Tagged articles

Machine Learning

1959 articles · Page 9 of 20

Sep 1, 2022 · Operations

What’s New in Zabbix 6.0? Enhanced Monitoring, HA, AI & Cloud Features Explained

Zabbix 6.0 introduces a suite of enhancements—including high‑availability clustering, advanced business‑service monitoring with SLA calculations, root‑cause analysis, machine‑learning‑based anomaly detection, Kubernetes templates, a redesigned audit log, TLS certificate checks, UI improvements, customizable branding, and new integrations—aimed at boosting operational visibility and efficiency across cloud and on‑premise environments.

KubernetesMachine LearningMonitoring

0 likes · 12 min read

What’s New in Zabbix 6.0? Enhanced Monitoring, HA, AI & Cloud Features Explained

Model Perspective

Aug 31, 2022 · Fundamentals

How to Build a Watermelon Sweetness Dataset: From Field to Features

This article describes how the author collected a watermelon dataset, defined measurable features such as size, color, sugar content, seed count, and texture, and documented the process with photos, tables, and a brief discussion of data characteristics for future machine‑learning analysis.

Data CollectionMachine Learningdata analysis

0 likes · 12 min read

How to Build a Watermelon Sweetness Dataset: From Field to Features

Aikesheng Open Source Community

Aug 31, 2022 · Big Data

Tencent's Big Data Construction: Philosophy, Architecture Evolution, and Open‑Source Strategy

The article introduces Tencent's big‑data platform philosophy and overall architecture, detailing three generations of evolution from offline Hadoop‑based processing to real‑time Spark/Storm integration and finally AI‑driven machine‑learning platforms, while also highlighting the team, book publication, and a related giveaway event.

Big DataMachine LearningTencent

0 likes · 12 min read

Tencent's Big Data Construction: Philosophy, Architecture Evolution, and Open‑Source Strategy

DataFunTalk

Aug 30, 2022 · Artificial Intelligence

Feature Engineering for Recommendation and Search Advertising

This article explains why meticulous feature engineering remains crucial in recommendation and search advertising, outlines what constitutes good features, describes common transformation techniques such as scaling, binning, and encoding, and provides practical examples and Q&A for practitioners.

AIMachine LearningRecommendation Systems

0 likes · 18 min read

Feature Engineering for Recommendation and Search Advertising

DaTaobao Tech

Aug 29, 2022 · Frontend Development

Subtoken‑TranX: Front‑end JavaScript Code Generation for Industrial Use

Subtoken‑TranX, a joint effort by Alibaba’s DaTaobao team and Peking University, converts natural‑language requirements into JavaScript by training on a curated 2,489‑pair dataset, using subtoken‑level AST generation and task‑augmented variable semantics, achieving superior accuracy over standard TranX and Transformer models and now powering Alibaba’s BizCook front‑end production platform.

ASTJavaScriptMachine Learning

0 likes · 12 min read

Subtoken‑TranX: Front‑end JavaScript Code Generation for Industrial Use

DataFunTalk

Aug 27, 2022 · Artificial Intelligence

User Growth Algorithms and Engineering Practices at Huya Live Streaming

This article details Huya's comprehensive user growth framework, covering the full acquisition‑activation‑retention‑revenue funnel, advertising workflow, crowd targeting stages, uplift modeling, virtual callbacks, intelligent bidding, and engineering implementations such as material automation, low‑latency RTA filtering, and dynamic strategy operators.

HuyaMachine LearningUplift Modeling

0 likes · 14 min read

User Growth Algorithms and Engineering Practices at Huya Live Streaming

Airbnb Technology Team

Aug 26, 2022 · Information Security

Airbnb Data Privacy and Security Engineering: Inspekt Data Classification Service and Angmar Secret Detection

Airbnb’s second privacy‑security article describes how the Inspekt service automatically classifies personal and sensitive data across diverse stores using regexes, Aho‑Corasick tries, machine‑learning models and custom validators, measures validator quality, and how the Angmar system scans code repositories for secrets via CI checks and pre‑commit hooks, with plans to broaden coverage to more APIs and data stores.

Cloud securityMachine LearningSecret Detection

0 likes · 16 min read

Airbnb Data Privacy and Security Engineering: Inspekt Data Classification Service and Angmar Secret Detection

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.

Linear RegressionMachine LearningMetrics

0 likes · 12 min read

Mastering Regression: Key Assumptions, Metrics, and Model Evaluation

ELab Team

Aug 24, 2022 · Artificial Intelligence

Demystifying AI: From Linear Regression to Neural Networks with TensorFlow.js

This article walks through the fundamentals of artificial intelligence, explaining linear and logistic regression, loss functions, gradient descent, and neural network basics, illustrated with TensorFlow.js code examples, visual analogies, and practical demos, helping readers grasp core concepts and their real‑world applications.

Artificial IntelligenceLinear RegressionLogistic Regression

0 likes · 18 min read

Demystifying AI: From Linear Regression to Neural Networks with TensorFlow.js

Python Programming Learning Circle

Aug 23, 2022 · Big Data

10 Python Packages for Automated Exploratory Data Analysis (EDA)

This article introduces ten Python packages that automate exploratory data analysis, explaining each library's capabilities, providing concise usage examples, and showing how they can generate comprehensive data summaries and visualizations with just a few lines of code.

Automated EDAEDAMachine Learning

0 likes · 8 min read

10 Python Packages for Automated Exploratory Data Analysis (EDA)

DevOps

Aug 23, 2022 · Artificial Intelligence

Intelligent Automation Testing: Self‑Healing and Machine‑Learning Techniques

This article reviews the evolution of automated testing toward intelligent solutions, explaining self‑healing mechanisms, machine‑learning‑driven object recognition, computer‑vision and OCR approaches, industry tools such as Healenium and Airtest, and future prospects for zero‑code AI‑powered test automation.

AIAutomation TestingMachine Learning

0 likes · 13 min read

Intelligent Automation Testing: Self‑Healing and Machine‑Learning Techniques

Model Perspective

Aug 18, 2022 · Artificial Intelligence

Master SciPy Clustering: K‑Means and Hierarchical Methods with Python

This guide introduces SciPy's clustering modules, explaining the vector quantization and k‑means algorithm in scipy.cluster.vq, and demonstrates hierarchical clustering with scipy.cluster.hierarchy, accompanied by complete Python code examples and visualizations to help you apply these techniques to real data.

ClusteringK-MeansMachine Learning

0 likes · 4 min read

Master SciPy Clustering: K‑Means and Hierarchical Methods with Python

DataFunSummit

Aug 18, 2022 · Artificial Intelligence

Evolution and Technical Practices of Du Xiaoman Risk Control Decision Engine

This article presents a comprehensive overview of Du Xiaoman's risk control system evolution—from early rule‑based engines to AI‑enhanced intelligent decision engines—detailing technical practices such as strategy iteration acceleration, decision latency reduction, parallel workflow design, and future trends in data quality, automated strategy optimization, and real‑time analytics.

