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Machine Learning

1959 articles · Page 10 of 20
DataFunSummit
DataFunSummit
Apr 23, 2022 · Artificial Intelligence

Intelligent Vehicle‑Cargo Matching, Driver Tagging, and Freight Price Prediction in a Logistics Big Data Platform

The article describes how a logistics company built a data‑driven platform that uses big‑data storage, DeepFM and LSTM models, and real‑time GPS tracking to create an intelligent vehicle‑cargo matching system, a multi‑label driver tagging framework, and a freight price prediction engine, thereby improving efficiency and reducing costs across the industry.

LSTMMachine Learningdeepfm
0 likes · 16 min read
Intelligent Vehicle‑Cargo Matching, Driver Tagging, and Freight Price Prediction in a Logistics Big Data Platform
Python Programming Learning Circle
Python Programming Learning Circle
Apr 19, 2022 · Artificial Intelligence

Step‑by‑Step Guide to Building Machine Learning Models with Scikit‑learn Templates

This article introduces a practical, step‑by‑step tutorial on building machine learning models with scikit‑learn, covering problem types, dataset loading, splitting, and a series of reusable templates (V1.0, V2.0, V3.0) for classification, regression, clustering, cross‑validation, and hyper‑parameter tuning, complete with code examples.

Machine LearningPythonclassification
0 likes · 17 min read
Step‑by‑Step Guide to Building Machine Learning Models with Scikit‑learn Templates
DaTaobao Tech
DaTaobao Tech
Apr 19, 2022 · Artificial Intelligence

Generative Re‑ranking for Diverse and Context‑Aware Recommendation

The paper presents a generative re‑ranking framework for Taobao’s home‑decor channel that combines heuristic sequence generation methods (MMR, DPP, beam search) with a context‑aware encoder to produce diverse, relevance‑balanced recommendation lists, achieving notable gains in PV, IPV, CTR and click‑diversity over traditional point‑wise ranking.

Context-AwareDiversityMachine Learning
0 likes · 19 min read
Generative Re‑ranking for Diverse and Context‑Aware Recommendation
DataFunSummit
DataFunSummit
Apr 17, 2022 · Artificial Intelligence

Precise Marketing Algorithms and Practices at Hello Mobility

This article presents Hello Mobility’s precise marketing system, covering its business background, value, framework, algorithmic capabilities such as Pu‑Learning LookAlike modeling, TSA semi‑supervised learning, and Graph Embedding, as well as identified pain points, project impact, and future directions for scaling and automation.

AIMachine Learninggraph embedding
0 likes · 12 min read
Precise Marketing Algorithms and Practices at Hello Mobility
Zuoyebang Tech Team
Zuoyebang Tech Team
Apr 15, 2022 · Artificial Intelligence

Zuoyebang’s NLP Platforms: Boosting Online Education with AI

In this interview, Zuoyebang’s NLP lead explains how the company built self‑developed platforms like IQC and FTP to automate text quality inspection and intelligent labeling, outlines their architecture, shares practical deep‑learning applications such as translation and grammar correction, and discusses future research directions in large‑scale multi‑label classification, few‑shot learning, and multimodal models.

AI PlatformsMachine LearningNLP
0 likes · 11 min read
Zuoyebang’s NLP Platforms: Boosting Online Education with AI
GuanYuan Data Tech Team
GuanYuan Data Tech Team
Apr 14, 2022 · Artificial Intelligence

Mastering Time Series Forecasting: From Moving Averages to Transformers

Time series forecasting, essential across weather, finance, and commerce, involves tasks like classification, clustering, anomaly detection, and especially prediction; this article explores its definitions, evaluation metrics, traditional methods, machine‑learning approaches, deep‑learning models such as TFT, and emerging AutoML tools, offering practical insights and best practices.

AutoMLDeep LearningGBDT
0 likes · 27 min read
Mastering Time Series Forecasting: From Moving Averages to Transformers
Shopee Tech Team
Shopee Tech Team
Apr 14, 2022 · Big Data

URL Normalization and Statistical Analysis in MDAP Using Probabilistic and Machine Learning Techniques

MDAP normalizes URLs by automatically learning pattern‑tree rule models using entropy‑based splits, gibberish and numeric detection, and scalable Flink processing, which groups millions of raw URLs into concise patterns for accurate statistical monitoring, dramatically reducing data noise while still facing latency and model‑iteration challenges.

FlinkMachine Learningpattern tree
0 likes · 20 min read
URL Normalization and Statistical Analysis in MDAP Using Probabilistic and Machine Learning Techniques
Python Programming Learning Circle
Python Programming Learning Circle
Apr 14, 2022 · Artificial Intelligence

Top Clustering Algorithms in Python with scikit-learn: A Comprehensive Tutorial

This tutorial explains clustering as an unsupervised learning task, outlines why no single algorithm fits all data, and provides step‑by‑step Python code using scikit‑learn to install the library, generate synthetic datasets, and apply ten popular clustering algorithms with visualizations.

ClusteringMachine LearningPython
0 likes · 21 min read
Top Clustering Algorithms in Python with scikit-learn: A Comprehensive Tutorial
Alipay Experience Technology
Alipay Experience Technology
Apr 14, 2022 · Frontend Development

Boosting Complex Marketing Animations: WebGL Performance Tricks & AI‑Powered Smart Effects

This talk explains how Ant Group's MarsStudio tackles the challenges of large‑scale marketing scenes by optimizing WebGL rendering to reduce CPU‑GPU communication and by using machine‑learning‑driven smart animation generation to let front‑end developers create dynamic effects quickly and efficiently.

GraphicsMachine Learningfrontend
0 likes · 9 min read
Boosting Complex Marketing Animations: WebGL Performance Tricks & AI‑Powered Smart Effects
DaTaobao Tech
DaTaobao Tech
Apr 13, 2022 · Artificial Intelligence

Machine‑Learning Based Bandwidth Prediction and Adaptive Streaming for Taobao Live: Concerto, OnRL, and Loki

Alibaba’s Taobao Live team replaced rule‑based bandwidth estimators with three machine‑learning solutions—Concerto, OnRL, and Loki—trained on over a million hours of global live‑stream data, achieving up to 13% throughput gain, threefold stall reduction, and up to 44% lower 95th‑percentile stalls, now deployed commercially.

Machine LearningReinforcement Learningadaptive bitrate
0 likes · 14 min read
Machine‑Learning Based Bandwidth Prediction and Adaptive Streaming for Taobao Live: Concerto, OnRL, and Loki
Tencent Cloud Developer
Tencent Cloud Developer
Apr 11, 2022 · Artificial Intelligence

Recall Module in Recommendation Systems: Multi-Path Retrieval and Optimization

The recall module in recommendation systems retrieves thousands of items from massive pools using parallel non-personalized and personalized paths—such as hot-item, content-based, behavior-based, and deep-model recall—prioritizing coverage and low latency while addressing challenges like hard-negative sampling, selection bias, objective alignment, and channel competition to feed downstream ranking.

