BestHub
Sign in
Tagged articles

machine learning

1966 articles · Page 8 of 20
Model Perspective
Model Perspective
Mar 21, 2023 · Artificial Intelligence

Master Linear Discriminant Analysis (LDA) with Python: Theory & Code

This article explains Linear Discriminant Analysis (LDA) as a pattern‑recognition technique that projects data onto a low‑dimensional space to maximize class separation, details its mathematical formulation with between‑class and within‑class scatter matrices, and provides a complete Python implementation using scikit‑learn on the Iris dataset, including visualization of the results.

LDALinear Discriminant AnalysisPython
0 likes · 6 min read
Master Linear Discriminant Analysis (LDA) with Python: Theory & Code
Python Programming Learning Circle
Python Programming Learning Circle
Mar 21, 2023 · Artificial Intelligence

A Survey of 10 Python Libraries for Explainable AI (XAI)

This article introduces Explainable AI (XAI), outlines its importance, describes a step-by-step workflow, and reviews ten Python libraries—including SHAP, LIME, ELI5, Shapash, Anchors, BreakDown, Interpret‑Text, AI Explainability 360, OmniXAI, and XAI—providing usage examples and code snippets.

Pythonexplainable AImachine learning
0 likes · 12 min read
A Survey of 10 Python Libraries for Explainable AI (XAI)
Baidu Geek Talk
Baidu Geek Talk
Mar 20, 2023 · Artificial Intelligence

How Graph Neural Networks Boost Anti‑Cheat in User Referral Activities

This article analyzes the use of graph neural network models, including GCN and multi‑graph SCGCN, to tackle cheating in referral‑based user acquisition by capturing user relationships, improving sample purity, and achieving up to a 50% increase in cheat‑sample recall.

GCNGraph AlgorithmsGraph Neural Networks
0 likes · 12 min read
How Graph Neural Networks Boost Anti‑Cheat in User Referral Activities
Smart Era Software Development
Smart Era Software Development
Mar 16, 2023 · Artificial Intelligence

10 Essential Elements of Machine Learning System Architecture

The article outlines ten core components—data and feature pipelines, feature store, training and retraining pipelines, metadata store, serving infrastructure, production monitoring, reusable ML pipelines, workflow orchestration, CI/CT/CD, and end‑to‑end quality control—that together form a scalable, reliable architecture for modern machine‑learning systems.

MLOpsfeature engineeringmachine learning
0 likes · 7 min read
10 Essential Elements of Machine Learning System Architecture
Efficient Ops
Efficient Ops
Mar 14, 2023 · Artificial Intelligence

How NetEase Games Built an AIOps Platform to Transform IT Operations

This article explains how NetEase Games leveraged AI, big data, and machine learning to create an AIOps platform that automates anomaly detection, log analysis, and fault localization, improving quality assurance, cost management, and operational efficiency across complex gaming infrastructures.

AIOpsIT Operationsanomaly detection
0 likes · 12 min read
How NetEase Games Built an AIOps Platform to Transform IT Operations
58 Tech
58 Tech
Mar 14, 2023 · Artificial Intelligence

Dialogue Robot Technology Practices in Recruitment Platforms

The DataFun Summit 2023 featured a presentation by senior AI engineer Sang Hailong on building and applying dialogue robot technologies—including QABot, TaskBot, intent recognition, and intelligent double‑call—in 58.com’s recruitment platform, with experimental insights on ChatGPT integration.

AIChatGPTchatbot
0 likes · 3 min read
Dialogue Robot Technology Practices in Recruitment Platforms
Python Crawling & Data Mining
Python Crawling & Data Mining
Mar 11, 2023 · Artificial Intelligence

How to Overcome Data Scarcity in Machine Learning: Strategies and Techniques

Facing data scarcity in machine learning, this article explores why large datasets are essential, categorizes missing data and label gaps, and presents practical solutions such as dataset reuse, augmentation, multimodal learning, curriculum learning, semi‑supervised methods, active learning, transfer and meta‑learning to mitigate the problem.

Data AugmentationData ScarcityMeta-Learning
0 likes · 19 min read
How to Overcome Data Scarcity in Machine Learning: Strategies and Techniques
21CTO
21CTO
Mar 10, 2023 · Artificial Intelligence

Inside OpenAI’s Robotics: Lilian’s Journey, AGI Vision, and AI Safety Insights

The interview with OpenAI Robotics researcher Lilian reveals the team’s gender makeup, her work on robot hands, reinforcement‑learning breakthroughs, applied AI safety projects, bias mitigation efforts, and how personal learning blogs fuel continuous innovation in artificial intelligence.

AGIBias MitigationOpenAI
0 likes · 11 min read
Inside OpenAI’s Robotics: Lilian’s Journey, AGI Vision, and AI Safety Insights
Python Programming Learning Circle
Python Programming Learning Circle
Mar 6, 2023 · Operations

Intelligent Operations: AI‑Driven Anomaly Detection, Alarm Compression, and Log Analysis Techniques

This article presents an AI‑enhanced operations framework that combines metric anomaly detection, alarm compression, log anomaly detection, and intelligent analysis using machine learning methods such as DBSCAN clustering, SARIMAX modeling, Apriori association rules, and LSTM‑based log parsing to improve fault detection and reduce operational costs.

AIOpsOperationsanomaly detection
0 likes · 15 min read
Intelligent Operations: AI‑Driven Anomaly Detection, Alarm Compression, and Log Analysis Techniques
DataFunSummit
DataFunSummit
Mar 3, 2023 · Artificial Intelligence

Intelligent Risk Control System Architecture and Development Trends

This article introduces the architecture of intelligent risk control, detailing its four-layer structure, the underlying data, feature, model, and decision components, platform interactions, and future development trends, highlighting how AI and big data enhance risk management efficiency and accuracy.

Decision Systemsbig datafeature engineering
0 likes · 12 min read
Intelligent Risk Control System Architecture and Development Trends
Architects' Tech Alliance
Architects' Tech Alliance
Mar 2, 2023 · Artificial Intelligence

In‑Depth Analysis of AI Servers for ChatGPT: Architecture, Costs, and Market Trends

This article provides a comprehensive technical overview of AI servers used for large‑scale models like ChatGPT, covering GPU‑centric architectures, classification by application and chip type, hardware cost breakdowns, market demand forecasts, domestic vendor strengths, and the impact of export restrictions on advanced accelerator chips.