Decision EngineMachine Learningdata-quality

0 likes · 18 min read

Evolution and Technical Practices of Du Xiaoman Risk Control Decision Engine

Python Programming Learning Circle

Aug 16, 2022 · Fundamentals

30 Useful Python Packages for Data Workflows

This article introduces thirty unique and practical Python packages that simplify various aspects of data workflows, including model training notifications, progress tracking, data validation, statistical calculations, date handling, and more, providing installation commands and code examples for each tool.

Data WorkflowMachine LearningPython

0 likes · 15 min read

30 Useful Python Packages for Data Workflows

Bilibili Tech

Aug 16, 2022 · Artificial Intelligence

Bilibili's Intelligent Adaptive Bitrate Algorithm: From Theory to Practice

Bilibili enhanced mobile video streaming by replacing standard ABR methods with an intelligent adaptive bitrate system that uses a real‑time QoE model, refined network‑speed preprocessing, long‑term feature analysis, decision‑tree‑based neural models, and personalized user modes to balance resolution, buffering, and data usage.

BilibiliMachine LearningPensieve algorithm

0 likes · 12 min read

Bilibili's Intelligent Adaptive Bitrate Algorithm: From Theory to Practice

Model Perspective

Aug 14, 2022 · Artificial Intelligence

Mastering Feature Binning with sklearn: Uniform, Quantile, and K‑Means Methods

This article explains why discretizing continuous variables improves model stability, introduces three common binning techniques—equal-width, equal-frequency, and clustering—and demonstrates how to implement each using scikit‑learn's KBinsDiscretizer with Python code examples on a synthetic score dataset.

KBinsDiscretizerMachine LearningPython

0 likes · 5 min read

Mastering Feature Binning with sklearn: Uniform, Quantile, and K‑Means Methods

DataFunSummit

Aug 14, 2022 · Artificial Intelligence

Optimizing Pre‑Ranking in Meituan Search: Knowledge Distillation and Neural Architecture Search

This article describes Meituan Search's pre‑ranking (coarse‑ranking) system evolution and presents two major optimization strategies—leveraging knowledge distillation to align coarse‑ranking with fine‑ranking and employing neural architecture search to jointly improve effectiveness and latency—demonstrating significant offline and online performance gains.

Machine LearningNeural Architecture Searchknowledge distillation

0 likes · 17 min read

Optimizing Pre‑Ranking in Meituan Search: Knowledge Distillation and Neural Architecture Search

Model Perspective

Aug 13, 2022 · Artificial Intelligence

Mastering Outlier Detection: Techniques, Algorithms, and PyOD Implementation

Outlier detection identifies data points far from the norm, using methods such as the 3‑sigma rule, boxplots, K‑Nearest Neighbors, and numerous probabilistic and proximity‑based algorithms, with practical PyOD code examples for training, evaluating, and visualizing models across various techniques.

Machine Learninganomaly detectionoutlier detection

0 likes · 8 min read

Mastering Outlier Detection: Techniques, Algorithms, and PyOD Implementation

政采云技术

Aug 11, 2022 · Artificial Intelligence

Semi‑Automatic Annotation with Label Studio and YOLOv5: Installation, Project Setup, and Model Training

This guide explains how to combine the open‑source labeling platform Label Studio with the YOLOv5 object‑detection model to achieve semi‑automatic annotation, covering installation of both tools, project creation, dataset configuration, and training a custom YOLOv5 model on your own data.

Label StudioMachine LearningPython

0 likes · 11 min read

Semi‑Automatic Annotation with Label Studio and YOLOv5: Installation, Project Setup, and Model Training

Model Perspective

Aug 8, 2022 · Artificial Intelligence

Mastering sklearn.svm: Parameters, Grid Search, and Real-World Examples

An in‑depth guide to sklearn.svm explains SVM classification and regression, details key parameters such as C and kernel types, demonstrates how to use GridSearchCV for hyperparameter tuning, and provides complete Python code examples for iris classification and California housing price prediction.

GridSearchCVMachine LearningPython

0 likes · 6 min read

Mastering sklearn.svm: Parameters, Grid Search, and Real-World Examples

DataFunSummit

Aug 8, 2022 · Artificial Intelligence

Voice Analysis for Financial Risk Control: Feature Extraction, Single-Channel Speech Separation, and Text Tagging

This talk presents the application of voice analysis in financial risk control, covering voice‑based risk feature extraction, single‑channel speech separation techniques, and speech‑text labeling methods, demonstrating how acoustic and textual cues can be leveraged to improve risk detection and model performance.

Machine Learningaudio processingrisk control

0 likes · 12 min read

Voice Analysis for Financial Risk Control: Feature Extraction, Single-Channel Speech Separation, and Text Tagging

Model Perspective

Aug 7, 2022 · Artificial Intelligence

Understanding Support Vector Regression: Theory and Formulation

Support Vector Regression (SVR) predicts continuous outputs by fitting a hyperplane that minimizes a loss function while employing an ε‑insensitive loss to reduce overfitting, and the article details its mathematical formulation, penalty terms, Lagrangian dual, and optimization process.

Lagrangian dualMachine LearningSVR

0 likes · 3 min read

Understanding Support Vector Regression: Theory and Formulation

Model Perspective

Aug 7, 2022 · Artificial Intelligence

Mastering Core ML Evaluation Metrics: From Bias‑Variance to ROC Curves

This article explains essential machine‑learning evaluation concepts—including the bias‑variance trade‑off, Gini impurity versus entropy, precision‑recall curves, ROC and AUC, the elbow method for K‑means, PCA scree plots, linear and logistic regression, SVM geometry, normal‑distribution rules, and Student’s t‑distribution—providing clear visual illustrations for each.

Machine LearningPCARoc

0 likes · 7 min read

Mastering Core ML Evaluation Metrics: From Bias‑Variance to ROC Curves

Model Perspective

Aug 6, 2022 · Artificial Intelligence

How Kernel Functions Enable SVMs to Classify Non‑Linear Data

When training data from two classes overlap heavily, linear SVMs fail, so we map inputs into a high‑dimensional Hilbert (feature) space using kernel functions—such as linear, polynomial, radial basis, and Fourier kernels—to make the data linearly separable, formulate a quadratic programming problem, solve its convex dual, and construct a classifier for unknown samples.

Hilbert spaceKernel MethodsMachine Learning

0 likes · 2 min read

How Kernel Functions Enable SVMs to Classify Non‑Linear Data

Model Perspective

Aug 6, 2022 · Artificial Intelligence

Understanding Activation Functions in Artificial Neural Networks

This article introduces artificial neural networks, explains the role of artificial neurons and their weighted connections, and provides an overview of common activation functions—including linear, nonlinear ramp, threshold/step, and sigmoid forms—highlighting their characteristics and typical saturation values.

Deep LearningMachine Learningactivation function

0 likes · 2 min read

Understanding Activation Functions in Artificial Neural Networks

Model Perspective

Aug 5, 2022 · Artificial Intelligence

What Are the Essential Steps and Types of Machine Learning?