AIMachine LearningMulti‑Path Retrieval
0 likes · 15 min read
Recall Module in Recommendation Systems: Multi-Path Retrieval and Optimization
NetEase LeiHuo Testing Center
NetEase LeiHuo Testing Center
Apr 11, 2022 · Artificial Intelligence

Understanding AI: Definitions, Applications in Games and Products, and Basic Machine Learning Concepts

This article explains what artificial intelligence is, distinguishes weak and strong AI, explores its applications in games, product testing, and NetEase's Fuxi platform, and introduces fundamental machine‑learning concepts such as supervised, unsupervised, and reinforcement learning, as well as neural networks and loss functions.

AIMachine Learninggame AI
0 likes · 10 min read
Understanding AI: Definitions, Applications in Games and Products, and Basic Machine Learning Concepts
Snowball Engineer Team
Snowball Engineer Team
Apr 11, 2022 · Artificial Intelligence

Design and Implementation of Snowball's Model Feature Management Platform

The article presents Snowball's model feature platform, detailing its motivation, architecture, feature lifecycle management, online engine design, optimization techniques, and the resulting improvements in feature iteration speed, reuse, and system stability for recommendation and search services.

Feature ManagementMachine Learningfeature engineering
0 likes · 16 min read
Design and Implementation of Snowball's Model Feature Management Platform
DataFunSummit
DataFunSummit
Apr 10, 2022 · Artificial Intelligence

Algorithmic Optimization of Information‑Flow Advertising for Hallo Mobility

This presentation details how Hallo Mobility tackles the challenges of information‑flow ad modeling by describing the ad ecosystem, the company’s business evolution, and the advertiser‑side algorithmic solutions—including plan‑level quality detection, creative‑level uplift modeling, feature‑cross engineering, and pre‑bid user screening—while outlining future directions for automated, data‑driven ad delivery.

AIAlgorithmInformation Flow
0 likes · 18 min read
Algorithmic Optimization of Information‑Flow Advertising for Hallo Mobility
Tencent Cloud Developer
Tencent Cloud Developer
Apr 7, 2022 · Artificial Intelligence

Re‑ranking in Recommendation Systems: Architecture, Techniques, and Efficiency

The article surveys the re‑ranking stage of modern recommendation pipelines, detailing its architecture after recall and precise ranking, and examining how shuffling and diversity improve user experience, while multi‑task fusion, context‑aware learning‑to‑rank, real‑time online learning, and traffic‑control strategies balance accuracy, efficiency, and business responsiveness.

DiversityMachine LearningRe‑ranking
0 likes · 15 min read
Re‑ranking in Recommendation Systems: Architecture, Techniques, and Efficiency
DataFunSummit
DataFunSummit
Apr 3, 2022 · Artificial Intelligence

Tree‑Based Causal Inference for Smart Subsidy Optimization at Hello Mobility

This article explains how Hello Mobility uses tree‑based causal inference and uplift modeling to improve smart subsidy efficiency in hotel marketing, covering background, uplift methods, custom split criteria, offline AUUC evaluation, online deployment, and future research directions.

Machine LearningMarketing OptimizationUplift Modeling
0 likes · 17 min read
Tree‑Based Causal Inference for Smart Subsidy Optimization at Hello Mobility
DataFunSummit
DataFunSummit
Apr 1, 2022 · Artificial Intelligence

Detecting Invalid Queries in Voice Interaction: Non‑Human Interaction and Ambiguous Intent Recognition

This talk presents a comprehensive study of invalid query detection in voice assistants, covering the definition of effective and ineffective queries, challenges of non‑human interaction and ambiguous intent recognition, data collection, model design, experimental results, user‑feedback loops, and future research directions.

Machine LearningNatural Language Understandinginvalid query detection
0 likes · 20 min read
Detecting Invalid Queries in Voice Interaction: Non‑Human Interaction and Ambiguous Intent Recognition
HelloTech
HelloTech
Mar 28, 2022 · Artificial Intelligence

Algorithmic Optimization for Information Flow Advertising at Hello Travel

Hello Travel tackles information‑flow advertising challenges by using LightGBM‑based models to predict order conversion, creative performance, and pre‑bid user quality, augmenting sparse data with feature engineering and uplift techniques, while planning future fully automated delivery, richer pre‑screening, and cross‑channel reinforcement‑learning enhancements.

AdvertisingLightGBMMachine Learning
0 likes · 18 min read
Algorithmic Optimization for Information Flow Advertising at Hello Travel
DataFunTalk
DataFunTalk
Mar 28, 2022 · Artificial Intelligence

Construction and Application of Meituan's On‑site Comprehensive Knowledge Graph

This article introduces Meituan's on‑site comprehensive knowledge graph, detailing its multi‑layer design, data‑driven construction pipeline, challenges of diverse user demands and industry complexity, and showcases practical applications in search, recommendation, intelligent display, as well as future expansion plans.

Knowledge GraphMachine LearningMeituan
0 likes · 22 min read
Construction and Application of Meituan's On‑site Comprehensive Knowledge Graph
DataFunSummit
DataFunSummit
Mar 27, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can uncover subtle correlations in large datasets, detailing user growth metrics, propensity‑score matching, causal recommendation models, heterogeneous treatment effect analysis, and practical strategies for improving retention and activity in recommendation systems.

Machine LearningPropensity Score MatchingRecommendation Systems
0 likes · 12 min read
Causal Machine Learning for User Growth: Concepts, Methods, and Applications
DataFunTalk
DataFunTalk
Mar 27, 2022 · Artificial Intelligence

Algorithmic Optimization for Information‑Flow Advertising at Hello Travel

This talk explains how Hello Travel tackles challenges in information‑flow advertising by describing the market landscape, their business background, and detailed algorithmic optimization across plan, creative, and pre‑bid dimensions, including data‑driven modeling, feature engineering, LightGBM and uplift models, and outlines future directions.

AdvertisingLightGBMMachine Learning
0 likes · 16 min read
Algorithmic Optimization for Information‑Flow Advertising at Hello Travel
IT Services Circle
IT Services Circle
Mar 23, 2022 · Artificial Intelligence

Local Outlier Factor (LOF) Algorithm: Theory, Workflow, Pros & Cons, and Python Implementation

This article introduces the classic density‑based anomaly detection method Local Outlier Factor (LOF), explains its underlying concepts such as k‑distance, reachability distance, and local reachability density, outlines the algorithm steps, discusses its advantages and limitations, and provides practical Python examples using PyOD and scikit‑learn.

