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163 articles · Page 2 of 2
Baobao Algorithm Notes
Baobao Algorithm Notes
Jun 5, 2024 · Artificial Intelligence

Is GLM‑4‑9B the New Powerhouse? A Deep Dive into Its Performance and Usage

This article reviews the open‑source 9‑billion‑parameter GLM‑4‑9B model, covering installation, quick‑start inference code, quirky Chinese riddles that highlight its strengths over GPT‑4, extensive benchmark tables for dialogue, multilingual, tool‑calling and multimodal tasks, and its broader impact on the Chinese AI ecosystem.

AIGLM-4-9BMultimodal
0 likes · 14 min read
Is GLM‑4‑9B the New Powerhouse? A Deep Dive into Its Performance and Usage
NewBeeNLP
NewBeeNLP
May 16, 2024 · Artificial Intelligence

How Large Language Models Transform Advertising Copy Generation

This article examines the adoption of large language models for intelligent advertising copy creation, detailing business challenges, model selection criteria, training data preparation, fine‑tuning methods, performance evaluation, deployment results, while highlighting the trade‑offs between model size, cost, and output quality.

AI marketingadvertising copyfine-tuning
0 likes · 20 min read
How Large Language Models Transform Advertising Copy Generation
NewBeeNLP
NewBeeNLP
May 15, 2024 · Artificial Intelligence

How Large Language Models and Knowledge Graphs Can Boost Each Other

This talk reviews recent advances in large language models, compares them with knowledge graphs, explores how LLMs enhance knowledge extraction and completion, examines how knowledge graphs aid LLM evaluation and safe deployment, and outlines future interactive integration between the two technologies.

AI researchKnowledge ExtractionKnowledge Graphs
0 likes · 13 min read
How Large Language Models and Knowledge Graphs Can Boost Each Other
IT Services Circle
IT Services Circle
May 1, 2024 · Artificial Intelligence

Summary of Andrew Ng’s AI Agent Talk: Models, Workflows, and Design Patterns

The article summarizes Andrew Ng’s presentation on AI agents, contrasting traditional single‑prompt large‑model usage with iterative agent‑based workflows, reporting experimental accuracy gains, and outlining four agent design patterns—reflection, tool use, planning, and multi‑agent collaboration—while discussing practical trade‑offs such as latency and token speed.

AI AgentLarge Language ModelPrompt Engineering
0 likes · 7 min read
Summary of Andrew Ng’s AI Agent Talk: Models, Workflows, and Design Patterns
DataFunTalk
DataFunTalk
Apr 21, 2024 · Artificial Intelligence

Guidelines for Building Domain-Specific Large Models: Dataset Construction, Training Methods, Evaluation, and Hardware Benchmarking

This article presents a comprehensive guide on constructing domain-specific large language models, covering the differences from general models, how to build high‑quality domain datasets, selecting appropriate training methods, designing validation sets, evaluating model capabilities, and benchmarking domestic hardware performance.

AIDataset ConstructionLarge Language Model
0 likes · 20 min read
Guidelines for Building Domain-Specific Large Models: Dataset Construction, Training Methods, Evaluation, and Hardware Benchmarking
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Feb 23, 2024 · Artificial Intelligence

Google’s Open‑Source Gemma Large Language Model: Architecture, Performance, and Community Reception

Google has released the open‑source Gemma LLM series (2B and 7B parameters) built on Gemini‑style architecture, offering free, commercial‑ready models that run on notebooks, support JAX/PyTorch/TensorFlow, outperform many open‑source peers, and have quickly sparked extensive community testing and discussion.

GemmaGoogleJAX
0 likes · 5 min read
Google’s Open‑Source Gemma Large Language Model: Architecture, Performance, and Community Reception
DataFunTalk
DataFunTalk
Feb 10, 2024 · Artificial Intelligence

Mitigating Hallucinations in Large Language Model Applications with Knowledge Graphs

This article examines the challenges of using large language models for industry Q&A, defines hallucination phenomena, evaluates their causes and impact, and proposes a set of strategies—including high‑quality fine‑tuning data, honest alignment, advanced decoding, and external knowledge‑graph augmentation—to reduce hallucinations and improve answer reliability.