AI serversChatGPTExport restrictions
0 likes · 17 min read
In‑Depth Analysis of AI Servers for ChatGPT: Architecture, Costs, and Market Trends
DataFunSummit
DataFunSummit
Mar 1, 2023 · Artificial Intelligence

Automating High-Fidelity Digital Human Creation: Scanning, Driving, and Remaining Challenges

The article details YINGMOU's research on automating the production of high‑fidelity digital humans, covering their rapid 3‑5‑day pipeline, extensive face‑asset database, advanced light‑field scanning, automatic topology reconstruction, AI‑driven rigging, dynamic mapping, and the unresolved issues of hair and cloth.

AI AutomationPBR materialsfacial rigging
0 likes · 12 min read
Automating High-Fidelity Digital Human Creation: Scanning, Driving, and Remaining Challenges
Python Programming Learning Circle
Python Programming Learning Circle
Mar 1, 2023 · Artificial Intelligence

Introducing Streamlit: A Free Open‑Source Framework for Building Machine‑Learning Apps with Python

Streamlit is a free, open‑source Python framework that lets machine‑learning engineers quickly turn scripts into interactive apps, offering features such as top‑down script execution, widget‑as‑variable handling, caching, GPU support, and seamless integration with version‑control tools, all without requiring separate frontend development.

App DevelopmentData VisualizationPython
0 likes · 9 min read
Introducing Streamlit: A Free Open‑Source Framework for Building Machine‑Learning Apps with Python
Top Architect
Top Architect
Mar 1, 2023 · Artificial Intelligence

Understanding the Internals of ChatGPT: Neural Networks, Embeddings, and Training Techniques

This article provides a comprehensive overview of how ChatGPT works, covering its probabilistic text generation, transformer architecture, embedding representations, neural network training processes, and the underlying principles that enable large language models to produce coherent and meaningful human-like language.

AIChatGPTTransformer
0 likes · 80 min read
Understanding the Internals of ChatGPT: Neural Networks, Embeddings, and Training Techniques
Bilibili Tech
Bilibili Tech
Feb 28, 2023 · Artificial Intelligence

High‑Quality Automatic Speech Recognition (ASR) Solutions at Bilibili: Data, Model, and Deployment Optimizations

Bilibili’s high‑quality ASR system combines large‑scale filtered business data, semi‑supervised Noisy‑Student training, an end‑to‑end CTC model with lattice‑free MMI decoding, and FP16‑optimized FasterTransformer inference on Triton, delivering top‑ranked accuracy, low latency, and scalable deployment for diverse Chinese‑English video content.

ASRBilibiliEnd-to-End
0 likes · 18 min read
High‑Quality Automatic Speech Recognition (ASR) Solutions at Bilibili: Data, Model, and Deployment Optimizations
DataFunTalk
DataFunTalk
Feb 26, 2023 · Artificial Intelligence

Interactive Recommendation System for Meituan Food Delivery: Architecture, Challenges, and Evaluation

This article details Meituan's interactive recommendation system for its food‑delivery homepage feed, covering the motivation, challenges, system architecture, user intent modeling, evaluation metrics, experimental results, and future directions, illustrating how real‑time, user‑centric recommendations improve conversion and user experience.

Food DeliveryMeituaninteractive recommendation
0 likes · 25 min read
Interactive Recommendation System for Meituan Food Delivery: Architecture, Challenges, and Evaluation
DaTaobao Tech
DaTaobao Tech
Feb 24, 2023 · Artificial Intelligence

Data Preprocessing and Statistical Analysis Techniques in Python

The article reviews essential Python data‑preprocessing and statistical‑analysis tools—including missing‑value imputation, outlier trimming, scaling, binning, knee‑point detection, correlation, chi‑square testing, linear regression, Wilson scoring, PCA weighting, text tokenization and sentiment analysis, plus visualization with matplotlib/seaborn and big‑data access via pyodps.

Pythonmachine learningstatistical analysis
0 likes · 17 min read
Data Preprocessing and Statistical Analysis Techniques in Python
DataFunTalk
DataFunTalk
Feb 17, 2023 · Artificial Intelligence

Full‑Chain Linkage Techniques for Alibaba Mama Display Advertising: From Precise Value Estimation to Set‑Selection Models

The article presents a comprehensive technical roadmap for Alibaba Mama's display advertising cascade ranking system, introducing full‑chain linkage, precise‑value estimation models (PDM, ESDM) and set‑selection approaches (LDM, LBDM), and demonstrates how these innovations jointly improve CTR and RPM while outlining future research directions.

Advertisingmachine learningpre‑ranking
0 likes · 25 min read
Full‑Chain Linkage Techniques for Alibaba Mama Display Advertising: From Precise Value Estimation to Set‑Selection Models
DataFunTalk
DataFunTalk
Feb 16, 2023 · Artificial Intelligence

Differences Between Advertising Algorithms and Recommendation Algorithms

This article compares advertising and recommendation algorithms, highlighting distinct optimization goals, model design focuses, training methods, implementation principles, auxiliary strategies, and model characteristics, emphasizing how ads aim to increase revenue while recommendations prioritize user engagement and diversity.

AdvertisingCTRalgorithm
0 likes · 5 min read
Differences Between Advertising Algorithms and Recommendation Algorithms
vivo Internet Technology
vivo Internet Technology
Feb 15, 2023 · Artificial Intelligence

Optimizing CDN Bandwidth Utilization and Cost Reduction with Predictive Control (Yugong Platform)

By leveraging the Yugong Platform’s predictive control—combining Prophet‑based threshold forecasts, custom real‑time bandwidth models, and a token‑bucket mechanism—to smooth peaks and fill valleys, enterprises can dramatically improve CDN bandwidth utilization, automate adjustments, and substantially lower peak‑based billing costs.

CDNFlow Controlbandwidth optimization
0 likes · 23 min read
Optimizing CDN Bandwidth Utilization and Cost Reduction with Predictive Control (Yugong Platform)
Tencent Cloud Developer
Tencent Cloud Developer
Feb 10, 2023 · Artificial Intelligence

Technical Overview of Claude's RLAIF Approach and Comparison with ChatGPT

Claude, Anthropic’s ChatGPT‑like assistant, employs Constitutional AI and a Reinforcement‑Learning‑from‑AI‑Feedback (RLAIF) pipeline that substitutes costly human‑ranked data with AI‑generated critiques and revisions, yielding comparable reasoning ability to ChatGPT while markedly increasing harmlessness through transparent rule‑based training, chain‑of‑thought prompting, and open‑source reproducible methods.

AI AlignmentChatGPTClaude
0 likes · 19 min read
Technical Overview of Claude's RLAIF Approach and Comparison with ChatGPT
Architect's Guide
Architect's Guide
Feb 9, 2023 · Artificial Intelligence

Why ChatGPT Is So Powerful: A Technical Overview of NLP Model Evolution

This article explains why ChatGPT performs so well by tracing the evolution of natural‑language processing from rule‑based grammars through statistical n‑gram models to neural architectures like RNNs, LSTMs, attention mechanisms, Transformers, and the massive data and training methods that power modern large language models.