LOFMachine LearningPython
0 likes · 10 min read
Local Outlier Factor (LOF) Algorithm: Theory, Workflow, Pros & Cons, and Python Implementation
DataFunSummit
DataFunSummit
Mar 22, 2022 · Artificial Intelligence

Housing Price Estimation and Average Price Calculation Using 58.com Data and CatBoost

This article presents a comprehensive overview of 58.com’s real‑estate price system, describes how average prices are computed from platform data, explains three anomaly‑detection methods, and details a CatBoost‑based machine‑learning model for automated house valuation, including feature engineering and evaluation metrics.

CatBoostMachine LearningReal Estate Data
0 likes · 15 min read
Housing Price Estimation and Average Price Calculation Using 58.com Data and CatBoost
DataFunTalk
DataFunTalk
Mar 21, 2022 · Artificial Intelligence

Graph-Based I2I Recall for Short Video Recommendation at Kuaishou

This article presents Kuaishou's graph‑based item‑to‑item (I2I) recall pipeline for short‑video recommendation, detailing the business challenges, the advantages of graph neural networks, the system architecture, optimization techniques such as similarity metric refinement, graph structure learning, edge‑weight learning, and future research directions.

I2I recallKuaishouMachine Learning
0 likes · 16 min read
Graph-Based I2I Recall for Short Video Recommendation at Kuaishou
DataFunTalk
DataFunTalk
Mar 20, 2022 · Artificial Intelligence

Detecting Invalid Queries in Voice Interaction: Non‑Human Interaction and Ambiguous Intent Recognition

This talk presents a comprehensive study of invalid query detection in voice assistants, covering the definition and taxonomy of invalid queries, challenges of non‑human interaction and ambiguous intent recognition, data collection and labeling strategies, feature engineering, deep neural network modeling, experimental results, user‑feedback loops, and current performance limits.

AIMachine Learningdialogue system
0 likes · 17 min read
Detecting Invalid Queries in Voice Interaction: Non‑Human Interaction and Ambiguous Intent Recognition
DataFunSummit
DataFunSummit
Mar 19, 2022 · Artificial Intelligence

Intelligent Advertising in Real Estate: Challenges, Practices, and Insights from Beike

This article presents Beike's experience in intelligent real‑estate advertising, detailing the business background, key technical challenges such as conversion‑rate estimation, delayed‑feedback modeling, GEO targeting, and budget allocation, and sharing practical solutions that improved conversion rates by over 10%.

AIDSPMachine Learning
0 likes · 11 min read
Intelligent Advertising in Real Estate: Challenges, Practices, and Insights from Beike
DataFunTalk
DataFunTalk
Mar 19, 2022 · Artificial Intelligence

QQ Music Recommendation Architecture: Challenges, Solutions, and Future Directions

This article details how QQ Music tackled rapid growth in recommendation traffic by redesigning its recommendation architecture, introducing a cloud‑native machine‑learning platform, optimizing data services, and adopting a DAG‑based recall system to improve scalability, flexibility, and development efficiency.

AIMachine Learningarchitecture
0 likes · 12 min read
QQ Music Recommendation Architecture: Challenges, Solutions, and Future Directions
Python Programming Learning Circle
Python Programming Learning Circle
Mar 19, 2022 · Artificial Intelligence

Building a Simple Digital Twin for Lithium‑Ion Batteries Using Python and Neural Networks

The article demonstrates how to build a digital twin for lithium‑ion batteries in Python by constructing a physics‑based model, augmenting it with experimental data using a simple Keras neural network, and visualizing predictions, illustrating the hybrid approach’s improved accuracy over purely empirical methods.

Digital TwinKerasMachine Learning
0 likes · 9 min read
Building a Simple Digital Twin for Lithium‑Ion Batteries Using Python and Neural Networks
Kuaishou Tech
Kuaishou Tech
Mar 16, 2022 · Artificial Intelligence

Multi-Dimensional Causal Forest Model for Heterogeneous Treatment Effects in Marketing

This paper introduces a novel multi-dimensional causal forest model combined with efficient integer programming algorithms to estimate heterogeneous treatment effects (HTE) in marketing scenarios, outperforming traditional tree-based methods through improved handling of intervention heterogeneity and resource allocation optimization.

A/B testingMachine LearningMarketing Algorithms
0 likes · 7 min read
Multi-Dimensional Causal Forest Model for Heterogeneous Treatment Effects in Marketing
DataFunSummit
DataFunSummit
Mar 15, 2022 · Artificial Intelligence

KuaiRec: A 99.6% Dense Short‑Video Recommendation Dataset for Unbiased and Interactive Recommendation Research

The article introduces KuaiRec, a densely observed short‑video recommendation dataset with 99.6% density covering 1,411 users and 3,327 videos, discusses its structure, advantages over sparse public datasets, and its applicability to unbiased, interactive, conversational and reinforcement‑learning based recommendation studies.

KuaiRecMachine LearningRecommendation Systems
0 likes · 7 min read
KuaiRec: A 99.6% Dense Short‑Video Recommendation Dataset for Unbiased and Interactive Recommendation Research
DataFunTalk
DataFunTalk
Mar 12, 2022 · Artificial Intelligence

NetEase Cloud Music Advertising System: Algorithm Practice and Model Evolution

This article presents a comprehensive overview of NetEase Cloud Music's advertising system, detailing its architecture, core challenges, CTR and CVR prediction models, feature engineering, model evolution from LR to deep learning, user vector modeling, and practical recommendations for improving ad performance.

AdvertisingCTR predictionDeep Learning
0 likes · 15 min read
NetEase Cloud Music Advertising System: Algorithm Practice and Model Evolution
DataFunSummit
DataFunSummit
Mar 10, 2022 · Artificial Intelligence

Applying Causal Inference to Debias Recommendation Systems at Kuaishou

This talk explores how causal inference techniques are used to identify and mitigate various biases in Kuaishou's recommendation pipeline, covering background theory, recent research advances, practical implementations for popularity and video completion debiasing, and reflections on challenges and future directions.

AIKuaishouMachine Learning
0 likes · 19 min read
Applying Causal Inference to Debias Recommendation Systems at Kuaishou
ByteDance SE Lab
ByteDance SE Lab
Mar 10, 2022 · Mobile Development

How Fastbot Boosts iOS App Stability with AI‑Driven Automated Testing

Fastbot, a collaborative AI‑powered testing service from ByteDance’s Quality Lab and GIP iOS platform team, overcomes TestFlight limits by using machine learning and reinforcement learning to automate stability testing, improve code coverage, detect accessibility issues, and streamline result consumption for faster app releases.

Machine LearningStabilityaccessibility
0 likes · 15 min read
How Fastbot Boosts iOS App Stability with AI‑Driven Automated Testing
Python Programming Learning Circle
Python Programming Learning Circle
Mar 9, 2022 · Artificial Intelligence

Top Python Machine Learning Libraries in 2021 and Their Key Features

This article introduces the most important Python machine‑learning libraries of 2021—including TensorFlow, Scikit‑Learn, NumPy, Keras, PyTorch, LightGBM, Eli5, SciPy, Theano and Pandas—explaining their purposes, distinctive characteristics, and why they are essential tools for modern AI development.