HallucinationKnowledge Graphlarge language models
0 likes · 21 min read
Mitigating Hallucinations in Large Language Model Applications with Knowledge Graphs
Baidu Geek Talk
Baidu Geek Talk
Jan 15, 2024 · Artificial Intelligence

Qianfan Large Model Platform: Making Large Models Accessible - Baidu's Latest Work on Model Fine-tuning and Deployment

Baidu’s Qianfan Large Model Platform provides a one‑stop enterprise solution with 54 pre‑installed models, advanced fine‑tuning, comprehensive evaluation metrics, and optimized deployment that cuts costs up to 90% and boosts throughput 3‑5×, enabling rapid, affordable AI application development.

AI-native applicationsBaidu QianfanLarge Model Platform
0 likes · 12 min read
Qianfan Large Model Platform: Making Large Models Accessible - Baidu's Latest Work on Model Fine-tuning and Deployment
政采云技术
政采云技术
Oct 10, 2023 · Artificial Intelligence

Predicting Membership Purchase with Logistic Regression: Feature Engineering, Model Training, Evaluation, and Deployment

This article presents a complete workflow for predicting whether users will purchase a membership using logistic regression, covering data collection, feature selection, handling imbalanced samples, model training, hyper‑parameter tuning, threshold optimization, evaluation metrics such as accuracy, precision, recall, AUC, lift, and finally deployment on a big‑data platform with PySpark.

Big Datafeature engineeringlogistic regression
0 likes · 17 min read
Predicting Membership Purchase with Logistic Regression: Feature Engineering, Model Training, Evaluation, and Deployment
NetEase LeiHuo Testing Center
NetEase LeiHuo Testing Center
Sep 22, 2023 · Artificial Intelligence

Understanding Large Language Models and Prompt Engineering: A Practical Guide

This article provides an introductory overview of large language models (LLMs), compares popular models, explains their underlying principles, and offers practical guidance on prompt engineering, model evaluation, usage tips, and safety considerations, helping readers effectively select and apply LLMs in various scenarios.

AILLMPrompt Engineering
0 likes · 44 min read
Understanding Large Language Models and Prompt Engineering: A Practical Guide
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 18, 2023 · Artificial Intelligence

Unlocking Domain-Specific Large Model Training: Proven Tricks and Pitfalls

This article shares practical techniques for domain‑specific large model continue pre‑training, including data selection, mixing ratios with general data, multi‑task instruction pre‑training, resource‑aware fine‑tuning strategies, evaluation set design, vocabulary considerations, and deployment constraints for 7‑13B models.

AI researchSFTmodel evaluation
0 likes · 9 min read
Unlocking Domain-Specific Large Model Training: Proven Tricks and Pitfalls
21CTO
21CTO
Jul 23, 2023 · Artificial Intelligence

What Nathan Lambert Reveals About Meta’s Llama 2: Key Insights and Technical Deep‑Dive

This article translates and analyzes Nathan Lambert’s commentary on Meta’s Llama 2 paper, detailing the model’s architecture, training data, RLHF pipeline, reward models, evaluation methods, safety improvements, licensing terms, and the broader implications for open‑source large language models.

Llama 2Meta AIOpen‑source LLM
0 likes · 22 min read
What Nathan Lambert Reveals About Meta’s Llama 2: Key Insights and Technical Deep‑Dive
DataFunSummit
DataFunSummit
May 31, 2023 · Artificial Intelligence

Evolution of Face Detection Techniques: Datasets, Research Directions, and Future Work

This article reviews the evolution of face detection, covering the Widely‑Face dataset, major research directions such as feature fusion, label assignment, auxiliary supervision, anchor‑free methods, NAS‑based designs, summarizes key papers from S3FD to MogFace, introduces ModelScope implementations, and outlines future challenges and opportunities.

AI researchcomputer visiondatasets
0 likes · 13 min read
Evolution of Face Detection Techniques: Datasets, Research Directions, and Future Work
GuanYuan Data Tech Team
GuanYuan Data Tech Team
May 25, 2023 · Artificial Intelligence

How to Build a Comprehensive ML Model Quality Assessment Framework

This article explains why and how to evaluate machine learning model quality through a structured framework that covers data validation, feature checks, and algorithm testing, helping ensure accuracy, reliability, and maintainability before deployment.