Machine learning involves five core steps—from data collection and preparation to model training, evaluation, and improvement—while encompassing supervised, unsupervised, and reinforcement learning methods, each with distinct algorithms and real-world applications across finance, healthcare, and retail.

ApplicationsMachine LearningReinforcement Learning

0 likes · 7 min read

What Are the Essential Steps and Types of Machine Learning?

Model Perspective

Aug 5, 2022 · Artificial Intelligence

Understanding Generalized Linear‑Separable Support Vector Machines

This article explains how hard‑margin and soft‑margin support vector machines handle perfectly and approximately linearly separable data, introduces slack variables and penalty parameters, derives the quadratic programming and dual formulations, and shows how the resulting classifier works on unseen samples.

Machine LearningOptimizationSupport Vector Machine

0 likes · 3 min read

Understanding Generalized Linear‑Separable Support Vector Machines

HelloTech

Aug 5, 2022 · Artificial Intelligence

Intelligent Transaction System Construction for Halu Carpool

In a July 2022 keynote, Halu’s senior algorithm expert Wang Fan outlined the construction of an intelligent transaction system for its car‑pool service, detailing business challenges, a decomposition into matching, pricing, marketing and arbitration, a recommendation‑pipeline architecture, and three‑stage algorithm evolution that boosted order volume by over 20 %.

AlgorithmMachine LearningPricing

0 likes · 12 min read

Intelligent Transaction System Construction for Halu Carpool

High Availability Architecture

Aug 5, 2022 · Big Data

Innovative Marketing Practices on the Cloud: How an Intelligent Data Lake Enables Flexible and Efficient Marketing Capabilities

The presentation details how Amazon Web Services’ intelligent data lake architecture integrates big data and machine learning to overcome marketing challenges, improve data governance, and provide scalable, real‑time analytics for personalized, data‑driven marketing across enterprises.

AWSBig DataCloud Computing

0 likes · 13 min read

Innovative Marketing Practices on the Cloud: How an Intelligent Data Lake Enables Flexible and Efficient Marketing Capabilities

Model Perspective

Aug 4, 2022 · Artificial Intelligence

How Supervised Learning Predicts House Prices – A Hands‑On Guide

Using a real‑world housing example, this article explains supervised and unsupervised learning, walks through building a price‑prediction function, introduces gradient descent for optimizing weights, and highlights pitfalls like overfitting, offering a practical introduction to core machine‑learning concepts.

Linear RegressionMachine LearningPython

0 likes · 13 min read

How Supervised Learning Predicts House Prices – A Hands‑On Guide

Model Perspective

Aug 3, 2022 · Artificial Intelligence

Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide

This article provides a thorough overview of the most widely used machine learning algorithms, classifying them by learning style and problem type, and highlighting popular methods such as supervised, unsupervised, semi‑supervised, regression, instance‑based, regularization, decision‑tree, Bayesian, clustering, association rule, neural network, deep learning, dimensionality‑reduction, and ensemble techniques.

AlgorithmsDeep LearningMachine Learning

0 likes · 10 min read

Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide

Model Perspective

Jul 30, 2022 · Artificial Intelligence

How Decision Trees Predict House Locations: From Intuition to Overfitting

This article explains machine learning fundamentals using a house‑location classification example, illustrating how decision trees create split points from features like elevation and price, grow recursively, achieve high training accuracy, and reveal overfitting when evaluated on unseen test data.

Artificial IntelligenceMachine Learningclassification

0 likes · 11 min read

How Decision Trees Predict House Locations: From Intuition to Overfitting

MaGe Linux Operations

Jul 29, 2022 · Artificial Intelligence

Master 10 Popular Clustering Algorithms in Python with Scikit‑Learn

This tutorial introduces clustering, explains why no single algorithm fits all data, and provides step‑by‑step Python examples using scikit‑learn for ten popular unsupervised learning methods, complete with code snippets and visualizations to illustrate results.

ClusteringMachine LearningPython

0 likes · 24 min read

Master 10 Popular Clustering Algorithms in Python with Scikit‑Learn

ByteDance Terminal Technology

Jul 29, 2022 · Artificial Intelligence

Pitaya: ByteDance’s End‑Side AI Engineering Platform Overview

Pitaya, built by ByteDance’s Client AI and MLX teams, is a comprehensive end‑side AI engineering platform that provides a full workflow from model development and data preparation to deployment, monitoring, and federated learning, supporting large‑scale commercial scenarios across multiple apps.

AI platformMachine Learningedge AI

0 likes · 14 min read

Pitaya: ByteDance’s End‑Side AI Engineering Platform Overview

DataFunTalk

Jul 29, 2022 · Artificial Intelligence

Tencent Music Cloud‑Native One‑Stop Machine Learning Platform: Features and Future Roadmap

This article introduces Tencent Music's cloud‑native, one‑stop machine learning platform, detailing its engineering workflow, distributed acceleration, inference closed‑loop, edge computing capabilities, and future plans, while highlighting challenges of traditional ML pipelines and the platform's solutions for resource orchestration, storage, scheduling, and GPU utilization.

AI platformDistributed TrainingMachine Learning

0 likes · 17 min read

Tencent Music Cloud‑Native One‑Stop Machine Learning Platform: Features and Future Roadmap

GuanYuan Data Tech Team

Jul 28, 2022 · Artificial Intelligence

Unlocking Reinforcement Learning: Core Concepts, Algorithms, and Real‑World Applications

This article introduces reinforcement learning by defining agents, environments, rewards, and policies, explains key concepts such as Markov Decision Processes and Bellman equations, and surveys major algorithms—including dynamic programming, Monte‑Carlo, TD learning, policy gradients, Q‑learning, DQN, and evolution strategies—while highlighting practical challenges and notable case studies like AlphaGo Zero.

Deep LearningEvolution StrategiesMDP

0 likes · 27 min read

Unlocking Reinforcement Learning: Core Concepts, Algorithms, and Real‑World Applications

Model Perspective

Jul 25, 2022 · Artificial Intelligence

How to Interpret Logistic Regression Parameters and Odds Ratios with Python

This article explains the concepts of odds and odds ratios, shows how logistic regression estimates them, and demonstrates practical model fitting and prediction using Python's statsmodels and scikit‑learn libraries with real‑world examples.

Logistic RegressionMachine LearningPython

0 likes · 8 min read

How to Interpret Logistic Regression Parameters and Odds Ratios with Python

Model Perspective

Jul 23, 2022 · Artificial Intelligence

LASSO Regression Explained: Theory, Case Studies, and Python Code

This article introduces the mathematical foundations of ordinary least squares, ridge, and LASSO regression, explains why LASSO requires coordinate descent, presents two real-world case studies with data, and provides complete Python code for fitting, visualizing, and interpreting LASSO models.

