Efficient Ops
Aug 21, 2017 · Operations

How AI-Driven Automation Transforms Tencent Game Operations

This article explains how Tencent Game operations moved from manual, threshold‑based monitoring to an AI‑powered, data‑driven workflow that automates scaling, improves online‑curve monitoring, enables full‑dimensional analysis, and reduces time, labor, and cost while enhancing player experience.

GamingMachine LearningMonitoring
0 likes · 16 min read
How AI-Driven Automation Transforms Tencent Game Operations
Qunar Tech Salon
Qunar Tech Salon
Aug 21, 2017 · Artificial Intelligence

Tourism Comment Text Mining and Recommendation System Using NLP and Big Data

This article presents a comprehensive NLP‑driven text‑mining workflow for tourism comment data, covering data cleaning, word2vec training, keyword extraction, sentiment analysis, ranking, and a lightweight architecture that enables fast, accurate recommendation of scenic spots for users.

Machine LearningNLPSentiment Analysis
0 likes · 5 min read
Tourism Comment Text Mining and Recommendation System Using NLP and Big Data
Architecture Digest
Architecture Digest
Aug 20, 2017 · Artificial Intelligence

Comprehensive Collection of Machine Learning, Python, and Mathematics Cheat Sheets

This article compiles over twenty‑seven curated cheat sheets covering machine learning algorithms, Python data‑science libraries, and essential mathematics such as probability, linear algebra, statistics, and calculus, providing concise reference images and source links for quick study.

Artificial IntelligenceCheat SheetMachine Learning
0 likes · 10 min read
Comprehensive Collection of Machine Learning, Python, and Mathematics Cheat Sheets