AI GovernanceData Validationfeature engineering
0 likes · 19 min read
How to Build a Comprehensive ML Model Quality Assessment Framework
Full-Stack Trendsetter
Full-Stack Trendsetter
May 18, 2023 · Artificial Intelligence

How 360 and ChatGLM Are Building China’s “Microsoft + OpenAI” Large‑Model Duo

On May 16, 360 and Zhipu AI announced a strategic partnership to co‑develop the trillion‑parameter models 360GLM and 360GPT, positioning them as China’s answer to Microsoft‑OpenAI by combining large‑scale pre‑training, bilingual capabilities, and integration with 360’s search and browser ecosystem.

360AI collaborationChatGLM
0 likes · 7 min read
How 360 and ChatGLM Are Building China’s “Microsoft + OpenAI” Large‑Model Duo
DataFunTalk
DataFunTalk
Feb 21, 2023 · Artificial Intelligence

Analysis of Large Language Models: Capabilities, Training Methods, and Limitations – Summary of Prof. Qiu Xipeng’s Lecture

Prof. Qiu Xipeng’s lecture provides a comprehensive overview of large language models—from their historical development and architectural foundations to key technologies such as in‑context learning, chain‑of‑thought, and natural‑instruction learning, as well as RLHF training, capability evaluation, and current limitations of ChatGPT.

Chain-of-ThoughtChatGPTIn-Context Learning
0 likes · 15 min read
Analysis of Large Language Models: Capabilities, Training Methods, and Limitations – Summary of Prof. Qiu Xipeng’s Lecture
Python Programming Learning Circle
Python Programming Learning Circle
Dec 7, 2022 · Artificial Intelligence

Predicting the 2022 FIFA World Cup Champion Using Machine Learning Models

This article details a data‑mining project that uses historical World Cup match data, extensive feature engineering, and various machine‑learning algorithms—including neural networks, logistic regression, SVM, decision trees, and random forests—to predict the champion of the 2022 tournament, while analyzing model errors and proposing improvements.

Data preprocessingWorld Cupclassification
0 likes · 7 min read
Predicting the 2022 FIFA World Cup Champion Using Machine Learning Models
Laiye Technology Team
Laiye Technology Team
Nov 23, 2022 · Artificial Intelligence

Design and Practices of a Data‑Driven OCR Testing System

The article describes Laiye's shift to a data‑driven deep‑learning workflow and presents the design, macro‑ and micro‑analysis features, visual diff tools, distributed tracing, and code examples of their OCR testing system that accelerate model evaluation and iterative optimization.

AIData‑DrivenMLOps
0 likes · 11 min read
Design and Practices of a Data‑Driven OCR Testing System
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
HaoDF Tech Team
HaoDF Tech Team
Oct 8, 2022 · Artificial Intelligence

Exploring Transformer Technology and Its Applications in NLP, Computer Vision, and OCR at Haodf.com

This article introduces the Transformer architecture, explains its attention mechanism, details its adaptations for natural language processing, computer vision, and OCR tasks, and presents experimental results of various models such as BERT, ELECTRA, Swin Transformer, and CRNN-BCN on large-scale medical data from Haodf.com.

NLPOCRSwin Transformer
0 likes · 39 min read
Exploring Transformer Technology and Its Applications in NLP, Computer Vision, and OCR at Haodf.com
Model Perspective
Model Perspective
Aug 25, 2022 · Artificial Intelligence

Mastering Regression: Key Assumptions, Metrics, and Model Evaluation

This article explains the fundamental assumptions of linear regression, compares linear and nonlinear models, discusses multicollinearity, outliers, regularization, heteroscedasticity, VIF, stepwise regression, and reviews essential evaluation metrics such as MAE, MSE, RMSE, R² and Adjusted R².

MetricsRegressionlinear regression
0 likes · 12 min read
Mastering Regression: Key Assumptions, Metrics, and Model Evaluation
Model Perspective
Model Perspective
Jun 17, 2022 · Artificial Intelligence

What Is Classification in Data Mining? Types, Models, and Key Applications

The article explains classification as a data‑analysis task that builds models to assign new observations to predefined categories, outlines its implementation steps, describes various data types (boolean, nominal, ordinal, continuous, discrete), presents common machine‑learning classifiers such as decision trees and neural networks, and highlights practical applications like crime detection, disease risk prediction, and credit assessment.

classificationdata miningmachine learning
0 likes · 5 min read
What Is Classification in Data Mining? Types, Models, and Key Applications
DaTaobao Tech
DaTaobao Tech
May 13, 2022 · Big Data

Taobao Big Data Model Governance and DataWorks Co‑development

Taobao’s rapidly expanding technical data system faced naming inconsistencies, low table reuse, and costly, inefficient data usage, prompting a joint effort with DataWorks to digitize model evaluation, enforce standardized governance, deliver intelligent end‑to‑end modeling tools, and launch a development assistant, resulting in a health‑monitoring dashboard, upgraded data maps, and a roadmap for further automation and architecture refinement.