LASSOMachine LearningPython

0 likes · 8 min read

LASSO Regression Explained: Theory, Case Studies, and Python Code

Meituan Technology Team

Jul 21, 2022 · Artificial Intelligence

Overview of Meituan Technical Team Papers Featured at ACM SIGIR 2022 and Related Works

The article highlights ten representative Meituan technical papers accepted at ACM SIGIR 2022, spanning personalized opinion tagging, cross‑domain sentiment classification, dialogue summarization transfer, universal retrieval, CTR prediction, image behavior modeling, and topic segmentation, each summarized with abstracts and download links for researchers.

Machine LearningNatural Language ProcessingRecommendation Systems

0 likes · 25 min read

Overview of Meituan Technical Team Papers Featured at ACM SIGIR 2022 and Related Works

JD Retail Technology

Jul 18, 2022 · Artificial Intelligence

JD’s Intelligent Product Matching Technology Wins the 11th Wu Wenjun AI Science and Technology Award

On July 16, JD.com’s jointly developed intelligent product‑matching technology received the second‑place Science and Technology Progress Award at the 11th Wu Wenjun Artificial Intelligence Award, highlighting its multi‑dimensional user profiling, cross‑modal product modeling, and precise matching innovations that have driven significant commercial and societal impact.

Artificial IntelligenceJD.comMachine Learning

0 likes · 4 min read

JD’s Intelligent Product Matching Technology Wins the 11th Wu Wenjun AI Science and Technology Award

DaTaobao Tech

Jul 18, 2022 · Artificial Intelligence

Walle: An End-to-End, General-Purpose, Large-Scale Device-Cloud Collaborative Machine Learning System

Walle is Alibaba’s first end‑to‑end, general‑purpose, large‑scale device‑cloud collaborative machine‑learning platform that manages billions of mobile devices, provides a full‑stack data and compute pipeline, cuts cloud load by 87 %, reduces latency to ~100 ms, and already powers over a trillion daily ML invocations across dozens of Alibaba apps.

MNNMachine LearningOSDI

0 likes · 11 min read

Walle: An End-to-End, General-Purpose, Large-Scale Device-Cloud Collaborative Machine Learning System

DataFunTalk

Jul 17, 2022 · Artificial Intelligence

Evolution of OPPO Commercial Advertising Targeting: From Differentiated to Intelligent to Untargeted Practices

This article details OPPO's commercial advertising targeting evolution, covering the background and logic, the multi‑layer targeting system and data modeling, automated intelligent targeting methods, the shift to untargeted crowd recall, and future considerations for ad‑targeting technology.

AdvertisingMachine LearningOPPO

0 likes · 13 min read

Evolution of OPPO Commercial Advertising Targeting: From Differentiated to Intelligent to Untargeted Practices

DataFunSummit

Jul 14, 2022 · Artificial Intelligence

Next‑Generation Song Recognition: From Audio Fingerprints to Cover Detection

This article reviews the limitations of traditional audio‑fingerprint song identification, surveys the evolution of cover‑song detection techniques, and details Tencent Music’s Lyra‑CoverNet system—including embedding extraction, sequence retrieval, automated labeling, deployment results, and future research directions—demonstrating how deep learning advances enable more accurate and scalable music recognition.

Machine LearningTencent Musicaudio fingerprint

0 likes · 10 min read

Next‑Generation Song Recognition: From Audio Fingerprints to Cover Detection

DataFunSummit

Jul 11, 2022 · Artificial Intelligence

Optimizing CVR in Sparse High‑Value Travel Recommendation Scenarios

This article presents a comprehensive overview of conversion‑rate (CVR) optimization for Alitrip’s travel recommendation platform, detailing the challenges of extremely sparse user feedback, the design of item, user, query and context features, and a series of model‑level and loss‑function techniques—including generic‑label modeling, global‑transaction modeling, ESMM, rank‑loss approximations, and multi‑task CTR auxiliary training—to improve both CTR and CVR performance in high‑ticket‑price scenarios.

CVR optimizationMachine LearningRecommendation Systems

0 likes · 19 min read

Optimizing CVR in Sparse High‑Value Travel Recommendation Scenarios

Bitu Technology

Jul 8, 2022 · Artificial Intelligence

Applying NLP and Machine Learning to Classify Tubi User Feedback

This article explains how Tubi leverages natural‑language processing, sentence embeddings (USE and BERT), and LightGBM models to automatically categorize large volumes of Net Promoter Score comments and customer‑support tickets, enabling data‑driven product decisions and workflow automation.

LightGBMMachine LearningNLP

0 likes · 11 min read

Applying NLP and Machine Learning to Classify Tubi User Feedback

DaTaobao Tech

Jul 8, 2022 · Frontend Development

Alibaba Front‑End Intelligent Technology: PipCook, DataCook, imgcook and Future Directions

Alibaba Front‑End Intelligent Technology combines PipCook, DataCook, and imgcook to enable data‑driven UI generation, on‑device AI inference via WASM‑Rust‑SIMD and WebGPU, and applications such as code IntelliSense and design‑to‑code, while outlining a roadmap toward unified AI‑powered interfaces for commerce.

AIMachine LearningTensorFlow.js

0 likes · 33 min read

Alibaba Front‑End Intelligent Technology: PipCook, DataCook, imgcook and Future Directions

DataFunTalk

Jul 5, 2022 · Artificial Intelligence

Identifying Viral Short‑Video Content on Kuaishou: Models, Features, and Engineering Framework

This article explains how Kuaishou detects and predicts viral short‑video素材 by defining content types, outlining essential viral elements, describing a two‑stage coarse‑recall and fine‑ranking model that combines speed‑based features, Gaussian mixture modeling, and a lightweight DNN, and showcases real‑world case studies and Q&A.

KuaishouMachine Learningrecommendation system

0 likes · 14 min read

Identifying Viral Short‑Video Content on Kuaishou: Models, Features, and Engineering Framework

政采云技术

Jul 5, 2022 · Artificial Intelligence

Overview of Natural Language Processing Techniques and Their Evolution

This article provides a comprehensive overview of natural language processing, covering its definition, historical development from one‑hot encoding to modern models such as word2vec, ELMo, GPT, and BERT, and discusses the advantages, limitations, and key concepts of each technique.

Artificial IntelligenceMachine LearningNLP

0 likes · 23 min read

Overview of Natural Language Processing Techniques and Their Evolution

Python Programming Learning Circle

Jul 4, 2022 · Artificial Intelligence

Building an Advertising Recommendation Model with Python and PyTorch

This article walks through the development of a simple advertising recommendation system using Python, covering data collection, preprocessing with label encoding, text embedding via Torch, constructing an MLP model, and initiating training, while reflecting on the challenges faced by Python developers in the big‑data era.

MLPMachine LearningPyTorch

0 likes · 5 min read

Building an Advertising Recommendation Model with Python and PyTorch

Model Perspective

Jul 3, 2022 · Fundamentals

Explore 20+ Essential Modeling Articles: From Differential Equations to Machine Learning

This curated list groups recent articles on change and predictive models, covering topics such as war dynamics, population, epidemic spread, differential equations, regression, time‑series analysis, machine learning classifiers, and grey‑prediction techniques, providing students with ready references for diverse modeling approaches.