AttentionChatGPTNLP
0 likes · 14 min read
Why ChatGPT Is So Powerful: A Technical Overview of NLP Model Evolution
Cloud Native Technology Community
Cloud Native Technology Community
Feb 7, 2023 · Cloud Native

Machine Learning‑Based Optimization of Kubernetes Resources

This article explains how machine learning can be applied to automatically optimize CPU and memory settings in Kubernetes clusters, covering both experiment‑driven and observation‑driven approaches, step‑by‑step procedures, best‑practice recommendations, and the benefits of combining both methods for efficient, scalable cloud‑native operations.

AutoscalingKubernetesPerformance
0 likes · 11 min read
Machine Learning‑Based Optimization of Kubernetes Resources
21CTO
21CTO
Feb 6, 2023 · Artificial Intelligence

Understanding the Transformer: How Attention Powers ChatGPT and Modern AI

This article breaks down the Transformer architecture behind ChatGPT, explaining its attention mechanism, embedding, positional encoding, and multi‑head self‑attention, while highlighting the model's impact on AI research, data requirements, and future innovations.

Artificial IntelligenceAttention MechanismChatGPT
0 likes · 18 min read
Understanding the Transformer: How Attention Powers ChatGPT and Modern AI
DataFunSummit
DataFunSummit
Feb 5, 2023 · Artificial Intelligence

Key Takeaways from the Causal Inference Summit: Motivation, Applications, Challenges, and Links to A/B Testing, Machine Learning, and Deep Learning

After attending the DataFun causal inference summit, this article outlines why causal analysis matters, its typical use cases, practical challenges, its relationship with A/B testing, and how it integrates with machine learning and deep learning to improve decision‑making and model robustness.

A/B testingCausal InferenceDecision Making
0 likes · 10 min read
Key Takeaways from the Causal Inference Summit: Motivation, Applications, Challenges, and Links to A/B Testing, Machine Learning, and Deep Learning
DataFunSummit
DataFunSummit
Feb 4, 2023 · Artificial Intelligence

Walle: An End‑to‑End, General‑Purpose, Scalable Edge‑Cloud Collaborative Machine Learning System

The article introduces Walle, Alibaba's four‑year‑old edge‑cloud collaborative machine‑learning platform that unifies compute containers, data pipelines, and a deployment platform to enable low‑latency, privacy‑preserving, and high‑throughput AI services across billions of mobile devices, and presents its architecture, design challenges, and evaluation results.

Cloud Computingdata pipelineedge computing
0 likes · 25 min read
Walle: An End‑to‑End, General‑Purpose, Scalable Edge‑Cloud Collaborative Machine Learning System
DataFunSummit
DataFunSummit
Feb 2, 2023 · Artificial Intelligence

Exploring Super Automation in JD Supply Chain: Architecture, Applications, and Future Outlook

This article presents JD's super automation approach for its supply chain, detailing the business background, challenges, AI‑driven forecasting, procurement, intelligent allocation, inventory clearing, integrated decision making, and future directions toward fully automated, optimal end‑to‑end operations.

JD.comOptimizationSupply Chain
0 likes · 17 min read
Exploring Super Automation in JD Supply Chain: Architecture, Applications, and Future Outlook
DataFunSummit
DataFunSummit
Feb 1, 2023 · Artificial Intelligence

OpenAI’s AI Text Classifier: Features, Limitations, and Real‑World Tests

OpenAI released an AI text classifier that predicts whether a passage was generated by AI, but its accuracy is limited—especially for short texts—so the article examines its training data, performance metrics, practical tests, comparisons with other detectors, and discusses broader implications for plagiarism and content creation.

AI detectionChatGPTOpenAI
0 likes · 16 min read
OpenAI’s AI Text Classifier: Features, Limitations, and Real‑World Tests
DataFunSummit
DataFunSummit
Feb 1, 2023 · Artificial Intelligence

Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting

The paper proposes clustering thousands of related time series and training separate global LSTM models for each cluster, showing that this reduces heterogeneity, leverages shared information, and improves forecasting accuracy compared to individual models, with extensive experiments on CIF2016 and NN5 datasets.

ClusteringLSTMRNN
0 likes · 33 min read
Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting
DataFunSummit
DataFunSummit
Jan 31, 2023 · Big Data

Data-Driven Production and Operations Management Decision-Making

This presentation explores how enterprises can transform massive operational data into precise decisions by applying statistical, machine‑learning, and optimization techniques across four topics: data‑to‑decision pipelines, promotion analysis metrics, long‑tail product pricing, and fast‑yet‑accurate unmanned‑warehouse management.

Optimizationdata-drivenmachine learning
0 likes · 13 min read
Data-Driven Production and Operations Management Decision-Making
DataFunTalk
DataFunTalk
Jan 27, 2023 · Artificial Intelligence

GNN for Science: Foundations, Applications, and Recent Advances in Equivariant Graph Neural Networks

This article reviews the role of graph neural networks in AI for science, covering background, the evolution of GNN models, applications in physics and biomedicine, recent advances in Euclidean equivariant GNNs, and the authors' own contributions such as GMN and GROVER, concluding with key distinctions between traditional GNNs and science‑focused approaches.

AI for ScienceGraph Neural NetworksMolecular Representation
0 likes · 16 min read
GNN for Science: Foundations, Applications, and Recent Advances in Equivariant Graph Neural Networks
DataFunTalk
DataFunTalk
Jan 25, 2023 · Artificial Intelligence

Between Heaven and Earth: Reflections of an Algorithm Engineer

The article argues that algorithm engineers should move beyond a narrow focus on deep‑learning models, emphasizing the importance of system architecture, data quality, and thoughtful problem framing to break through performance plateaus in advertising and recommendation systems.

AdvertisingAlgorithm EngineeringData Quality
0 likes · 10 min read
Between Heaven and Earth: Reflections of an Algorithm Engineer
DataFunSummit
DataFunSummit
Jan 18, 2023 · Artificial Intelligence

Interview on the Current State, Challenges, and Future Trends of Graph Algorithms

This interview summarizes experts' insights on graph algorithm technology, covering its early industrial adoption, data scale and sparsity challenges, various graph types and models, application scenarios such as recommendation and risk control, R&D workflow hurdles, and emerging research directions like pre‑training, explainability, and combinatorial optimization.