Big DataData GovernanceData Platform
0 likes · 12 min read
Taobao Big Data Model Governance and DataWorks Co‑development
Python Programming Learning Circle
Python Programming Learning Circle
Dec 21, 2021 · Artificial Intelligence

Introduction to CatBoost: Features, Advantages, and Practical Implementation

This article introduces CatBoost, outlines its key advantages such as automatic handling of categorical features, symmetric trees, and feature combination, and provides a step‑by‑step Python tutorial—including data preparation, model training, visualization, and feature importance analysis—using a CTR prediction dataset.

CatBoostPythonboosting
0 likes · 5 min read
Introduction to CatBoost: Features, Advantages, and Practical Implementation
DataFunTalk
DataFunTalk
Sep 27, 2021 · Artificial Intelligence

Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Evaluation

This article introduces the fundamentals of transfer learning, explains its theoretical foundations and formulas, and demonstrates how multi‑task learning and domain‑adaptation techniques are applied to financial risk‑control scenarios to overcome label scarcity, distribution shift, and model complexity challenges, presenting detailed experimental results and analysis.

Domain AdaptationMulti-Task Learningdeep learning
0 likes · 17 min read
Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Evaluation
HelloTech
HelloTech
Aug 27, 2021 · Artificial Intelligence

Algorithm Testing Practices and Machine Learning Foundations at Hello

The Hello algorithm testing team outlines its workflow—from data collection and cleaning through model training, evaluation, and deployment—while teaching machine‑learning fundamentals, detailing company‑wide use cases, defining key terms, and describing four testing capability dimensions covering data quality, service reliability, model performance, and system engineering.

AIData Qualityalgorithm testing
0 likes · 12 min read
Algorithm Testing Practices and Machine Learning Foundations at Hello
DataFunTalk
DataFunTalk
Jul 6, 2021 · Artificial Intelligence

Automated End-to-End Model Iteration in Intelligent Risk Control Systems

This article explains how an intelligent risk control system can achieve fully automated, end-to-end model iteration, detailing the multi-layer architecture, sample and feature selection, automated training, evaluation, scoring, deployment, and the efficiency gains compared with manual processes.

AIfeature engineeringmodel automation
0 likes · 20 min read
Automated End-to-End Model Iteration in Intelligent Risk Control Systems
DataFunTalk
DataFunTalk
May 17, 2021 · Artificial Intelligence

Comprehensive Overview of Machine Learning Model Evaluation Metrics

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

AUCAccuracyKS
0 likes · 19 min read
Comprehensive Overview of Machine Learning Model Evaluation Metrics
Python Programming Learning Circle
Python Programming Learning Circle
May 8, 2021 · Artificial Intelligence

Top 10 New Features in Scikit‑learn 0.24

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

Data preprocessingPythonScikit-learn
0 likes · 8 min read
Top 10 New Features in Scikit‑learn 0.24
DataFunTalk
DataFunTalk
May 1, 2021 · Artificial Intelligence

How to Evaluate Machine Learning Model Performance Before Production Deployment

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

HR attritionROC AUCevidently
0 likes · 17 min read
How to Evaluate Machine Learning Model Performance Before Production Deployment
Didi Tech
Didi Tech
Apr 26, 2021 · Artificial Intelligence

Model Quality Assurance Practices at DiDi: Challenges, Solutions, and Evaluation

DiDi’s shift to machine‑learning‑driven ride‑hailing services revealed major QA challenges—data and feature quality, model verification, and API stability—prompting a four‑pillar framework and a unified “Strategy‑Center 1.0” platform to systematically monitor, evaluate, and improve model effectiveness, bias paths, and feature discovery.