Machine LearningModelingmathematical models

0 likes · 3 min read

Explore 20+ Essential Modeling Articles: From Differential Equations to Machine Learning

DataFunSummit

Jul 3, 2022 · Artificial Intelligence

Graph Neural Network Approaches for Internet Financial Fraud Detection

The talk examines how the COVID‑19 pandemic accelerated online financial services and fraud, outlines the challenges of traditional and internet‑based fraud detection, and presents graph neural network solutions—including PC‑GNN and AO‑GNN—demonstrating their effectiveness on real‑world and public datasets while discussing future research directions.

AUC optimizationMachine Learningfinancial fraud

0 likes · 12 min read

Graph Neural Network Approaches for Internet Financial Fraud Detection

Python Crawling & Data Mining

Jul 3, 2022 · Artificial Intelligence

Logistic Regression vs KNN: Python Stock Trading Experiment

A Python enthusiast reproduces a Tsinghua University quantitative trading strategy, swapping K‑Nearest Neighbors for logistic regression, fetches three years of Moutai stock data, engineers features, trains and evaluates the model, and finds logistic regression slightly underperforms the original KNN benchmark.

Logistic RegressionMachine Learningstock trading

0 likes · 5 min read

Logistic Regression vs KNN: Python Stock Trading Experiment

21CTO

Jun 26, 2022 · Artificial Intelligence

Can Babies Teach Us to Build the Next Generation of AI?

Researchers at Trinity College Dublin propose new AI guidelines inspired by infant learning, arguing that babies' experiential, unsupervised learning can overcome current machine learning limitations, and outlining three principles to help develop more efficient, data‑light AI systems.

AIMachine LearningUnsupervised Learning

0 likes · 4 min read

Can Babies Teach Us to Build the Next Generation of AI?

DataFunTalk

Jun 24, 2022 · Artificial Intelligence

Explore‑and‑Exploit (EE) in JD Search: Bias Mitigation, Model Iteration, and Evaluation

The talk presents JD Search's Explore‑and‑Exploit (EE) module, detailing its bias‑mitigation pipeline—including position, popularity, and exposure debiasing—model architecture upgrades with SVGP and causal inference, online AB metrics, offline evaluation methods, and future research directions to improve search diversity and long‑term value.

Bias MitigationMachine LearningSVGP

0 likes · 17 min read

Explore‑and‑Exploit (EE) in JD Search: Bias Mitigation, Model Iteration, and Evaluation

DataFunSummit

Jun 23, 2022 · Artificial Intelligence

Unlocking Data Potential: Automatic Data Augmentation, Denoising, Active Learning, and Data Splitting

The talk explains how to maximize the value of training data by exploring background on model generalization, automatic data augmentation techniques, denoising strategies, active learning for selecting unlabeled samples, and robust data splitting methods, offering practical guidelines for AI practitioners.

AIActive LearningData Augmentation

0 likes · 16 min read

Unlocking Data Potential: Automatic Data Augmentation, Denoising, Active Learning, and Data Splitting

Model Perspective

Jun 22, 2022 · Artificial Intelligence

Understanding Model Performance: Precision, Recall, and F1 Score Explained

This article explains how to evaluate classification models by moving beyond simple accuracy to using confusion matrices, precision, recall, and the F1 score, illustrating their trade‑offs and when each metric is most appropriate for different real‑world scenarios.

F1 scoreMachine LearningPrecision

0 likes · 4 min read

Understanding Model Performance: Precision, Recall, and F1 Score Explained

Model Perspective

Jun 19, 2022 · Artificial Intelligence

How Decision Trees Work: From Entropy to Gini Index Explained

This article introduces decision tree algorithms, explains their role in supervised learning for classification and regression, details the construction process, compares information gain and Gini index for attribute selection, and reviews popular tree methods such as ID3, C4.5, and CART with illustrative examples.

C4.5CARTGini Index

0 likes · 7 min read

How Decision Trees Work: From Entropy to Gini Index Explained

Model Perspective

Jun 18, 2022 · Artificial Intelligence

Understanding Support Vector Machines: Theory, Example, and Python Code

This article explains the fundamentals of Support Vector Machines, describes how they separate data with optimal hyperplanes, provides a 2‑D example with visualizations, and includes Python code using scikit‑learn to generate synthetic data, plot points, and illustrate possible decision boundaries.

Machine LearningSupport Vector Machineclassification

0 likes · 4 min read

Understanding Support Vector Machines: Theory, Example, and Python Code

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.

Machine LearningModel Evaluationclassification

0 likes · 5 min read

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

Model Perspective

Jun 17, 2022 · Artificial Intelligence

Understanding Supervised Learning: Regression vs Classification Explained

This article explains the fundamentals of supervised machine learning, distinguishing between regression and classification, describing how algorithms learn mappings from inputs to outputs, and outlining common models such as linear regression, logistic regression, decision trees, SVMs, random forests, and neural networks.

Artificial IntelligenceMachine Learningclassification

0 likes · 4 min read

Understanding Supervised Learning: Regression vs Classification Explained

Model Perspective

Jun 13, 2022 · Artificial Intelligence

Understanding Decision Trees: From Basic Process to Watermelon Example

This article explains the fundamentals of decision tree learning, describing its recursive construction, the criteria for splitting nodes using information gain based on entropy, and walks through a classic watermelon dataset example to illustrate how attributes are selected and the final tree is built.

ID3 algorithmInformation GainMachine Learning

0 likes · 8 min read

Understanding Decision Trees: From Basic Process to Watermelon Example

Efficient Ops

Jun 12, 2022 · Artificial Intelligence

Unlocking AI Success: A Deep Dive into the Model/MLOps Capability Maturity Framework

This article explains the globally first AI model development management standard—Model/MLOps Capability Maturity Model (Part 1: Development Management)—detailing its structure, key domains such as requirement management, test case design, and project planning, and how organizations can assess and improve their AI engineering capabilities.

AI governanceCapability Maturity ModelMLOps

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Unlocking AI Success: A Deep Dive into the Model/MLOps Capability Maturity Framework

DataFunTalk

Jun 10, 2022 · Artificial Intelligence

Intelligent Risk Control Algorithms for Logistics and Commercial Vehicle Finance

This article examines the rapid growth of financing demand among small‑and‑micro enterprises in logistics and commercial vehicle sectors, outlines the high financial penetration in the industry, and details how AI‑driven intelligent risk‑control frameworks—covering data pipelines, model selection, feature‑portrait systems, and graph‑based applications—address the challenges and opportunities of modern financial risk management.