ApplicationsGraph AlgorithmsGraph Neural Networks
0 likes · 14 min read
Interview on the Current State, Challenges, and Future Trends of Graph Algorithms
Model Perspective
Model Perspective
Jan 12, 2023 · Artificial Intelligence

Neural Networks Explained: Architecture, Training, and Reinforcement Basics

This article introduces neural networks, covering their layered structure, common types like CNNs and RNNs, key components such as activation functions, loss, learning rate, backpropagation, dropout, batch normalization, and extends to reinforcement learning concepts including MDPs, policies, value functions, and Q‑learning.

CNNRNNmachine learning
0 likes · 6 min read
Neural Networks Explained: Architecture, Training, and Reinforcement Basics
Efficient Ops
Efficient Ops
Jan 9, 2023 · Operations

How Guotai Junan’s AIOps Platform Achieved Top‑Tier Evaluation in Intelligent Operations

Guotai Junan’s Intelligent Operations Service Platform, powered by AI‑driven AIOps, passed the China Academy of Information and Communications Technology’s excellence assessment for anomaly detection, showcasing advanced data‑driven monitoring, digital‑transformation initiatives, and future plans for fault prediction, self‑healing, and comprehensive operations intelligence.

AIOpsDigital TransformationIT Operations
0 likes · 15 min read
How Guotai Junan’s AIOps Platform Achieved Top‑Tier Evaluation in Intelligent Operations
Efficient Ops
Efficient Ops
Jan 9, 2023 · Operations

Guotai Junan’s AIOps Success: Inside the Award‑Winning Intelligent Operations Platform

The article explains how AIOps—AI‑driven IT operations—has become a strategic trend, details Guotai Junan’s award‑winning intelligent operations platform that achieved the top‑level “exception detection” evaluation, and shares interview insights on implementation, challenges, and future directions.

AIOpsDigital TransformationIT Operations
0 likes · 16 min read
Guotai Junan’s AIOps Success: Inside the Award‑Winning Intelligent Operations Platform
58UXD
58UXD
Jan 9, 2023 · Artificial Intelligence

How to Transform VR Scenes into Visually Stunning Experiences in Four Simple Steps

This article outlines a four‑step workflow to improve VR scene quality—covering hardware upgrades, optimized shooting setups, advanced image preprocessing, and custom color‑filter styling—demonstrating how systematic enhancements and blind testing can produce visually appealing, high‑quality VR experiences.

VRimage processingmachine learning
0 likes · 6 min read
How to Transform VR Scenes into Visually Stunning Experiences in Four Simple Steps
DataFunTalk
DataFunTalk
Jan 7, 2023 · Artificial Intelligence

How to Better Leverage Data in Causal Inference

This presentation introduces two recent works from Ant Group that improve causal inference by explicitly using historical control data to reduce selection bias and by fusing heterogeneous multi‑source data, describing the GBCT and WMDL methods, their theoretical foundations, experimental results, and practical applications in finance.

Bias CorrectionCausal Inferencedata fusion
0 likes · 18 min read
How to Better Leverage Data in Causal Inference
DaTaobao Tech
DaTaobao Tech
Jan 6, 2023 · Artificial Intelligence

Two‑Stage Ranking Optimization in E‑commerce Search: From Coarse to Fine Ranking

The paper presents a two‑stage e‑commerce search framework where the coarse‑ranking stage is redesigned with multi‑objective optimization, expanded negative sampling, and listwise distillation—guided by a new global transaction hitrate metric—enabling it to surpass fine‑ranking on large candidate sets and boost overall GMV by about one percent.

coarse rankinge-commercefine ranking
0 likes · 25 min read
Two‑Stage Ranking Optimization in E‑commerce Search: From Coarse to Fine Ranking
AntTech
AntTech
Jan 3, 2023 · Artificial Intelligence

Ray: The Distributed Framework Powering the Next Generation of Generative AI

Ray, an open‑source distributed computing framework originally created by Berkeley's RiseLab and heavily contributed to by Ant Group, underpins many AI workloads—from privacy‑preserving federated learning to large‑scale model training for ChatGPT—making it a critical yet often overlooked engine of the generative AI revolution.

OpenAIRaymachine learning
0 likes · 7 min read
Ray: The Distributed Framework Powering the Next Generation of Generative AI
DataFunTalk
DataFunTalk
Dec 28, 2022 · Artificial Intelligence

Automated Feature Engineering and Modeling for Credit Risk: A DataFun Case Study

This article explains how DataFun’s automated feature engineering and modeling platform dramatically reduces credit‑risk model development time from weeks to days by standardizing feature creation, integrating popular algorithms such as LR, XGBoost and LightGBM, and providing comprehensive evaluation, deployment and monitoring capabilities.

AIRFMautomated feature engineering
0 likes · 14 min read
Automated Feature Engineering and Modeling for Credit Risk: A DataFun Case Study
DataFunTalk
DataFunTalk
Dec 26, 2022 · Artificial Intelligence

A Review of Causal Inference Methods: Potential Outcomes, Structural Causal Models, and Recent Advances

This article reviews the two main streams of causal inference—potential‑outcome (Rubin) models and structural causal (Pearl) diagrams—covers classic techniques such as A/B testing, instrumental variables, matching, difference‑in‑differences, synthetic controls, matrix completion, heterogeneous treatment effect estimation, and discusses modern machine‑learning‑based approaches and causal discovery algorithms.

A/B testingCausal Inferenceeconometrics
0 likes · 33 min read
A Review of Causal Inference Methods: Potential Outcomes, Structural Causal Models, and Recent Advances
ITPUB
ITPUB
Dec 21, 2022 · Databases

How OpenMLDB Guarantees Real‑Time, Consistent Features for Machine Learning at Scale

This article explains the data and feature engineering challenges of deploying machine learning, introduces OpenMLDB’s open‑source architecture—including offline Spark‑based processing, a high‑availability online engine with dual‑layer memory indexes, snapshot/binlog persistence, and pre‑aggregation techniques—then showcases real‑world case studies and the project’s roadmap.

Feature StoreOpenMLDBReal-time Analytics
0 likes · 15 min read
How OpenMLDB Guarantees Real‑Time, Consistent Features for Machine Learning at Scale
Bilibili Tech
Bilibili Tech
Dec 20, 2022 · Industry Insights

Can Recommendation Algorithms Speed Up Test Case Prioritization? A Bilibili Case Study

This article presents a detailed study on applying recommendation‑system techniques to test case prioritization for Bilibili's mobile apps, describing the problem definition, evaluation metrics, data processing, FM model selection, experimental results, practical deployment, and future research directions.