AI OperationsFeature EvaluationModel Quality Assurance
0 likes · 8 min read
Model Quality Assurance Practices at DiDi: Challenges, Solutions, and Evaluation
Sohu Tech Products
Sohu Tech Products
Apr 14, 2021 · Artificial Intelligence

Evaluating Machine Learning Model Performance Before Production: An Employee Attrition Case Study

This tutorial walks through a complete workflow for assessing machine‑learning models—using a Kaggle HR attrition dataset, comparing Random Forest and Gradient Boosting via ROC‑AUC, precision, recall and segment analysis with the Evidently library—to decide which model is ready for production deployment.

PrecisionROC AUCemployee attrition
0 likes · 17 min read
Evaluating Machine Learning Model Performance Before Production: An Employee Attrition Case Study
58 Tech
58 Tech
Dec 25, 2020 · Artificial Intelligence

User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model

This article presents a comprehensive study on C‑side user identity recognition for internet platforms, addressing cold‑start and sample‑scarcity challenges by comparing keyword matching, XGBoost, TextCNN, a fusion model, and an improved Wide & Deep architecture, showing that the latter achieves the highest F1 score of 80.67%.

TextCNNUser IdentityWide&Deep
0 likes · 13 min read
User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model
JD Retail Technology
JD Retail Technology
Sep 28, 2020 · Artificial Intelligence

Why AI Testing Is Still Painful and How to Solve It

The talk explores the current pain points of AI testing, outlines data‑quality analysis methods, highlights critical ETL and model‑testing considerations, and shares practical case studies and platform designs to improve machine‑learning quality assurance.

AI testingData QualityETL
0 likes · 5 min read
Why AI Testing Is Still Painful and How to Solve It
DataFunTalk
DataFunTalk
Sep 26, 2020 · Artificial Intelligence

What Makes a Good Model? Understanding Model Concepts, Types, and Evaluation in Data Science

This article explores the definition of a model, distinguishes business, data, and function models, discusses criteria for a good model—including performance, fidelity to real‑world relationships, and interpretability—and examines why a universal model does not exist, all within the context of data science and AI.

AIModeldata science
0 likes · 18 min read
What Makes a Good Model? Understanding Model Concepts, Types, and Evaluation in Data Science
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 28, 2020 · Artificial Intelligence

Avoid Common Pitfalls in Industrial Text Classification: A Practical Guide

This comprehensive guide examines real‑world text classification projects, covering label taxonomy design, data scarcity solutions, efficient annotation, new‑class discovery, algorithm selection, evaluation metrics, OOV handling, model evolution, rule‑model integration, performance‑boosting tricks, and inference under resource constraints.

NLPText Classificationadversarial validation
0 likes · 15 min read
Avoid Common Pitfalls in Industrial Text Classification: A Practical Guide
DataFunTalk
DataFunTalk
Aug 14, 2020 · Artificial Intelligence

Illustrated Guide to the Complete Machine Learning Workflow

This article presents a hand‑drawn, illustrated walkthrough of the entire machine‑learning pipeline—from dataset definition, exploratory data analysis, preprocessing, and data splitting to model building, algorithm selection, hyper‑parameter tuning, feature selection, and evaluation for both classification and regression tasks.

Data preprocessingRegressionclassification
0 likes · 17 min read
Illustrated Guide to the Complete Machine Learning Workflow
DataFunTalk
DataFunTalk
Apr 12, 2020 · Artificial Intelligence

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

In this article, Wang Zhe addresses fifteen common questions about recommendation systems, covering topics such as building cross‑domain knowledge, the role of deep reinforcement learning, handling sparse or low‑sample data, offline‑online evaluation, knowledge graphs, graph neural networks, model interpretability, large‑scale ID embedding, and career advice for engineers.

Graph Neural NetworkKnowledge GraphRecommendation Systems
0 likes · 14 min read
Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems
37 Interactive Technology Team
37 Interactive Technology Team
Apr 28, 2019 · Artificial Intelligence

Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring

By applying XGBoost‑based regression models that are retrained daily on two‑week order data and tuned per sub‑package, the mobile‑game recharge monitoring system predicts 10‑minute order volumes, sharply cuts false alarms from hundreds to dozens, and delivers precise, scalable alerts for game operations.