LightGBMMachine LearningPyTorch
0 likes · 12 min read
Top Python Machine Learning Libraries in 2021 and Their Key Features
ITPUB
ITPUB
Mar 2, 2022 · Artificial Intelligence

Leveraging Giant AI Models for Startup Success: Opportunities and Pitfalls

This article examines how startups can harness massive pre‑trained AI models such as GPT‑3, outlining the historical context, benefits of transfer learning, the steep costs and data‑alignment challenges, and strategic considerations when using cloud APIs versus self‑hosting.

AICloud APIsMachine Learning
0 likes · 14 min read
Leveraging Giant AI Models for Startup Success: Opportunities and Pitfalls
DataFunSummit
DataFunSummit
Mar 1, 2022 · Artificial Intelligence

Alibaba's Smart Supply‑Chain Forecasting: Scenarios, Algorithm R&D, and Application Cases

This article details Alibaba's exploration of intelligent supply‑chain forecasting, covering scenario classification, three generations of prediction algorithms, the self‑developed Falcon model, performance evaluation, and real‑world cases such as Double 11 and live‑streaming, highlighting challenges and practical solutions.

AIDeep LearningMachine Learning
0 likes · 18 min read
Alibaba's Smart Supply‑Chain Forecasting: Scenarios, Algorithm R&D, and Application Cases
HelloTech
HelloTech
Mar 1, 2022 · Artificial Intelligence

Causal Inference and Tree‑Based Uplift Modeling for Intelligent Subsidy in Ride‑Sharing Services

The paper applies causal inference and tree‑based uplift modeling to identify coupon‑responsive riders, using T‑, S‑, and X‑Learners as well as a proprietary Treelift model that directly optimizes per‑user utility, achieving a 4.7% lift over manual rules and 2.3% over prior response models.

AIMachine LearningMarketing Optimization
0 likes · 17 min read
Causal Inference and Tree‑Based Uplift Modeling for Intelligent Subsidy in Ride‑Sharing Services
DataFunSummit
DataFunSummit
Feb 28, 2022 · Artificial Intelligence

UGC Sentiment Analysis Solutions and Applications in Taobao

This article presents a comprehensive overview of Taobao's user‑generated content (UGC) sentiment analysis pipeline, covering background, task definition, challenges, model architecture—including RoBERTa‑based extraction, sentiment‑knowledge pre‑training, and graph augmentation—personalized impression ranking, business impact cases, and future research directions.

E‑commerceMachine LearningPretrained Models
0 likes · 16 min read
UGC Sentiment Analysis Solutions and Applications in Taobao
DataFunSummit
DataFunSummit
Feb 27, 2022 · Artificial Intelligence

Dxm Eros: A Massive‑Scale Graph Platform for Financial Risk Control

This article introduces the Dxm Eros ultra‑large graph platform, explains its architecture, storage, analysis, modeling and visualization capabilities, and demonstrates how graph‑based machine learning is applied to fintech risk control, anti‑fraud, anti‑money‑laundering and automated audit workflows.

AIFinTechGraph Database
0 likes · 17 min read
Dxm Eros: A Massive‑Scale Graph Platform for Financial Risk Control
DataFunTalk
DataFunTalk
Feb 26, 2022 · Artificial Intelligence

Intelligent Advertising in Real Estate: Challenges, Practices, and Insights from Beike

This article presents Beike's experience in applying intelligent advertising to the real‑estate industry, detailing business background, key technical challenges, multi‑task conversion modeling, GEO user targeting, delayed‑feedback calibration, and ROI‑driven budget allocation to improve ad effectiveness.

AIAdvertisingMachine Learning
0 likes · 10 min read
Intelligent Advertising in Real Estate: Challenges, Practices, and Insights from Beike
JD Cloud Developers
JD Cloud Developers
Feb 25, 2022 · Artificial Intelligence

How JD’s Heuristic QA Boosts Smart Customer Service with AI

This article details JD's heuristic question‑answering framework for intelligent customer service, covering its pre‑consultation prediction, in‑consultation associative input, post‑consultation recommendation modules, underlying algorithms, deployment results, and future enhancement directions.

AICustomer ServiceMachine Learning
0 likes · 17 min read
How JD’s Heuristic QA Boosts Smart Customer Service with AI
DataFunTalk
DataFunTalk
Feb 22, 2022 · Artificial Intelligence

Real‑Time Graph Neural Network for Payment Fraud Detection at eBay

This article describes how eBay applies graph neural networks to real‑time payment fraud detection, covering the anti‑fraud scenario, limitations of traditional GBDT pipelines, challenges of constructing and serving dynamic heterogeneous graphs, the end‑to‑end solution with directed slice graphs and a Lambda‑style architecture, and experimental results comparing GNN with LightGBM.

Machine LearningReal-Time Analyticsfraud detection
0 likes · 15 min read
Real‑Time Graph Neural Network for Payment Fraud Detection at eBay
DataFunTalk
DataFunTalk
Feb 21, 2022 · Artificial Intelligence

Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems

This talk explains how recommendation bias arises from popularity and position effects, introduces causal inference concepts and three inference levels, reviews recent research such as DICE and Huawei’s causal embedding, and details Kuaishou’s practical applications—including popularity debias, causal representation decoupling, and video completion‑rate debias—along with experimental results and future challenges.

Bias MitigationKuaishouMachine Learning
0 likes · 20 min read
Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems
DataFunTalk
DataFunTalk
Feb 18, 2022 · Artificial Intelligence

Travel Intent Prediction in E-commerce: Algorithm Strategies, Multi‑source Behavior Modeling, and Model Design

This talk presents Alibaba's travel intent prediction system, detailing the unique challenges of low‑frequency, multi‑source travel behavior, the multi‑granular CNN and time‑attention model architecture, experimental comparisons with baselines, and how integrated user interest modeling improves recommendation performance.

Deep LearningMachine Learningattention
0 likes · 11 min read
Travel Intent Prediction in E-commerce: Algorithm Strategies, Multi‑source Behavior Modeling, and Model Design
DataFunSummit
DataFunSummit
Feb 15, 2022 · Artificial Intelligence

Real-time Fraud Detection in E-commerce Payments Using Graph Neural Networks

This article presents an end‑to‑end solution that leverages graph neural networks and dynamic bipartite graph construction to detect payment fraud in eBay's e‑commerce platform in real time, addressing traditional model limitations, graph latency challenges, and demonstrating superior performance over GBDT approaches.

E‑commerceMachine Learningfraud detection
0 likes · 15 min read
Real-time Fraud Detection in E-commerce Payments Using Graph Neural Networks
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Feb 10, 2022 · Artificial Intelligence

Simple Music Recommendation System: Audio‑Feature and Playlist‑Based Approaches

This article presents two straightforward music recommendation methods—content‑based filtering using audio features and collaborative filtering using playlist data—detailing their design ideas, key Python and Go code snippets, model training, evaluation, and possible improvements.