BilibiliTest Case Prioritizationfactorization machine
0 likes · 13 min read
Can Recommendation Algorithms Speed Up Test Case Prioritization? A Bilibili Case Study
Volcano Engine Developer Services
Volcano Engine Developer Services
Dec 15, 2022 · Artificial Intelligence

How Adaptive Transfer Kernels Boost Low‑Resource Regression: IEEE TPAMI Insights

The paper introduces adaptive transfer kernel learning for transfer Gaussian process regression, defines transfer kernels mathematically, proposes three generalized forms and two improved kernels, proves their positive‑semi‑definiteness, and demonstrates superior performance on low‑resource regression tasks through extensive experiments.

Gaussian ProcessKernel Methodslow-resource regression
0 likes · 9 min read
How Adaptive Transfer Kernels Boost Low‑Resource Regression: IEEE TPAMI Insights
JD Retail Technology
JD Retail Technology
Dec 12, 2022 · Artificial Intelligence

Keynote Presentations from the 2022 Global AI Technology Conference – First Industrial Vision Frontier Forum

The 2022 Global AI Technology Conference’s First Industrial Vision Frontier Forum in Hangzhou gathered leading experts to discuss advances in industrial AI visual defect detection, multimodal pre‑training models, smart meteorology, digital intelligence in retail, third‑generation compound semiconductor detection, meta‑imaging, and broader industrial AI applications, highlighting the future of intelligent manufacturing.

AIIndustrial VisionMeta Imaging
0 likes · 12 min read
Keynote Presentations from the 2022 Global AI Technology Conference – First Industrial Vision Frontier Forum
Tencent Tech
Tencent Tech
Dec 9, 2022 · Artificial Intelligence

How Tencent’s Angel PowerFL Team Dominated iDASH with Homomorphic Encryption

Tencent’s Angel PowerFL team clinched the iDASH homomorphic encryption champion and secured top spots in MPC and SGX tracks, showcasing innovative privacy‑preserving machine‑learning models, CKKS‑based encrypted inference, and a scalable SGX clustering solution that push the boundaries of secure computation.

Homomorphic EncryptionPrivacy ComputingiDASH
0 likes · 5 min read
How Tencent’s Angel PowerFL Team Dominated iDASH with Homomorphic Encryption
DataFunSummit
DataFunSummit
Dec 6, 2022 · Artificial Intelligence

Multimodal Reasoning, Logic Inference, and Machine Learning: An Integrated Survey

This article surveys the development of artificial intelligence from symbolic and connectionist perspectives, covering deductive and inductive reasoning, multimodal and cross‑modal inference, knowledge‑graph reasoning, text and visual understanding, and their applications in causal inference, dialogue consistency, and security vulnerability analysis.

Causal InferenceKnowledge GraphsMultimodal Reasoning
0 likes · 18 min read
Multimodal Reasoning, Logic Inference, and Machine Learning: An Integrated Survey
DataFunTalk
DataFunTalk
Dec 4, 2022 · Artificial Intelligence

Key Insights on Causal Inference: Motivation, Applications, Challenges, and Links to A/B Testing, ML, and Deep Learning

This article summarizes the motivations behind causal inference, its typical business applications such as intelligent decision‑making and prediction, the practical challenges of validation and data, and its relationship with A/B testing, machine learning, and deep learning, providing a concise overview for newcomers.

AB TestingBusiness AnalyticsCausal Inference
0 likes · 10 min read
Key Insights on Causal Inference: Motivation, Applications, Challenges, and Links to A/B Testing, ML, and Deep Learning
ELab Team
ELab Team
Nov 29, 2022 · Frontend Development

How to Build Real-Time Mouse Gesture Recognition with TensorFlow.js

This article explains how to design, implement, and evaluate a mouse gesture recognition system using machine learning and geometric analysis, covering data preprocessing, model training with TensorFlow.js, cosine‑similarity matching, performance optimizations, and extensions to three‑dimensional VR/AR environments.

TensorFlow.jscosine similaritygesture recognition
0 likes · 32 min read
How to Build Real-Time Mouse Gesture Recognition with TensorFlow.js
MaGe Linux Operations
MaGe Linux Operations
Nov 26, 2022 · Artificial Intelligence

The Timeless Foundations of Machine Learning: 6 Core Algorithms Explained

Andrew Ng’s latest AI newsletter article revisits six foundational machine‑learning algorithms—linear regression, logistic regression, gradient descent, neural networks, decision trees, and k‑means clustering—tracing their historical origins, core concepts, and lasting impact on modern AI applications.

Decision TreesLinear RegressionLogistic Regression
0 likes · 20 min read
The Timeless Foundations of Machine Learning: 6 Core Algorithms Explained
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 25, 2022 · Artificial Intelligence

How Multi‑Objective Optimization Boosted Taobao Search’s Coarse Ranking

This report details the multi‑stage architecture of Taobao’s main search, introduces a new global‑transaction hitrate metric, analyzes offline and online evaluation gaps, and presents a series of model, loss‑function, and sampling improvements that together lifted overall conversion by about one percent.

coarse rankinge-commercemachine learning
0 likes · 26 min read
How Multi‑Objective Optimization Boosted Taobao Search’s Coarse Ranking
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Nov 18, 2022 · Artificial Intelligence

Machine Learning-Based Anomaly Detection for Core Business Metrics

The paper proposes a containerized, machine‑learning framework that fuses rule‑based and XGBoost‑driven anomaly detection to monitor daily active users on a cloud music platform, achieving 89 % recall, 81 % precision and up to 74 % recall improvement over traditional threshold methods, while outlining future model refinement and broader metric applicability.

3-sigmaBusiness MetricsHolt-Winters
0 likes · 11 min read
Machine Learning-Based Anomaly Detection for Core Business Metrics
Hulu Beijing
Hulu Beijing
Nov 18, 2022 · Artificial Intelligence

How Video Search Engines Rank Results: From Click Models to Multi‑Goal Optimization

This article explains the architecture of video search engine ranking, covering optimization objectives such as relevance, click‑through rate and watch time, and detailing pointwise, pairwise and listwise learning approaches, model training pipelines, and online serving strategies.

click-through ratemachine learningmulti-objective optimization
0 likes · 17 min read
How Video Search Engines Rank Results: From Click Models to Multi‑Goal Optimization
DataFunTalk
DataFunTalk
Nov 17, 2022 · Artificial Intelligence

Enhance the Visual Representation via Discrete Adversarial Training

The Alibaba AAIG team proposes Discrete Adversarial Training (DAT), which leverages VQGAN‑based discretization to generate natural‑looking adversarial samples that improve visual representation robustness and transferability across classification, self‑supervised learning, and object detection tasks without sacrificing accuracy, achieving new state‑of‑the‑art results on multiple benchmarks.