Anomaly DetectionMobile GamingRegression
0 likes · 8 min read
Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 6, 2019 · Artificial Intelligence

How Deep Learning Unwarps Curved Document Images for Better OCR

This article explores how deep‑learning‑based image dewarping techniques, from traditional hardware methods to modern U‑Net, Stacked U‑Net and Dilated U‑Net architectures, can correct warped document photos, improve OCR accuracy, and support intelligent verification in high‑throughput business scenarios.

OCRU‑Netdeep learning
0 likes · 19 min read
How Deep Learning Unwarps Curved Document Images for Better OCR
HomeTech
HomeTech
Feb 28, 2019 · Artificial Intelligence

How to Systematically Test and Monitor AI Models in Large‑Scale Production

This article presents a comprehensive approach to testing, automating, and monitoring AI prediction models in a high‑traffic environment, covering background, challenges, evaluation metrics, data sampling methods, automated test scripts, and online monitoring to ensure model accuracy, performance, and reliability.

AI testingAutomationBig Data
0 likes · 13 min read
How to Systematically Test and Monitor AI Models in Large‑Scale Production
JD Tech Talk
JD Tech Talk
Jan 16, 2019 · Artificial Intelligence

Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results

This article presents a detailed case study of building a purchase‑user prediction model by integrating Convolutional Neural Networks for feature extraction with Long Short‑Term Memory networks for time‑series forecasting, covering background, model structure, data augmentation, experimental results, and business impact.

CNNLSTMdeep learning
0 likes · 10 min read
Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results
Qunar Tech Salon
Qunar Tech Salon
Sep 18, 2018 · Artificial Intelligence

Scikit-learn Tutorial: Supervised Learning with Linear Regression

This article provides a comprehensive guide to using Python's scikit-learn library for supervised learning, focusing on linear regression, covering theoretical background, environment setup, data preprocessing, model training, evaluation with mean squared error, cross‑validation, and detailed code examples.

Pythoncross-validationlinear regression
0 likes · 14 min read
Scikit-learn Tutorial: Supervised Learning with Linear Regression
Architecture Digest
Architecture Digest
Jul 12, 2018 · Artificial Intelligence

How to Choose the Right Machine Learning Algorithm

This article explains that there is no universal solution for selecting machine learning algorithms and outlines practical factors—such as data characteristics, problem type, business constraints, and algorithm complexity—to help practitioners systematically narrow down and pick the most suitable models.

Data preprocessingalgorithm selectionmachine learning
0 likes · 14 min read
How to Choose the Right Machine Learning Algorithm
Tencent Cloud Developer
Tencent Cloud Developer
Jun 25, 2018 · Artificial Intelligence

Using MLP for Image Classification: Implementation, Results, and Limitations

The article demonstrates how a simple fully‑connected MLP can be trained on a small 64×64×3 cat‑vs‑non‑cat dataset, achieving perfect training accuracy but only 78 % test accuracy, and explains that parameter explosion, vanishing gradients, and lack of spatial invariance limit MLPs, motivating the shift to CNNs.

H5pyMLPPython
0 likes · 15 min read
Using MLP for Image Classification: Implementation, Results, and Limitations
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 22, 2018 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

This beginner-friendly guide walks through core machine‑learning concepts—from data organization and feature design to supervised and unsupervised algorithms such as perceptron, logistic regression, decision trees, LDA, and ensemble techniques—while explaining model evaluation, overfitting, and practical tuning strategies.

deep learningensemble methodsfeature engineering
0 likes · 8 min read
Essential Machine Learning Algorithms Every Beginner Must Know
Baidu Intelligent Testing
Baidu Intelligent Testing
May 8, 2018 · Artificial Intelligence

Interview with Baidu QATC Chair Yang Fei on AI Testing Challenges and the Future of QA

In this interview, Baidu QATC chair Yang Fei discusses his career, the evolving scope of quality assurance from code to AI model testing, key challenges such as service quality and model interpretability, practical approaches for defect discovery, continuous evaluation pipelines, and advice for QA professionals' personal growth.

AIBaiduQA
0 likes · 7 min read
Interview with Baidu QATC Chair Yang Fei on AI Testing Challenges and the Future of QA
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.

Data preprocessingPythonfeature selection
0 likes · 24 min read
Comparative Analysis and Optimization of Machine Learning Models on the UCI Census Income Dataset
Hulu Beijing
Hulu Beijing
Jan 18, 2018 · Artificial Intelligence

Why Accuracy Misleads and How to Pick Better ML Evaluation Metrics

This article uses realistic Hulu business scenarios to illustrate the pitfalls of relying solely on accuracy, precision, recall, RMSE, and other single metrics, and explains how combining complementary evaluation measures such as average accuracy, precision‑recall curves, ROC, F1‑score, and MAPE can provide a more comprehensive assessment of classification, ranking, and regression models.