Collaborative FilteringMachine LearningPython
0 likes · 13 min read
Simple Music Recommendation System: Audio‑Feature and Playlist‑Based Approaches
DataFunTalk
DataFunTalk
Feb 8, 2022 · Artificial Intelligence

Large-Scale Graph Platform Dxm Eros for Financial Risk Control

This article introduces the Dxm Eros ultra‑large graph platform, detailing its architecture, storage, analysis, modeling, and visualization modules, and demonstrates how graph machine‑learning techniques are applied to financial risk control, fraud detection, anti‑money‑laundering, and automated credit review.

AIGraph DatabaseKnowledge Graph
0 likes · 18 min read
Large-Scale Graph Platform Dxm Eros for Financial Risk Control
DataFunSummit
DataFunSummit
Feb 7, 2022 · Artificial Intelligence

Financial Risk Control: Full Process, Data & Technology Requirements, and Visual Analytics Cases

This presentation explains the fundamentals of financial risk control, outlines the data and AI technologies needed across the pre‑loan, in‑loan, and post‑loan stages, and showcases visual‑analytics and federated‑learning case studies that improve model interpretability, monitoring, and enterprise risk management.

AIMachine Learningdata visualization
0 likes · 22 min read
Financial Risk Control: Full Process, Data & Technology Requirements, and Visual Analytics Cases
DeWu Technology
DeWu Technology
Feb 7, 2022 · Artificial Intelligence

Generalized Recommendation Solution for Transaction Scenarios

DeWu’s e‑commerce platform consolidated dozens of small‑scale transaction scenes into a universal personalized recommendation system by adopting a user‑to‑item DSSM dual‑tower model with unified sampling, category‑aware negative mining, cosine‑normalized embeddings, and real‑time serving, boosting click‑through rates by over 10% across all scenarios.

DSSME‑commerceMachine Learning
0 likes · 13 min read
Generalized Recommendation Solution for Transaction Scenarios
DataFunTalk
DataFunTalk
Feb 7, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can detect subtle correlations in large datasets, improve user growth metrics such as retention and activity, and presents practical methods like propensity score matching, uplift modeling, HTE analysis, and meta‑learners applied to recommendation systems.

Machine LearningPropensity Score MatchingUplift Modeling
0 likes · 13 min read
Causal Machine Learning for User Growth: Concepts, Methods, and Applications
21CTO
21CTO
Feb 4, 2022 · Artificial Intelligence

AlphaCode: DeepMind’s AI Beats Half of Human Programmers in Codeforces Contest

DeepMind’s AlphaCode, a transformer‑based AI trained on hundreds of gigabytes of code, demonstrated competitive programming abilities comparable to the middle 54% of human participants on Codeforces, highlighting significant progress in AI‑driven problem solving and code generation.

AI programmingAlphaCodeArtificial Intelligence
0 likes · 7 min read
AlphaCode: DeepMind’s AI Beats Half of Human Programmers in Codeforces Contest
IT Services Circle
IT Services Circle
Feb 1, 2022 · Fundamentals

Comprehensive Guide to Recommended Programming Videos and Resources for Python, Go, Frontend, and Data Analysis

This article curates a wide range of high‑quality video tutorials, books, and online resources covering Python fundamentals, web crawling, data analysis, machine learning, Go language development, and front‑end technologies, offering practical recommendations and rating scores to help learners choose suitable study material.

GoLearning ResourcesMachine Learning
0 likes · 12 min read
Comprehensive Guide to Recommended Programming Videos and Resources for Python, Go, Frontend, and Data Analysis
G7 EasyFlow Tech Circle
G7 EasyFlow Tech Circle
Jan 30, 2022 · Artificial Intelligence

Uncovering Road Freight Accident Causes with DoWhy & EconML: A Causal Inference Walkthrough

This article explains why causal inference is essential for decision‑making, contrasts it with pure prediction, outlines the four DoWhy steps (modeling, identification, estimation, refutation), and demonstrates a case study on road freight accidents using DoWhy and EconML with code examples and results.

DoWhyEconMLMachine Learning
0 likes · 16 min read
Uncovering Road Freight Accident Causes with DoWhy & EconML: A Causal Inference Walkthrough
Java High-Performance Architecture
Java High-Performance Architecture
Jan 25, 2022 · Cloud Native

Why Is Debugging Microservices on Kubernetes So Hard? Proven Strategies to Overcome It

Debugging microservices in a Kubernetes environment is challenging due to the abstraction of pods, network complexities, infrastructure issues, and application-level faults, but by monitoring at the service layer, aggregating data, and applying machine‑learning‑based anomaly detection, teams can effectively identify and resolve problems.

KubernetesMachine LearningTroubleshooting
0 likes · 6 min read
Why Is Debugging Microservices on Kubernetes So Hard? Proven Strategies to Overcome It
DataFunTalk
DataFunTalk
Jan 24, 2022 · Artificial Intelligence

Meituan Search Ranking: Multi‑Business Sorting Architecture and Optimization Practices

This article presents Meituan's search ranking system, detailing its multi‑business sorting architecture, layered ranking pipeline, quota and fine‑ranking models, aggregation modeling techniques, and supporting platforms such as Lego and Poker, while also sharing practical insights and recruitment information.

AIMachine LearningMeituan
0 likes · 16 min read
Meituan Search Ranking: Multi‑Business Sorting Architecture and Optimization Practices
Alimama Tech
Alimama Tech
Jan 19, 2022 · Artificial Intelligence

Advances in Alibaba Search Advertising Estimation: Model Deepening, Interaction, and System Efficiency (2021 Review)

The 2021 review of Alibaba’s Mama Search Advertising estimation platform details advances in model deepening—such as hash‑based embedding compression, adaptive dynamic parameters and graph neural networks—model interaction via a multi‑stage cascade with ranking distillation and oracle bias, and system efficiency gains from HPC training, mixed‑precision, multi‑hash embeddings, and fp16 quantization that deliver roughly a thirty‑fold speed‑up.

Ad TechCTRCVR
0 likes · 34 min read
Advances in Alibaba Search Advertising Estimation: Model Deepening, Interaction, and System Efficiency (2021 Review)
DataFunTalk
DataFunTalk
Jan 13, 2022 · Artificial Intelligence

Graph Neural Networks for Fraud Detection: Overview, Methods, and Resources

This article provides a comprehensive overview of fraud detection using graph neural networks, covering background definitions, fraud categories, GNN application steps, a timeline of key research papers, practical challenges, solutions, and a collection of open‑source resources and datasets.