Artificial IntelligenceMachine Learninggraph analytics

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Intelligent Risk Control Algorithms for Logistics and Commercial Vehicle Finance

DataFunSummit

Jun 8, 2022 · Artificial Intelligence

Search Term Recommendation: Scenarios, Algorithm Design, and Future Directions

This article presents a comprehensive overview of search term recommendation in QQ Browser, covering various recommendation scenarios, challenges, query library architecture, multi‑task ranking models, coarse‑to‑fine ranking pipelines, auto‑completion strategies, and future research directions.

AIMachine LearningRecommendation Systems

0 likes · 14 min read

Search Term Recommendation: Scenarios, Algorithm Design, and Future Directions

Laravel Tech Community

Jun 6, 2022 · Artificial Intelligence

What an Open‑Source Twitter Algorithm Would Look Like: Architecture, Data Model, and Engineering Challenges

This article examines the practical aspects of open‑sourcing Twitter’s recommendation algorithm, covering the platform’s data model, timeline views, ranking features, a TypeScript pseudocode illustration, and the major engineering challenges of scale, real‑time processing, reliability, and security.

AlgorithmMachine LearningTwitter

0 likes · 14 min read

What an Open‑Source Twitter Algorithm Would Look Like: Architecture, Data Model, and Engineering Challenges

Python Programming Learning Circle

Jun 5, 2022 · Artificial Intelligence

The Rise of Hugging Face: From Emoji Logo to Leading AI Platform

From its quirky start as a teenage iPhone chatbot to becoming the central hub for open‑source transformer models, Hugging Face has grown into a fast‑rising AI platform, securing $100 million Series C funding, serving thousands of organizations, and aiming to democratize machine learning.

AIFundingHugging Face

0 likes · 7 min read

Model Perspective

Jun 4, 2022 · Artificial Intelligence

Master K-means Clustering: How the Algorithm Finds Compact Groups

K-means is a classic distance‑based clustering algorithm that iteratively partitions data into k compact, well‑separated groups by minimizing the sum of squared errors, using random centroid initialization and heuristic updates until convergence, making it a fundamental tool in AI and data analysis.

AlgorithmClusteringK-Means

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Master K-means Clustering: How the Algorithm Finds Compact Groups

Model Perspective

Jun 4, 2022 · Artificial Intelligence

Master Systematic Clustering: From Distance Matrix to Multi-Level Groupings

Systematic clustering, a widely used hierarchical clustering technique, builds a dendrogram by iteratively merging the closest sample points based on a distance matrix, allowing analysts to visualize and select groupings at various distance thresholds, from a single cluster to each point as its own class.

ClusteringMachine Learningdistance matrix

0 likes · 3 min read

Master Systematic Clustering: From Distance Matrix to Multi-Level Groupings

Model Perspective

Jun 4, 2022 · Fundamentals

Understanding Sample Similarity: Distance Metrics and Cluster Methods

This article explains how to quantify similarity between data samples using distance metrics such as Manhattan, Euclidean, and Chebyshev, outlines the properties these distances must satisfy, and describes common inter‑class measures like single linkage, complete linkage, centroid, group average, and sum‑of‑squares methods.

ClusteringMachine LearningMinkowski

0 likes · 4 min read

Understanding Sample Similarity: Distance Metrics and Cluster Methods

Model Perspective

Jun 2, 2022 · Artificial Intelligence

Master Polynomial Regression: Fit Non‑Linear Data with Simple Polynomials

Polynomial regression extends linear models by fitting data with higher‑order polynomial functions, requiring selection of the polynomial degree and its coefficients, and can be applied alongside other nonlinear fitting techniques to capture complex growth trends in real‑world systems.

Machine LearningPolynomial Regressionnonlinear fitting

0 likes · 3 min read

Master Polynomial Regression: Fit Non‑Linear Data with Simple Polynomials

Model Perspective

Jun 2, 2022 · Fundamentals

Understanding Simple and Multivariate Linear Regression Models

This article introduces the basics of simple (univariate) linear regression and extends to multivariate linear regression, explaining their regression equations, the use of the least‑squares method to estimate parameters, and the practical relevance of multiple predictors in modeling real‑world phenomena.

Least SquaresMachine Learningmultivariate analysis

0 likes · 3 min read

Understanding Simple and Multivariate Linear Regression Models

Tencent Cloud Developer

May 31, 2022 · Artificial Intelligence

Scalable Graph Neural Architecture Search System (PaSca) – WWW 2022 Best Student Paper

PaSca, a scalable graph neural architecture search system that separates message aggregation from updates, explores over 150,000 GNN designs with multi‑objective optimization, delivers models that outperform traditional GNNs in accuracy, memory and speed, has been open‑sourced and deployed at Tencent for risk control, recommendation and fraud detection, and earned the WWW 2022 Best Student Paper award.

Big DataMachine LearningNeural Architecture Search

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Scalable Graph Neural Architecture Search System (PaSca) – WWW 2022 Best Student Paper

HelloTech

May 30, 2022 · Artificial Intelligence

Harbor's Passive Growth Algorithms and Growth Engine: Practices and Insights

Harbor’s growth engine combines a passive, attribution‑driven traffic‑allocation algorithm with componentized ranking, search, and marketing systems—using pairwise/Listwise models, multi‑task CTR/CVR prediction, and automated strategy triggers—to align short‑term efficiency with long‑term LTV goals while moving toward causal inference and domain‑expert‑driven general models.

AIAlgorithm EngineeringMachine Learning

0 likes · 11 min read

Harbor's Passive Growth Algorithms and Growth Engine: Practices and Insights

TAL Education Technology

May 26, 2022 · Artificial Intelligence

GoodFuture International Algorithm Team Wins Champion and Runner‑up in the 5th Educational Data Mining Workshop

The GoodFuture International Algorithm Team, together with Jinan University Guangdong Smart Education Research Institute, distinguished themselves among 95 global teams in the 5th Educational Data Mining in Computer Science Education Workshop, securing a champion title in one task and a runner‑up in another, showcasing advanced AI‑driven predictive and recommendation techniques for intelligent student assessment.

Educational Data MiningMachine LearningPredictive Modeling

0 likes · 6 min read

GoodFuture International Algorithm Team Wins Champion and Runner‑up in the 5th Educational Data Mining Workshop

HelloTech

May 26, 2022 · Artificial Intelligence

Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies

Hello’s automated C‑side growth algorithm loop integrates diverse traffic sources, semi‑supervised PU‑learning, graph‑based look‑alike targeting, causal uplift models for smart subsidies, and adaptive copy and external ad optimization, dramatically boosting ride‑hailing and lifestyle service revenue while minimizing engineering duplication.

AI platformMachine LearningRecommendation Systems

0 likes · 20 min read

Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies

Architects' Tech Alliance

May 25, 2022 · Industry Insights

How Emerging Tech Fuels the Metaverse: Edge, 5G/6G, AI, and Decentralized Cloud

This report analyzes the metaverse’s technical foundation—covering edge computing, 5G/6G, machine learning, AIoT, holographic imaging, sensor tech, and decentralized cloud—while examining data growth in China, brain‑computer interaction policies, and the future of virtual avatars.