RobustnessVisual Representationadversarial training
0 likes · 12 min read
Enhance the Visual Representation via Discrete Adversarial Training
Baidu Intelligent Testing
Baidu Intelligent Testing
Nov 15, 2022 · Artificial Intelligence

Risk‑Driven Delivery and Quality Assessment Model for Automated Testing at Baidu

This article describes Baidu's risk‑driven delivery approach, detailing the three current testing challenges, the design of a quality‑assessment system that uses machine‑learning (logistic regression) for risk identification, control and decision, and the resulting improvements in testing efficiency and quality.

AIautomationmachine learning
0 likes · 14 min read
Risk‑Driven Delivery and Quality Assessment Model for Automated Testing at Baidu
Practical DevOps Architecture
Practical DevOps Architecture
Nov 15, 2022 · Fundamentals

Comprehensive Programming Course Curriculum Overview

This article presents a detailed curriculum covering programming fundamentals, web development (HTML, CSS, JavaScript, jQuery), backend frameworks (Flask, Django), database concepts, data analysis with Python (NumPy, pandas, matplotlib, seaborn), machine learning, AI, and related project tutorials, along with a brief promotional note.

CurriculumData Sciencemachine learning
0 likes · 12 min read
Comprehensive Programming Course Curriculum Overview
Baidu Tech Salon
Baidu Tech Salon
Nov 14, 2022 · Artificial Intelligence

How Risk‑Driven Delivery Boosts Test Efficiency with AI‑Powered Quality Models

This article analyzes Baidu's risk‑driven delivery approach, detailing how machine‑learning models identify, control, and decide on testing risks, replace manual judgments, improve test efficiency and quality, and deliver measurable savings and bug interceptions across large‑scale software projects.

Logistic RegressionRisk Managementautomated delivery
0 likes · 13 min read
How Risk‑Driven Delivery Boosts Test Efficiency with AI‑Powered Quality Models
Model Perspective
Model Perspective
Nov 13, 2022 · Fundamentals

9 Compelling Reasons to Choose Python Over Matlab for Modeling

Python outshines Matlab in cost, openness, community support, cross‑platform flexibility, extensive libraries, especially for machine learning, and offers a richer ecosystem of IDEs and flexible coding styles, making it a superior, free alternative for modern scientific and engineering modeling tasks.

MATLABPythonmachine learning
0 likes · 4 min read
9 Compelling Reasons to Choose Python Over Matlab for Modeling
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 11, 2022 · Artificial Intelligence

Award-Winning AI Dubbing System for Film and TV by iQIYI

iQIYI’s award‑winning AI dubbing system, IQDubbing, uses deep‑learning voice conversion, voiceprint, facial and audio processing to cut dubbing time to one‑sixth, slash costs, secure 21 patents, and has already dubbed over 90 films and 200 TV series across multiple languages.

AIawardsdubbing
0 likes · 3 min read
Award-Winning AI Dubbing System for Film and TV by iQIYI
Zuoyebang Tech Team
Zuoyebang Tech Team
Nov 9, 2022 · Artificial Intelligence

Boost Data Annotation Efficiency with VAPAL: Active Learning Meets Virtual Adversarial Perturbation

This article explains how a pool‑based active learning framework that combines uncertainty sampling (using BADGE, ALPS, or virtual adversarial perturbations) with diversity‑driven clustering can dramatically cut labeling costs for Transformer‑based NLP models, and presents experimental results showing VAPAL’s competitive performance and early‑stage advantages.

NLPactive learningdata annotation
0 likes · 10 min read
Boost Data Annotation Efficiency with VAPAL: Active Learning Meets Virtual Adversarial Perturbation
JD Cloud Developers
JD Cloud Developers
Nov 7, 2022 · Artificial Intelligence

Detecting Time‑Series Anomalies Without Thresholds Using LSTM and Unsupervised Fusion

This article presents a threshold‑free anomaly detection framework for streaming time series that combines an LSTM‑based baseline module with an unsupervised detection module, detailing the architecture, training process, data preprocessing, and experimental results that demonstrate superior accuracy and F1 scores.

LSTMTime SeriesUnsupervised Learning
0 likes · 15 min read
Detecting Time‑Series Anomalies Without Thresholds Using LSTM and Unsupervised Fusion
Airbnb Technology Team
Airbnb Technology Team
Nov 3, 2022 · Artificial Intelligence

T-LEAF: A Taxonomy Learning and Evaluation Framework for Airbnb Community Support Classification System

The T‑LEAF framework introduces quantitative metrics for coverage, usefulness, and consistency to iteratively develop Airbnb’s unified Contact‑Reason taxonomy, enabling faster feedback loops, reducing “Other” classifications, and improving both human annotation agreement and machine‑learning prediction accuracy in production.

ClassificationCommunity Supportevaluation framework
0 likes · 14 min read
T-LEAF: A Taxonomy Learning and Evaluation Framework for Airbnb Community Support Classification System
Model Perspective
Model Perspective
Oct 30, 2022 · Artificial Intelligence

How ALE Plots Overcome Partial Dependence Limitations in ML

The Accumulated Local Effect (ALE) plot, introduced by Daniel W. Apley in 2016, addresses the correlation issue inherent in Partial Dependence Plots, offering unbiased, faster, and more accurate feature impact visualizations for machine‑learning models, especially in domains like financial risk control.

ALEFeature Importancemachine learning
0 likes · 9 min read
How ALE Plots Overcome Partial Dependence Limitations in ML
Model Perspective
Model Perspective
Oct 26, 2022 · Artificial Intelligence

Master Machine Learning Algorithms: Types, Python Code & Real-World Examples

This article categorizes machine learning algorithms into supervised, unsupervised, and reinforcement learning, then details ten common algorithms—including linear regression, logistic regression, decision trees, SVM, Naive Bayes, K‑NN, K‑means, random forest, and dimensionality reduction—accompanied by clear Python code examples and illustrative diagrams.

AlgorithmsPythonUnsupervised Learning
0 likes · 14 min read
Master Machine Learning Algorithms: Types, Python Code & Real-World Examples
Model Perspective
Model Perspective
Oct 21, 2022 · Artificial Intelligence

How Explainable Boosting Machines (EBM) Combine Accuracy and Interpretability

Explainable Boosting Machines (EBM) integrate boosting trees into generalized additive models, using the FAST algorithm to efficiently detect high‑impact pairwise interactions, delivering near‑state‑of‑the‑art accuracy while preserving strong global and local interpretability, as demonstrated on breast‑cancer data.