AIMachine Learningfraud detection
0 likes · 24 min read
Graph Neural Networks for Fraud Detection: Overview, Methods, and Resources
DataFunTalk
DataFunTalk
Jan 12, 2022 · Artificial Intelligence

Advances in Knowledge Graph Construction: AI Development, Named Entity Recognition, Relation Extraction, and Attribute Completion

This technical report presents a comprehensive overview of artificial intelligence evolution, knowledge‑graph construction techniques—including traditional, cross‑lingual and reading‑comprehension based named entity recognition, weak‑supervised and joint relation extraction, attribute completion via multi‑source cues, and conditional knowledge‑graph modeling—highlighting recent research findings and experimental results.

AI developmentKnowledge GraphMachine Learning
0 likes · 20 min read
Advances in Knowledge Graph Construction: AI Development, Named Entity Recognition, Relation Extraction, and Attribute Completion
58 Tech
58 Tech
Jan 10, 2022 · Artificial Intelligence

Resource Utilization Optimization Practices for the 58.com Machine Learning Platform (WPAI)

This article details the 58.com WPAI machine learning platform's architecture and the optimizations applied to training task scheduling, inference service elastic scaling, and offline‑online resource mixing, demonstrating how these techniques significantly improve GPU/CPU utilization and inference performance across both GPU and CPU environments.

AIElastic ScalingInference Acceleration
0 likes · 27 min read
Resource Utilization Optimization Practices for the 58.com Machine Learning Platform (WPAI)
DataFunSummit
DataFunSummit
Jan 9, 2022 · Artificial Intelligence

Applying Graph Neural Networks to Fraud Detection: Background, Research Progress, Methods, and Resources

This article reviews the fundamentals of fraud, surveys the evolution of graph neural network research for fraud detection, outlines practical application steps, discusses key challenges such as disguise, scalability, and label scarcity, and provides representative papers, new research directions, industrial case studies, and open-source resources.

AIGNNMachine Learning
0 likes · 23 min read
Applying Graph Neural Networks to Fraud Detection: Background, Research Progress, Methods, and Resources
DataFunTalk
DataFunTalk
Jan 8, 2022 · Artificial Intelligence

Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM

This article surveys classic recommendation algorithms—including Logistic Regression, Factorization Machines, Field‑aware FM, Wide & Deep, DeepFM, DCN, and xDeepFM—explaining their principles, feature preprocessing, problem scopes, and industrial applications within personalized recommendation systems.

Deep LearningMachine LearningRecommendation Systems
0 likes · 12 min read
Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM
DataFunTalk
DataFunTalk
Jan 7, 2022 · Artificial Intelligence

Group-Theoretic Self-Supervised Representation Learning (Lecture)

On Jan 7, 2024, BIT’s “Hundred Lectures” will feature Assistant Professor Hanwang Zhang presenting his group‑theoretic self‑supervised representation learning work, including the IP‑IRM method that iteratively partitions data and applies invariant risk minimization to achieve fully disentangled visual features, with the session streamed via Tencent Meeting.

AIMachine LearningSelf-supervised Learning
0 likes · 4 min read
Group-Theoretic Self-Supervised Representation Learning (Lecture)
Amap Tech
Amap Tech
Jan 6, 2022 · Artificial Intelligence

Wuhan University and Amap Win the Smartphone‑Based Indoor Positioning Track at IPIN2021

Wuhan University and Amap clinched first place in IPIN 2021’s Smartphone‑Based Indoor Positioning track by fusing machine‑learning‑driven pedestrian dead‑reckoning with magnetic, Bluetooth and Wi‑Fi matching, achieving real‑time, high‑frequency indoor navigation using only a phone’s built‑in sensors.

IPIN2021Indoor positioningMachine Learning
0 likes · 6 min read
Wuhan University and Amap Win the Smartphone‑Based Indoor Positioning Track at IPIN2021
DataFunSummit
DataFunSummit
Jan 3, 2022 · Artificial Intelligence

Exploration of Alibaba's Feizhu Recommendation Algorithms and Full‑Space CVR Estimation Models (ESMM, ESM², HM³)

This article presents an in‑depth overview of Alibaba's e‑commerce and travel recommendation systems, covering the evolution of full‑space CVR estimation models such as ESMM, ESM² and HM³, their architectural components, challenges, and practical applications in the Feizhu platform.

AlibabaCVR estimationFull‑Space Modeling
0 likes · 25 min read
Exploration of Alibaba's Feizhu Recommendation Algorithms and Full‑Space CVR Estimation Models (ESMM, ESM², HM³)
DataFunSummit
DataFunSummit
Jan 1, 2022 · Artificial Intelligence

Intelligent Advertising Delivery System: Budget‑Constrained Bidding, Multi‑Constraint Bidding, Sequential Allocation, and Multi‑Channel Optimization

This article systematically introduces Alibaba's advertising intelligence platform, covering the evolution from simple CPM/CPC models to advanced budget‑constrained, multi‑constraint, and sequential bidding strategies, multi‑channel optimization, and reinforcement‑learning‑based solutions that jointly maximize advertiser ROI and platform revenue.

Machine LearningMulti-ChannelReinforcement Learning
0 likes · 34 min read
Intelligent Advertising Delivery System: Budget‑Constrained Bidding, Multi‑Constraint Bidding, Sequential Allocation, and Multi‑Channel Optimization
Code DAO
Code DAO
Jan 1, 2022 · Artificial Intelligence

Automating Machine Learning Workflows with Scikit‑Learn Pipelines

This article demonstrates how to build a reproducible fraud‑detection workflow using scikit‑learn's Pipeline class, comparing a manual script with a pipeline‑based approach on the IEEE‑CIS Kaggle dataset and showing the benefits of modular, repeatable ML code.

Machine LearningPipelinePython
0 likes · 8 min read
Automating Machine Learning Workflows with Scikit‑Learn Pipelines
DataFunTalk
DataFunTalk
Dec 30, 2021 · Artificial Intelligence

Push Notification Volume Optimization Using Uplift Modeling at Tencent Mobile QQ Browser

This article details Tencent's application of uplift modeling to optimize QQ Browser push notification volume, covering push system characteristics, causal analysis challenges, a refined S‑learner with metric learning, and resulting DAU improvements, while also addressing practical Q&A on uplift techniques.

DAU optimizationMachine LearningPush Notification
0 likes · 8 min read
Push Notification Volume Optimization Using Uplift Modeling at Tencent Mobile QQ Browser
Efficient Ops
Efficient Ops
Dec 29, 2021 · Artificial Intelligence

How AI Model Risk Governance Maturity Is Shaping Financial Fraud Prevention

The article details China's new AI model risk governance regulations, the CAICT 2021 GOLF+ IT Governance Forum, Tongdun Technology's successful maturity assessment for financial gambling‑fraud risk models, and insights from executives on implementation challenges, benefits, and future plans.

AI governanceMachine LearningRegulation
0 likes · 13 min read
How AI Model Risk Governance Maturity Is Shaping Financial Fraud Prevention
Python Programming Learning Circle
Python Programming Learning Circle
Dec 29, 2021 · Artificial Intelligence

Top 10 Python Machine‑Learning Libraries of 2021 (Including Notable Domestic Projects)

This article surveys the ten most prominent Python libraries for machine learning released in 2021, highlighting both internationally popular and high‑performing Chinese open‑source projects, and explains their main features, performance advantages, and where to find them on GitHub.