AccuracyRMSEfeature engineering
0 likes · 12 min read
Why Accuracy Misleads and How to Pick Better ML Evaluation Metrics
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-intelligencedata science
0 likes · 11 min read
10 Must‑Know Machine Learning Algorithms for Engineers
Hulu Beijing
Hulu Beijing
Nov 9, 2017 · Artificial Intelligence

Mastering ROC Curves: How to Plot and Compute AUC for Binary Classification

This article explains the fundamentals of ROC curve construction, the calculation of AUC, compares ROC with PR curves, and provides step‑by‑step examples—including a medical diagnosis scenario and threshold adjustments—to help readers accurately evaluate binary classification models.

AUCROCbinary classification
0 likes · 10 min read
Mastering ROC Curves: How to Plot and Compute AUC for Binary Classification
Continuous Delivery 2.0
Continuous Delivery 2.0
Jun 28, 2017 · Fundamentals

The Evolution of Software Engineering and the Role of Development Models

The article traces the history of software engineering as a continual shortening of release cycles, discusses the persistent emergence of new development models—from the waterfall to spiral and iterative approaches—and highlights George Box’s insight that while all models are imperfect, the useful ones illuminate practice.

development modelsmethodologymodel evaluation
0 likes · 3 min read
The Evolution of Software Engineering and the Role of Development Models
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 27, 2017 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

This guide introduces beginners to core machine learning concepts, covering feature design, supervised and unsupervised methods such as perceptron, logistic regression, decision trees, LDA, and ensemble techniques like bagging and boosting, while explaining model evaluation, overfitting, and practical optimization strategies.

ensemble methodsfeature engineeringmachine learning
0 likes · 9 min read
Essential Machine Learning Algorithms Every Beginner Must Know
Architects' Tech Alliance
Architects' Tech Alliance
Nov 24, 2016 · Big Data

Data Mining Overview: Process, Techniques, and Model Evaluation

This article provides a comprehensive introduction to data mining, covering its definition, goal setting, data sampling, exploration, preprocessing, pattern discovery, model building, evaluation methods, and the main analytical techniques such as classification, regression, clustering, association rules, feature and deviation analysis, and web mining.

Clusteringassociation rulesclassification
0 likes · 10 min read
Data Mining Overview: Process, Techniques, and Model Evaluation
21CTO
21CTO
Sep 20, 2016 · Artificial Intelligence

What Quora’s VP Reveals About Building Real‑World Recommender Systems

In this talk, Quora’s VP of Engineering Xavier Amatriain shares practical lessons from building the company’s large‑scale recommender system, covering data richness, implicit signals, model choices, feature engineering, evaluation strategies, and why distribution isn’t always required.

feature engineeringimplicit feedbackmatrix factorization
0 likes · 4 min read
What Quora’s VP Reveals About Building Real‑World Recommender Systems
21CTO
21CTO
Sep 14, 2015 · Artificial Intelligence

How Airbnb Builds Machine Learning Models to Detect Fraudulent Transactions

Airbnb’s trust and safety team uses a series of machine‑learning models—starting from defining the prediction target, through data sampling and feature engineering, to evaluating precision and recall—to identify and mitigate fraud risks such as chargebacks across its global peer‑to‑peer rental platform.

AIAirbnbfeature engineering
0 likes · 7 min read
How Airbnb Builds Machine Learning Models to Detect Fraudulent Transactions
Art of Distributed System Architecture Design
Art of Distributed System Architecture Design
Aug 2, 2015 · Artificial Intelligence

Designing Machine Learning Models for Fraud Detection: Sampling, Feature Engineering, and Evaluation

This article explains how Airbnb's Trust & Safety team builds machine‑learning models to detect fraudulent behavior, covering problem definition, role‑based sampling, feature design techniques such as normalization and CP‑coding, and the trade‑offs between precision and recall in model evaluation.

AIfeature engineeringfraud detection
0 likes · 10 min read
Designing Machine Learning Models for Fraud Detection: Sampling, Feature Engineering, and Evaluation