Explainable Boosting MachineFAST algorithmgeneralized additive model
0 likes · 10 min read
How Explainable Boosting Machines (EBM) Combine Accuracy and Interpretability
DataFunSummit
DataFunSummit
Oct 19, 2022 · Artificial Intelligence

Series Six of the Integer Intelligence Autonomous Driving Dataset Collection – Overview and Highlights

This article presents a comprehensive overview of several publicly available autonomous driving datasets, focusing on Series Six of the Integer Intelligence collection, which includes StreetLearn, UTBM RoboCar, Multi‑Vehicle Stereo Event Camera, comma2k19, the Annotated Laser Dataset, Ford, and Oxford RobotCar, detailing their sources, download links, publication years, key features, and research relevance.

autonomous drivingcomputer visiondatasets
0 likes · 10 min read
Series Six of the Integer Intelligence Autonomous Driving Dataset Collection – Overview and Highlights
21CTO
21CTO
Oct 18, 2022 · Information Security

Can Google’s New KataOS Deliver a Provably Secure Embedded AI Platform?

Google’s experimental KataOS, built with Rust atop the formally verified seL4 microkernel and paired with the Sparrow reference implementation, aims to provide a provably secure operating system for embedded machine‑learning workloads, highlighting collaborations, architectural choices, and its place among historic microkernels.

KataOSMicrokernelRust
0 likes · 5 min read
Can Google’s New KataOS Deliver a Provably Secure Embedded AI Platform?
Model Perspective
Model Perspective
Oct 17, 2022 · Fundamentals

Unlocking Bayes' Theorem: From Basics to Real-World Applications

Bayes' theorem, a cornerstone of probability theory, relates prior knowledge, likelihood, and evidence to compute posterior probabilities, highlighting why prior and likelihood differ, and explaining concepts such as prior, likelihood, posterior, and evidence with intuitive examples and their relevance to sequential data analysis.

Bayes theoremmachine learningposterior
0 likes · 5 min read
Unlocking Bayes' Theorem: From Basics to Real-World Applications
Baidu Geek Talk
Baidu Geek Talk
Oct 17, 2022 · Artificial Intelligence

OCR Technology: PaddleOCR and Paddle.js Integration

The article explains OCR fundamentals and details how Baidu’s open‑source PaddleOCR suite can be converted and run in browsers via the @paddlejs‑models/ocr SDK, describing model initialization, detection and CRNN‑based recognition pipelines, and presenting benchmark results that show the newer ch_PP‑OCRv2 model achieving higher accuracy and faster inference than the mobile variant.

AIOCRPaddle.js
0 likes · 9 min read
OCR Technology: PaddleOCR and Paddle.js Integration
Xianyu Technology
Xianyu Technology
Oct 13, 2022 · Artificial Intelligence

Design of a Generalized Recommendation Platform for Xianyu Marketplace

The article presents a generalized recommendation platform for Xianyu Marketplace that consolidates feature processing, candidate generation, recall, scoring, and experiment management into shared core components, enabling rapid onboarding of new scenarios, reducing engineering effort, and delivering over 8% CTR lift and 10% more impressions.

Platform ArchitectureXianyumachine learning
0 likes · 12 min read
Design of a Generalized Recommendation Platform for Xianyu Marketplace
DataFunTalk
DataFunTalk
Oct 12, 2022 · Artificial Intelligence

Feature Embedding Modeling for Recommendation Systems: Techniques, Models, and Practical Insights from Weibo

This article presents a comprehensive overview of feature embedding modeling in recommendation systems, discussing the necessity of feature modeling, three technical directions (gate threshold, variable‑length embeddings, and enrichment), detailed descriptions of models such as FiBiNet, FiBiNet++, ContextNet, and MaskNet, experimental findings, and a Q&A session that addresses practical challenges and future work.

CTR modelsFeature ImportanceWeibo
0 likes · 34 min read
Feature Embedding Modeling for Recommendation Systems: Techniques, Models, and Practical Insights from Weibo
MaGe Linux Operations
MaGe Linux Operations
Oct 11, 2022 · Artificial Intelligence

Are Statistics and Machine Learning Really the Same? Uncover the Real Differences

While many claim that machine learning is merely statistics with a flashy veneer, this article explores the nuanced distinctions between the two fields—examining their goals, methodologies, and examples such as linear regression—to clarify why they are related yet fundamentally different.

Linear Regressionmachine learningmodel evaluation
0 likes · 17 min read
Are Statistics and Machine Learning Really the Same? Uncover the Real Differences
Programmer DD
Programmer DD
Oct 11, 2022 · Artificial Intelligence

Why Is Google Translate Suddenly Failing in Mainland China?

Recent reports reveal that Google Translate’s mainland China domain translate.google.cn now redirects to a search page and ultimately to the Hong Kong site, rendering the service inaccessible for Chinese users, while underlying issues stem from censored training data, outdated infrastructure, and past attempts to revive Google’s search in China.

AIChinaGoogle Translate
0 likes · 5 min read
Why Is Google Translate Suddenly Failing in Mainland China?
Model Perspective
Model Perspective
Oct 10, 2022 · Fundamentals

Matrix-to-Matrix Derivatives: Definitions, Differential Method & Examples

This article explains the definition of matrix‑to‑matrix derivatives, introduces the vectorization‑based differential approach using Kronecker products, presents key matrix‑vectorization properties, and walks through detailed examples illustrating how to compute such derivatives, highlighting their role and limitations in machine‑learning optimization.

Kronecker productderivativemachine learning
0 likes · 5 min read
Matrix-to-Matrix Derivatives: Definitions, Differential Method & Examples
Model Perspective
Model Perspective
Oct 9, 2022 · Artificial Intelligence

Why Model Interpretability Matters: Tackling the Black‑Box Problem in AI

This article explains the challenges of black‑box machine‑learning models, illustrates real‑world banking examples, and introduces explainable AI techniques such as intrinsic vs. post‑hoc and local vs. global explanations to improve trust, safety, and fairness.

AI ethicsblack-box modelsmachine learning
0 likes · 13 min read
Why Model Interpretability Matters: Tackling the Black‑Box Problem in AI
Model Perspective
Model Perspective
Oct 8, 2022 · Artificial Intelligence

How Ensemble Learning Boosts Model Performance: A Comprehensive Overview

Ensemble learning combines multiple individual models—either homogeneous or heterogeneous—using strategies such as boosting, bagging, averaging, voting, or stacking to create a stronger learner, and this article explains its principles, key algorithms, and combination methods in detail.

BaggingStackingmachine learning
0 likes · 8 min read
How Ensemble Learning Boosts Model Performance: A Comprehensive Overview
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Oct 8, 2022 · Artificial Intelligence

Wasserstein GAN (WGAN): Theory and Hands‑On Implementation

This article explains why traditional GANs suffer from training instability, introduces the Wasserstein (Earth‑Mover) distance as a smoother alternative, derives the WGAN objective, discusses Lipschitz constraints, and provides practical PyTorch code modifications to convert a vanilla GAN into a stable WGAN.

