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DataFunSummit
DataFunSummit
May 11, 2024 · Artificial Intelligence

Why Causal Inference Matters in Machine Learning and Its Banking Applications

The article explains the necessity of incorporating causal relationships into machine learning, outlines the development of causal science, and details how uplift modeling and causal‑regularized stable learning are applied to marketing and risk control in the banking sector, while also discussing practical challenges and experimental results.

BankingUplift Modelingcausal inference
0 likes · 14 min read
Why Causal Inference Matters in Machine Learning and Its Banking Applications
Python Programming Learning Circle
Python Programming Learning Circle
May 11, 2024 · Artificial Intelligence

A Comprehensive Overview of Popular Python Libraries for Artificial Intelligence and Data Science

This article introduces and demonstrates more than twenty widely used Python libraries for artificial intelligence, computer vision, natural language processing, and data analysis, providing concise explanations and runnable code snippets that illustrate each library's core functionality and typical use cases.

Artificial IntelligenceData ScienceNumPy
0 likes · 29 min read
A Comprehensive Overview of Popular Python Libraries for Artificial Intelligence and Data Science
DataFunTalk
DataFunTalk
May 9, 2024 · Artificial Intelligence

Graph Model Practices and Applications in Baidu Recommendation System

This article introduces the background of graph data, explains common graph modeling algorithms such as graph embedding and graph neural networks, compares their strengths, and details the evolution and large‑scale deployment of Feed graph models in Baidu's recommendation platform.

BaiduEmbeddinggraph models
0 likes · 11 min read
Graph Model Practices and Applications in Baidu Recommendation System
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
May 9, 2024 · Artificial Intelligence

On‑Device AI and Federated Learning: Era Background, Theory, and Practical Applications

This article outlines the evolution from 1G to 6G communications, explains the third AI wave driven by big data, theory, and compute, introduces federated learning (horizontal, vertical, transfer), and details on‑device AI architectures, decision tree and neural network models, and real‑world use cases such as video preloading and autonomous driving.

Artificial IntelligenceBig DataEdge Computing
0 likes · 13 min read
On‑Device AI and Federated Learning: Era Background, Theory, and Practical Applications
DataFunSummit
DataFunSummit
May 7, 2024 · Artificial Intelligence

Regional Heterogeneity in Game AB Experiments: Detection, Decomposition, and Prediction

This article examines how game AB experiments can exhibit significant regional differences, outlines a meta‑analysis framework to detect heterogeneity, decomposes its sources into treatment‑effect and distributional factors, and demonstrates how to predict outcomes for unseen regions using machine‑learning models.

AB testingCATEcausal inference
0 likes · 11 min read
Regional Heterogeneity in Game AB Experiments: Detection, Decomposition, and Prediction
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
May 5, 2024 · Artificial Intelligence

Comprehensive Guide to Neural Network Algorithms: Definitions, Structure, Implementation, and Training

This article provides an in‑depth tutorial on neural network algorithms, covering their biological inspiration, significance, advantages and drawbacks, detailed architecture, data preparation, one‑hot encoding, weight initialization, forward and backward propagation, cost functions, regularization, gradient checking, and complete Python code examples.

AIBackpropagationNeural Networks
0 likes · 37 min read
Comprehensive Guide to Neural Network Algorithms: Definitions, Structure, Implementation, and Training
IT Services Circle
IT Services Circle
May 2, 2024 · Artificial Intelligence

LLM.c: A 1000‑Line C Implementation for Training GPT‑2

Andrej Karpathy’s LLM.c project demonstrates how a compact, pure‑C (and CUDA) codebase of roughly 1000 lines can train a GPT‑2 model, covering data preparation, memory management, layer implementations, compilation, and practical tips for running and testing the model on CPUs and GPUs.

AICUDAGPT-2
0 likes · 10 min read
LLM.c: A 1000‑Line C Implementation for Training GPT‑2
DataFunSummit
DataFunSummit
May 1, 2024 · Artificial Intelligence

Causal Solutions for Recommendation System Bias and Practical Applications

This article presents causal inference–based methods to address bias in recommendation systems, covering the transformation of recommendation problems into causal problems, selection bias mitigation through double‑robust and multi‑robust learning, individual treatment effect estimation, and a case study on attention bias in music recommendation.

bias mitigationcausal inferencedouble robust learning
0 likes · 12 min read
Causal Solutions for Recommendation System Bias and Practical Applications
JD Cloud Developers
JD Cloud Developers
Apr 30, 2024 · Artificial Intelligence

Build a Handwritten Digit Recognizer in Java with TensorFlow

This article walks through the complete process of creating, training, evaluating, saving, and loading a MNIST handwritten digit recognition model using TensorFlow in Java, comparing it with the equivalent Python implementation and covering required knowledge, environment setup, and code details.