5GArtificial IntelligenceDecentralized Cloud

0 likes · 8 min read

How Emerging Tech Fuels the Metaverse: Edge, 5G/6G, AI, and Decentralized Cloud

Alimama Tech

May 23, 2022 · Artificial Intelligence

Alibaba Mama Team Papers Accepted at KDD 2022 and Other Top Conferences

The Alibaba Mama technical team secured five paper acceptances at the prestigious KDD 2022 conference, presenting advances such as curriculum‑guided Bayesian reinforcement learning for ROI‑constrained bidding, adversarial‑gradient driven exploration for click‑through‑rate prediction, externality‑aware transformers for e‑commerce ads, multi‑modal multi‑query pretraining, and generative‑replay streaming graph neural networks.

Advertising BiddingKDD 2022Machine Learning

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Alibaba Mama Team Papers Accepted at KDD 2022 and Other Top Conferences

Architect

May 19, 2022 · Artificial Intelligence

Learning to Rank (LTR) Practice in Amap Search Suggestions: From Data Collection to Model Optimization

This article details Amap's practical experience with Learning to Rank for search suggestions, covering application scenarios, data pipeline construction, feature engineering, model training, loss‑function adjustments, and the resulting performance improvements, while also discussing challenges such as sparse features and click bias.

AmapLearning-to-RankMachine Learning

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Learning to Rank (LTR) Practice in Amap Search Suggestions: From Data Collection to Model Optimization

Meituan Technology Team

May 19, 2022 · Artificial Intelligence

Tulong: An Industrial Graph Neural Network Framework and Learning Platform at Meituan

Tulong is Meituan’s industrial graph neural network framework and learning platform that combines a compact MTGraph engine, a modular operator‑based GNN library, and visual workflow tools to enable heterogeneous, billion‑edge graph training on a single machine with up to 60 % memory savings and 2–4× speedups, streamlining search, recommendation, advertising and delivery pipelines.

Industrial AIMachine Learningframework

0 likes · 24 min read

Tulong: An Industrial Graph Neural Network Framework and Learning Platform at Meituan

Bitu Technology

May 18, 2022 · Artificial Intelligence

Mitigating Exposure Bias in Tubi’s Recommendation System

This article explains how Tubi’s machine‑learning team reduces exposure bias in its video recommendation pipeline by normalizing popularity features, incorporating additional signals such as search behavior, and applying exploration techniques like bandit algorithms to diversify content exposure.

Machine Learningbanditsexploration

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Mitigating Exposure Bias in Tubi’s Recommendation System

DaTaobao Tech

May 18, 2022 · Artificial Intelligence

Deep Ranking Optimization for E-commerce Recommendation

The 2021 Taobao New‑Product team boosted e‑commerce recommendation by redesigning the coarse‑ranking stage with a dual‑tower DSSM, low‑cost feature‑crossing, NOVA attention and multi‑task distillation from a fine‑ranking teacher, delivering up to +30‰ GAUC gain and 3‑5 % online CTR and click improvements.

E‑commerceMachine LearningModel Optimization

0 likes · 17 min read

Deep Ranking Optimization for E-commerce Recommendation

Alibaba Cloud Developer

May 17, 2022 · Artificial Intelligence

How Databricks and Prophet Power Retail Demand Forecasting for Store‑Item Sales

This article walks through why accurate demand forecasting is critical for retailers, shows how to prepare and visualize sales data, demonstrates building a store‑item model with Databricks DDI and Facebook Prophet, and explains scaling the model to predict every product across all stores, highlighting performance metrics and practical tips.

DatabricksMachine LearningProphet

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How Databricks and Prophet Power Retail Demand Forecasting for Store‑Item Sales

php Courses

May 16, 2022 · Backend Development

Interesting PHP Projects: AI Libraries, Networking Frameworks, and Useful Tools

This article introduces a curated list of notable PHP projects—including advanced machine‑learning libraries, a neural‑network framework, a natural‑language‑processing toolkit, a distributed long‑connection service, a database migration tool, a versatile filesystem abstraction, a C++ extension framework, and PHP‑FPM—highlighting their features, use‑cases, and sample code.

FrameworksLibrariesMachine Learning

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Interesting PHP Projects: AI Libraries, Networking Frameworks, and Useful Tools

Code DAO

May 16, 2022 · Artificial Intelligence

How to Build a Simple Neural Network from Scratch with NumPy

This article walks through implementing a basic multi‑layer neural network using only NumPy, covering terminology, network architecture, forward and backward propagation, activation functions, loss calculation, parameter updates with SGD, and compares the custom model with a Keras implementation.

BackpropagationMachine LearningNeural Network

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How to Build a Simple Neural Network from Scratch with NumPy

DataFunTalk

May 15, 2022 · Artificial Intelligence

Search Term Recommendation: Scenarios, Algorithm Design, Challenges and Future Directions

This article presents an in‑depth overview of search term recommendation in QQ Browser, covering the various recommendation scenarios, the composition of recommendation items, the multi‑stage algorithm architecture, key technical challenges, evaluation metrics, and future research directions such as multi‑task and session‑aware modeling.

Machine Learningfuture researchmulti-task learning

0 likes · 15 min read

Search Term Recommendation: Scenarios, Algorithm Design, Challenges and Future Directions

Code DAO

May 14, 2022 · Fundamentals

A Learned Harmonic Mean Estimator for Efficient Bayesian Model Selection

The article presents a machine‑learning‑assisted harmonic mean estimator that computes Bayesian model evidence without dependence on sampling strategies, explains its theoretical basis, compares it to the original estimator, and demonstrates its accuracy on Rosenbrock and Normal‑Gamma benchmarks.

Bayesian model selectionMCMCMachine Learning

0 likes · 12 min read

A Learned Harmonic Mean Estimator for Efficient Bayesian Model Selection

Alimama Tech

May 11, 2022 · Artificial Intelligence

PICASSO: An Industrial-Scale Sparse Training Engine for Wide-and-Deep Recommender Systems

PICASSO, Alibaba’s GPU‑centric sparse training engine for wide‑and‑deep recommender systems, merges identical embedding tables, interleaves data and kernel operations, and caches hot embeddings on GPU, eliminating the parameter server and delivering up to tenfold speedups over TensorFlow‑PS while maintaining model quality.

AlibabaGPU OptimizationMachine Learning

0 likes · 14 min read

PICASSO: An Industrial-Scale Sparse Training Engine for Wide-and-Deep Recommender Systems

DataFunSummit

May 10, 2022 · Artificial Intelligence

Optimizing Fliggy Search Ranking with Product Inclusion Relationships: The DIRN Model

This article presents the DIRN model, which leverages product inclusion graphs and graph‑based embeddings to address the challenges of ranking both single‑item and complex travel products on Fliggy, demonstrating significant CTR, CVR, and GMV improvements through offline experiments and online A/B testing.