AI librariesMachine LearningPython
0 likes · 9 min read
Top 10 Python Machine‑Learning Libraries of 2021 (Including Notable Domestic Projects)
Efficient Ops
Efficient Ops
Dec 27, 2021 · Artificial Intelligence

Inside China Merchants Bank’s AI Model Risk Governance: Interview and Assessment Insights

China’s leading bank shares how its intelligent customer‑service semantic‑matching and multimodal service analysis models passed the AI model risk‑governance maturity assessment, detailing the governance measures, challenges faced, and future plans, while the CAICT framework that underpins the evaluation is explained.

AI ethicsAI risk governanceChina
0 likes · 13 min read
Inside China Merchants Bank’s AI Model Risk Governance: Interview and Assessment Insights
DataFunTalk
DataFunTalk
Dec 26, 2021 · Artificial Intelligence

Neural–Symbolic Learning and Multimodal Knowledge Discovery: Recent Advances, Methods, and Challenges

This talk reviews recent progress in neural‑symbolic learning and multimodal knowledge discovery, highlighting examples such as GPT‑3 reasoning failures, the need for symbolic knowledge, historical developments, various integration methods, challenges in multimodal knowledge graphs, and future research directions.

AIKnowledge GraphMachine Learning
0 likes · 20 min read
Neural–Symbolic Learning and Multimodal Knowledge Discovery: Recent Advances, Methods, and Challenges
Beike Product & Technology
Beike Product & Technology
Dec 23, 2021 · Artificial Intelligence

Highlights from the CNCC 2021 Knowledge Graph and Graph Machine Learning Forum

The CNCC 2021 forum brought together leading academics and industry experts to discuss advances in graph neural networks, graph computing for quantum chemistry, and practical applications of knowledge‑graph reasoning in sectors such as real‑estate and online video, showcasing both research breakthroughs and industrial deployment strategies.

Artificial IntelligenceIndustry ApplicationsKnowledge Graph
0 likes · 8 min read
Highlights from the CNCC 2021 Knowledge Graph and Graph Machine Learning Forum
Alimama Tech
Alimama Tech
Dec 22, 2021 · Industry Insights

How Alibaba’s Traffic Quality Team Detects and Mitigates Advertiser Arbitrage

This article details Alibaba Mama's traffic quality team's comprehensive approach to identifying and curbing advertiser arbitrage through crowdsourced traffic detection, statistical baselines, behavior‑sequence modeling, graph mining, RPM perception, insight platforms, and downstream remediation, highlighting challenges and future directions.

Ad FraudMachine Learningarbitrage
0 likes · 19 min read
How Alibaba’s Traffic Quality Team Detects and Mitigates Advertiser Arbitrage
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.

CatBoostMachine LearningModel Evaluation
0 likes · 5 min read
Introduction to CatBoost: Features, Advantages, and Practical Implementation
Python Crawling & Data Mining
Python Crawling & Data Mining
Dec 21, 2021 · Frontend Development

How to Build Interactive Machine‑Learning Web Apps with Streamlit: A Complete Guide

This tutorial explains how to install Streamlit, use its text, data, chart, and input components, manage layout, control flow, and caching, and demonstrates a full garbage‑classification web app that integrates a third‑party image‑recognition API, providing complete Python code examples.

Interactive UIMachine LearningStreamlit
0 likes · 15 min read
How to Build Interactive Machine‑Learning Web Apps with Streamlit: A Complete Guide
Code DAO
Code DAO
Dec 20, 2021 · Artificial Intelligence

Building Efficient Data Pipelines with TensorFlow’s tf.data API

This article explains how to use TensorFlow’s tf.data API to construct high‑performance, flexible data pipelines—from loading images or tensors, applying transformations and data augmentation, to batching, shuffling, caching, prefetching, and feeding the pipeline directly into model.fit for training.

Data PipelineMachine LearningPrefetch
0 likes · 9 min read
Building Efficient Data Pipelines with TensorFlow’s tf.data API
Code DAO
Code DAO
Dec 18, 2021 · Artificial Intelligence

Essential Feature Selection Techniques for Machine Learning

This article explains why feature selection is crucial for building robust machine‑learning models and walks through popular filter, wrapper, and embedded methods—including information gain, chi‑square, LASSO, random‑forest importance, and PCA—providing code examples and practical guidance.

Embedded MethodsFilter MethodsMachine Learning
0 likes · 18 min read
Essential Feature Selection Techniques for Machine Learning
Code DAO
Code DAO
Dec 18, 2021 · Artificial Intelligence

Implement Random Forest Regression in Python using Scikit-Learn

This article explains the fundamentals of random forest regression, describes why it outperforms single decision trees for nonlinear or noisy data, defines bootstrapping and bagging, and provides a step‑by‑step Python example using NumPy, Pandas, and Scikit‑Learn’s RandomForestRegressor with data loading, preprocessing, model training, prediction, and evaluation via MSE and R².

BootstrappingMachine LearningPython
0 likes · 6 min read
Implement Random Forest Regression in Python using Scikit-Learn
Code DAO
Code DAO
Dec 18, 2021 · Artificial Intelligence

Accelerating Gradient Boosting with CatBoost

This article explains how CatBoost implements gradient boosting, handles categorical features without preprocessing, lists its key advantages, details common training parameters, and provides a step‑by‑step regression example with code for fitting, cross‑validation, grid search, tree visualization, and parameter inspection.

CatBoostMachine Learninggradient boosting
0 likes · 7 min read
Accelerating Gradient Boosting with CatBoost
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 17, 2021 · Artificial Intelligence

Why GBDT Often Beats Neural Networks in Kaggle Competitions – An Analytical Deep Dive

This article analyzes why gradient‑boosted decision trees frequently outperform neural networks in many Kaggle contests, examining data characteristics, model strengths and weaknesses, real competition examples, and practical guidelines for choosing the right model based on nonlinearity and interpretability.

GBDTKaggleMachine Learning
0 likes · 9 min read
Why GBDT Often Beats Neural Networks in Kaggle Competitions – An Analytical Deep Dive
Code DAO
Code DAO
Dec 13, 2021 · Artificial Intelligence

A Comprehensive Guide to Ensemble Learning: Bagging, Boosting, and Stacking

This article explains the core concepts of ensemble learning, covering the bias‑variance trade‑off, the mechanics of bagging with bootstrap and random forests, the sequential strategies of boosting (AdaBoost and gradient boosting), and the heterogeneous stacking framework with meta‑models and multi‑layer extensions.