GaNPyTorchWGAN
0 likes · 21 min read
Wasserstein GAN (WGAN): Theory and Hands‑On Implementation
Model Perspective
Model Perspective
Oct 7, 2022 · Artificial Intelligence

Master Gradient Descent: From Intuition to Advanced Variants

This comprehensive guide explains the mathematical foundation, intuitive intuition, algorithmic steps, tuning strategies, and variants of gradient descent, comparing it with other optimization methods and illustrating its use in machine‑learning models such as linear regression.

Linear RegressionOptimizationgradient descent
0 likes · 14 min read
Master Gradient Descent: From Intuition to Advanced Variants
Model Perspective
Model Perspective
Oct 6, 2022 · Artificial Intelligence

Mastering the Chain Rule for Vector‑to‑Vector and Scalar‑to‑Matrix Derivatives

This article explains the chain rule for vector‑to‑vector derivatives, scalar‑to‑multiple‑vector and scalar‑to‑matrix cases, illustrates how to handle dimensional compatibility, provides concrete examples such as least‑squares optimization, and summarizes four key matrix‑vector derivative conclusions for efficient machine‑learning calculations.

Derivativeschain rulemachine learning
0 likes · 5 min read
Mastering the Chain Rule for Vector‑to‑Vector and Scalar‑to‑Matrix Derivatives
MaGe Linux Operations
MaGe Linux Operations
Oct 1, 2022 · Artificial Intelligence

11 Powerful Feature Selection Techniques Every Data Scientist Should Master

This guide walks through a comprehensive set of feature‑selection strategies—from removing unused or missing columns to handling multicollinearity, low‑variance features, and using PCA—complete with Python code examples and visualizations to help you build leaner, more interpretable machine‑learning models.

Pythondata preprocessingdimensionality reduction
0 likes · 18 min read
11 Powerful Feature Selection Techniques Every Data Scientist Should Master
Tencent Advertising Technology
Tencent Advertising Technology
Sep 23, 2022 · Industry Insights

How Tencent Ads’ CONFLUX and MVKE Algorithms Boost Conversion – Insights from KDD2022

Tencent Ads hosted two KDD2022‑focused live sessions showcasing the CONFLUX and MVKE algorithms, explaining their technical foundations, real‑world impact on billions of ad impressions, and answering audience questions about brand versus performance ads, validation methods, and future research directions.

Advertising TechnologyConversion OptimizationKDD2022
0 likes · 6 min read
How Tencent Ads’ CONFLUX and MVKE Algorithms Boost Conversion – Insights from KDD2022
Alimama Tech
Alimama Tech
Sep 21, 2022 · Artificial Intelligence

EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search

The paper introduces EXTR, a Transformer‑based CTR prediction model that jointly encodes diverse externalities from surrounding organic results and ads and infers missing ad placements via a Potential Allocation Generator, achieving superior AUC, COPC and LogLoss on Taobao data and deployment in Alibaba’s advertising system.

AdvertisingExternalitiesTransformer
0 likes · 11 min read
EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search
NetEase Game Operations Platform
NetEase Game Operations Platform
Sep 19, 2022 · Artificial Intelligence

Applying AIOps to Game Operations: Roadmap, Anomaly Detection, and Fault Localization

This article describes NetEase's AIOps journey for game operations, explaining the Gartner definition of intelligent operations, the implementation roadmap, detailed anomaly‑detection techniques for business, performance, and log data, and a comprehensive fault‑localization workflow that combines resource, code, and historical analysis.

AIOpsanomaly detectionfault localization
0 likes · 12 min read
Applying AIOps to Game Operations: Roadmap, Anomaly Detection, and Fault Localization
Model Perspective
Model Perspective
Sep 18, 2022 · Artificial Intelligence

How Bayesian Linear Regression Reveals Uncertainty in Model Parameters

This article explains Bayesian linear regression, describing its probabilistic treatment of weights, prior and posterior computation, MAP and numerical solutions, and how it enables uncertainty quantification, online learning, and model comparison through Bayes factors.

Bayesian inferenceLinear RegressionMAP estimation
0 likes · 9 min read
How Bayesian Linear Regression Reveals Uncertainty in Model Parameters
DataFunSummit
DataFunSummit
Sep 17, 2022 · Artificial Intelligence

Advertising Targeting: From Glory to Sunset – Technical Reflections and Future Directions

This article reviews the evolution of advertising targeting technology, recounts its historical impact, analyzes the underlying machine‑learning models—from early Python classifiers to XGBoost‑Spark, DNN, and attention‑based wide‑deep systems—and discusses why the technique is now waning while outlining possible future integrations with large‑scale recall and cost‑aware optimization.

CTRmachine learningtargeting
0 likes · 26 min read
Advertising Targeting: From Glory to Sunset – Technical Reflections and Future Directions
ITPUB
ITPUB
Sep 15, 2022 · Artificial Intelligence

Why Precise Feature Engineering Still Matters in Recommendation Systems

In the era of deep learning, feature engineering remains crucial for recommendation and search advertising because it bridges raw relational data and models, improves performance, reduces complexity, and handles high‑cardinality, large‑scale, and time‑sensitive scenarios with robust transformations and statistical encoding.

AIdata preprocessingfeature engineering
0 likes · 20 min read
Why Precise Feature Engineering Still Matters in Recommendation Systems
DataFunSummit
DataFunSummit
Sep 13, 2022 · Artificial Intelligence

Elegant Integration of Ads in Search: An Analysis of Baidu's Mobius Approach

This article examines how search advertising can be seamlessly blended with user queries by balancing relevance and revenue, reviewing the evolution from portal indexing to recommendation systems, and detailing Baidu's Mobius framework that jointly optimizes relevance, CTR, and eCPM in a unified pipeline.

CTRMobiusUser Intent
0 likes · 24 min read
Elegant Integration of Ads in Search: An Analysis of Baidu's Mobius Approach
Python Crawling & Data Mining
Python Crawling & Data Mining
Sep 11, 2022 · Big Data

How Tencent Built Its Massive Big Data Platform Over a Decade

Over more than ten years, Tencent evolved its big data infrastructure through three generations—from early Hadoop-based offline processing, to a hybrid real‑time Spark/Storm system, and finally to a self‑developed, open‑source machine‑learning platform—highlighting the shift from “borrowed” solutions to fully proprietary, AI‑ready architectures.

Architecturedata warehousemachine learning
0 likes · 10 min read
How Tencent Built Its Massive Big Data Platform Over a Decade