AlibabaDIRNMachine Learning

0 likes · 13 min read

Optimizing Fliggy Search Ranking with Product Inclusion Relationships: The DIRN Model

Python Programming Learning Circle

May 10, 2022 · Artificial Intelligence

Seven Classic Regression Models for Machine Learning

This article introduces regression analysis and explains why it is essential for predictive modeling, then details seven widely used regression techniques—including linear, logistic, polynomial, stepwise, ridge, lasso, and elastic‑net—while offering guidance on selecting the most appropriate model for a given dataset.

Lasso RegressionLinear RegressionLogistic Regression

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Seven Classic Regression Models for Machine Learning

DataFunSummit

May 8, 2022 · Artificial Intelligence

Machine Learning‑Based Time Series Forecasting and Anomaly Detection System at JD Search

The article describes JD Search's machine‑learning alert system that combines offline and real‑time training, FFT‑based periodic detection, Prophet forecasting, and DBSCAN anomaly clustering, and explains architectural design, data preprocessing, model optimization, and distributed deployment to improve alert accuracy and response speed.

DBSCANDistributed ComputingFFT

0 likes · 10 min read

Machine Learning‑Based Time Series Forecasting and Anomaly Detection System at JD Search

Code DAO

May 7, 2022 · Artificial Intelligence

Why Normal (Gaussian) Distributions Are Fundamental to Machine Learning

The article explains how normal (Gaussian) distributions underpin many machine‑learning algorithms, reviewing the central limit theorem, multivariate Gaussian sampling, and key properties such as products, sums, conditional and marginal distributions, linear transformations, and Gaussian‑based Bayesian inference.

GaussianMachine Learningbayesian inference

0 likes · 7 min read

Why Normal (Gaussian) Distributions Are Fundamental to Machine Learning

Baidu Intelligent Testing

May 6, 2022 · Backend Development

Exploring Baidu's Scalable Intelligent Testing: Automated Test Case Generation for Code, API, UI, and GUI

This article details Baidu's large‑scale intelligent testing framework, describing how AST‑based unit test generation, automated API test creation, visual UI interaction case synthesis, GUI traversal action set generation, and front‑end assertion automation work together to achieve high‑coverage, low‑cost automated testing across multiple languages and platforms.

API testingASTBaidu

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Exploring Baidu's Scalable Intelligent Testing: Automated Test Case Generation for Code, API, UI, and GUI

Baidu Geek Talk

May 6, 2022 · Artificial Intelligence

Artificial Intelligence Development History and Pre‑training Model Trends

From the 1940s birth of computers to today's ultra‑large pre‑training models like Baidu’s ERNIE 3.0, AI has progressed through three development waves, now driven by algorithms, compute and data, with pre‑training lowering application barriers and evolving toward larger, multimodal, and more generalizable systems.

Artificial IntelligenceDeep LearningMachine Learning

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Artificial Intelligence Development History and Pre‑training Model Trends

Code DAO

May 6, 2022 · Fundamentals

Information Theory Foundations for Machine Learning and Deep Learning

The article explains Shannon information content, entropy, cross‑entropy, KL‑divergence, conditional entropy and mutual information, illustrating each concept with coin‑flip and dice examples, visual formulas, and discusses their roles as loss functions and evaluation metrics in machine‑learning models.

KL DivergenceMachine Learningcross entropy

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Information Theory Foundations for Machine Learning and Deep Learning

NetEase Yanxuan Technology Product Team

May 5, 2022 · Artificial Intelligence

Time Series Forecasting Algorithm System in E-commerce: Practice and Applications at NetEase Yanxuan

NetEase Yanxuan built an end‑to‑end time‑series forecasting system for e‑commerce that integrates rich user, product, business and external features with a suite of statistical, machine‑learning and deep‑learning models, delivers predictions via a Tornado‑based service for thousands of SKUs, warehouses, advertising and app traffic, and shows that simpler models like XGBoost often outperform complex deep nets while interpretability and external shocks remain key challenges.

Machine LearningSales PredictionTime Series Forecasting

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Time Series Forecasting Algorithm System in E-commerce: Practice and Applications at NetEase Yanxuan

DataFunSummit

May 3, 2022 · Artificial Intelligence

Scientific Data Definition, Application, Evaluation, and Explanation for Financial Risk Modeling

This presentation explores how to scientifically define, apply, evaluate, and interpret data in financial risk management, covering data alignment with business goals, feature selection, model metrics such as KS and PSI, handling pandemic effects, and methods for model explainability.

Machine LearningModel EvaluationRisk Modeling

0 likes · 13 min read

Scientific Data Definition, Application, Evaluation, and Explanation for Financial Risk Modeling

DataFunTalk

Apr 28, 2022 · Artificial Intelligence

Sequence Feature Modeling in Large-Scale Recommendation Systems and Fast Deployment with EasyRec

This article reviews the evolution of behavior‑sequence modeling methods—from pooling and target‑attention to RNN, capsule, transformer, and graph neural networks—explains their industrial relevance, and demonstrates how to quickly apply these techniques in the EasyRec framework with practical configuration examples.

DINEasyRecMachine Learning

0 likes · 21 min read

Sequence Feature Modeling in Large-Scale Recommendation Systems and Fast Deployment with EasyRec

DataFunTalk

Apr 25, 2022 · Artificial Intelligence

Scientific Data Definition, Application, Evaluation, and Explanation in Financial Risk Modeling

This presentation explores how to scientifically define, apply, evaluate, and interpret data in financial risk management, covering data alignment with business goals, feature selection, model metrics like KS and PSI, handling pandemic impacts, and methods for model explanation and improvement.

KS metricMachine LearningModel Evaluation

0 likes · 14 min read

Scientific Data Definition, Application, Evaluation, and Explanation in Financial Risk Modeling

DataFunTalk

Apr 24, 2022 · Artificial Intelligence

Machine Learning‑Driven Time Series Forecasting and Anomaly Detection System at JD Search

The article describes JD Search’s machine‑learning‑based time‑series forecasting and anomaly‑detection platform, detailing its overall architecture, offline and real‑time training pipelines, FFT‑based periodicity detection, Prophet forecasting, DBSCAN outlier detection, and distributed optimizations such as Alink integration and load‑balancing strategies.

DBSCANFFTMachine Learning

0 likes · 10 min read

Machine Learning‑Driven Time Series Forecasting and Anomaly Detection System at JD Search

NetEase Yanxuan Technology Product Team

Apr 24, 2022 · Operations

Traffic Distribution and Allocation: Non‑Intervention vs. Intervention, Objectives, and Technical Solutions

The article compares non‑intervention (natural) traffic, where models autonomously maximize UV, with intervention (allocation) traffic that fine‑tunes re‑ranking to meet short‑term business goals, outlines objectives of balancing immediate profit and long‑term value, and presents two technical solutions—an ML‑plus‑OR integer‑programming model and a PID‑based control loop—for real‑time traffic allocation.

Machine LearningOptimizationPID control

0 likes · 9 min read

Traffic Distribution and Allocation: Non‑Intervention vs. Intervention, Objectives, and Technical Solutions