BaggingMachine LearningRandom Forest
0 likes · 20 min read
A Comprehensive Guide to Ensemble Learning: Bagging, Boosting, and Stacking
ITPUB
ITPUB
Dec 13, 2021 · Artificial Intelligence

How Data Augmentation Boosts Machine Learning When Data Is Scarce

This article explains how data augmentation can alleviate overfitting by artificially expanding limited training sets, outlines common transformation techniques for images, text, and audio, and discusses the method's benefits, practical applications, and inherent limitations for machine‑learning practitioners.

Data AugmentationDeep LearningMachine Learning
0 likes · 6 min read
How Data Augmentation Boosts Machine Learning When Data Is Scarce
DataFunTalk
DataFunTalk
Dec 12, 2021 · Artificial Intelligence

Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques

This talk presents the design and implementation of 58’s commercial recruitment recommendation system, covering the characteristics of the app’s recommendation scenario, system architecture, region‑based and behavior‑based recall methods, and coarse‑ and fine‑ranking models with various optimizations and future directions.

AIE‑commerceMachine Learning
0 likes · 20 min read
Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques
DataFunSummit
DataFunSummit
Dec 12, 2021 · Artificial Intelligence

Design and Implementation of 58.com Commercial Recruitment Recommendation System

This article presents a comprehensive overview of the 58.com commercial recruitment recommendation system, detailing its business challenges, system architecture, region‑based and behavior‑based recall strategies, coarse‑ and fine‑ranking models, bias handling, evaluation methods, and future directions.

CTRDBSCANEGES
0 likes · 20 min read
Design and Implementation of 58.com Commercial Recruitment Recommendation System
Code DAO
Code DAO
Dec 11, 2021 · Artificial Intelligence

How to Optimize Machine Learning Hyperparameters with GridSearchCV

This article explains how GridSearchCV automates hyperparameter tuning for machine‑learning models, demonstrates its use with a RandomForest classifier on the breast‑cancer dataset—including code, cross‑validation, best‑parameter results, and discusses its advantages and scalability limits.

GridSearchCVMachine LearningRandomForest
0 likes · 6 min read
How to Optimize Machine Learning Hyperparameters with GridSearchCV
Baidu App Technology
Baidu App Technology
Dec 7, 2021 · Artificial Intelligence

Paddle.js OCR SDK: Text Recognition in Web Browsers

Paddle.js OCR SDK brings Baidu’s lightweight PaddleOCR models to web browsers, offering init() and recognize() APIs that load the ch_PP-OCRv2 detection (DB) and recognition (CRNN with bidirectional LSTM) models in parallel, achieving 258 ms detection, 60 ms recognition, 0.52 F‑score, and a combined size under 12 MB.

AIMachine LearningOCR
0 likes · 7 min read
Paddle.js OCR SDK: Text Recognition in Web Browsers
DataFunTalk
DataFunTalk
Dec 6, 2021 · Artificial Intelligence

Observational Data Causal Inference: Fundamentals, Quasi‑Experimental Methods, and Tencent Case Studies

This article provides a comprehensive overview of causal inference on observational data, explaining confounding and collider structures, experimental solutions, the differences between observational and experimental data, challenges such as Simpson's paradox, and detailed Tencent case studies using DID, regression discontinuity, and uplift modeling to guide practical analysis.

DIDMachine LearningQuasi-experiment
0 likes · 26 min read
Observational Data Causal Inference: Fundamentals, Quasi‑Experimental Methods, and Tencent Case Studies
Meituan Technology Team
Meituan Technology Team
Dec 2, 2021 · Artificial Intelligence

Pretraining Techniques for Search Advertising Relevance at Meituan

Meituan improves search‑ad relevance by applying pre‑trained BERT models enhanced with data‑augmented samples, multi‑task learning, keyword extraction and two‑stage knowledge distillation, producing a lightweight distilled model that, when fused with traditional relevance signals, boosts CTR, lowers Badcase@5 and raises NDCG while preserving revenue.

BERTMachine Learningadvertising relevance
0 likes · 30 min read
Pretraining Techniques for Search Advertising Relevance at Meituan
21CTO
21CTO
Nov 30, 2021 · Artificial Intelligence

Why Twitter’s New CEO Is Tackling Algorithmic Bias and What It Means for AI

Jack Dorsey stepped down as Twitter CEO, appointing CTO Parag Agrawal—an AI‑focused leader who aims to reduce algorithmic bias, advance open‑source social standards, and drive growth—while the company’s stock rose and broader industry trends in leadership were highlighted.

AI biasCEO transitionLeadership
0 likes · 7 min read
Why Twitter’s New CEO Is Tackling Algorithmic Bias and What It Means for AI
Code DAO
Code DAO
Nov 29, 2021 · Artificial Intelligence

Feature Selection: Reducing Input Variables for Predictive Modeling

This article explains the purpose and types of feature selection, compares supervised and unsupervised, wrapper, filter, and embedded methods, discusses choosing statistical metrics based on variable types, and provides scikit‑learn code examples for regression and classification tasks.

Embedded MethodsFilter MethodsMachine Learning
0 likes · 12 min read
Feature Selection: Reducing Input Variables for Predictive Modeling
DataFunTalk
DataFunTalk
Nov 28, 2021 · Artificial Intelligence

Fine‑Grained Content Understanding and Operation in QQ Music: Optimizing the Recommendation System

This article presents QQ Music’s end‑to‑end solution for data‑driven content understanding, value evaluation, and fine‑grained operation, detailing offline and real‑time pipelines, neural‑network models, a content middle‑platform, parameter services, and a precise delivery system that boost user engagement while preserving experience.

AI modelsMachine Learningcontent understanding
0 likes · 24 min read
Fine‑Grained Content Understanding and Operation in QQ Music: Optimizing the Recommendation System
DataFunSummit
DataFunSummit
Nov 27, 2021 · Artificial Intelligence

Knowledge Graph Construction, Applications, and Recent Advances in Entity Linking

This article reviews the fundamentals of knowledge graphs, their practical uses in question answering, search and recommendation, and surveys recent research on entity linking—including dual‑encoder retrieval, BERT‑based models, multilingual approaches, and zero‑shot methods—while also outlining modern knowledge‑graph construction pipelines and open challenges.

Knowledge GraphMachine LearningNatural Language Processing
0 likes · 21 min read
Knowledge Graph Construction, Applications, and Recent Advances in Entity Linking
DataFunTalk
DataFunTalk
Nov 26, 2021 · Artificial Intelligence

Graph Neural Networks for Molecular Networks and Drug Discovery

This presentation by Stanford PhD student Huang Kexin explores the challenges and innovations of applying graph machine learning to molecular and biomedical networks, introducing specialized GNN architectures, actionable hypothesis generation, domain‑scientist interfaces, few‑shot learning, and the Therapeutics Data Commons for accelerating drug discovery.

BioinformaticsMachine Learningbiomedical AI
0 likes · 9 min read
Graph Neural Networks for Molecular Networks and Drug Discovery