Deep LearningMNISTTensorFlow
0 likes · 34 min read
Build a Handwritten Digit Recognizer in Java with TensorFlow
Ximalaya Technology Team
Ximalaya Technology Team
Apr 30, 2024 · Artificial Intelligence

Multi‑Stage Funnel Architecture and Optimization Practices in an Advertising Engine

The advertising engine uses a five‑stage funnel—retrieval, recall, coarse ranking, fine ranking, and re‑ranking—each optimized with specialized indexes, multi‑channel recall, multi‑objective twin‑tower models, deep CTR/CVR predictors, and cold‑start paths, delivering up to 33 % spend growth, 6 % eCPM lift and lower latency while maintaining diversity.

Advertisingcold starteCPM
0 likes · 15 min read
Multi‑Stage Funnel Architecture and Optimization Practices in an Advertising Engine
Software Development Quality
Software Development Quality
Apr 29, 2024 · Fundamentals

How Precise Testing Boosts Software Quality and Efficiency

Precise testing, a modern approach that defines clear test goals, leverages code analysis, coverage tools, and machine‑learning‑driven test selection, can dramatically improve software quality and efficiency, as demonstrated by case studies in finance, medical devices, and e‑commerce, while also reducing costs.

Software Testingautomationmachine learning
0 likes · 4 min read
How Precise Testing Boosts Software Quality and Efficiency
DataFunSummit
DataFunSummit
Apr 28, 2024 · Artificial Intelligence

Graph Knowledge Transfer: Methods, Practices, and the Knowledge Bridge Learning Framework

This article presents a comprehensive overview of graph knowledge transfer, covering its definition, the data‑hungry problem, distribution shift challenges, the Knowledge Bridge Learning (KBL) framework, the Bridged‑GNN model, extensive experiments on real‑world scenarios, and a concluding Q&A session.

Knowledge Transferdomain adaptationgraph learning
0 likes · 22 min read
Graph Knowledge Transfer: Methods, Practices, and the Knowledge Bridge Learning Framework
Python Programming Learning Circle
Python Programming Learning Circle
Apr 26, 2024 · Artificial Intelligence

Five Essential Python Libraries for Machine Learning Engineers

This article introduces five essential Python libraries—MLflow, Streamlit, FastAPI, XGBoost, and ELI5—that every junior or intermediate machine‑learning engineer and data scientist should master to streamline experiment tracking, build interactive web apps, deploy models efficiently, achieve fast accurate predictions, and improve model interpretability.

ELI5FastAPIPython
0 likes · 8 min read
Five Essential Python Libraries for Machine Learning Engineers
JD Retail Technology
JD Retail Technology
Apr 24, 2024 · Backend Development

Design and Optimization of JD Advertising Retrieval Platform: Adaptive Compute Allocation, High‑Efficiency Search Engine, and Platform‑Scale Infrastructure

The article presents a comprehensive overview of JD's advertising retrieval platform, detailing how it balances limited compute resources with massive data through adaptive compute allocation, distributed execution graphs, elastic systems, and multi‑stage algorithmic improvements to achieve high‑performance, scalable ad matching.

AdvertisingJD.comcompute optimization
0 likes · 22 min read
Design and Optimization of JD Advertising Retrieval Platform: Adaptive Compute Allocation, High‑Efficiency Search Engine, and Platform‑Scale Infrastructure
Python Programming Learning Circle
Python Programming Learning Circle
Apr 18, 2024 · Artificial Intelligence

Implementing an Automatic Math Expression Grading System with Python and Convolutional Neural Networks

This tutorial walks through building a self‑trained OCR pipeline that generates synthetic digit images, trains a CNN model, segments handwritten math expressions, predicts each character, evaluates the arithmetic result, and overlays checkmarks, crosses or answers onto the original image.

CNNImage ProcessingOCR
0 likes · 28 min read
Implementing an Automatic Math Expression Grading System with Python and Convolutional Neural Networks
21CTO
21CTO
Apr 18, 2024 · Artificial Intelligence

GPT‑6, VAR Models, and the Latest AI Breakthroughs Shaping Tech

The article surveys recent AI and tech developments, from Sam Altman's claim that GPT‑6 will become a universal tool and Baidu's new intelligent computing OS, to Peking University and ByteDance's VAR model outperforming diffusion models, plus updates on Boston Dynamics' Atlas robot, Linux kernel Kconfig, AMD Ryzen Pro CPUs, and SQLite 3.45.3.

Artificial IntelligenceLinux kernelRobotics
0 likes · 11 min read
GPT‑6, VAR Models, and the Latest AI Breakthroughs Shaping Tech
JD Retail Technology
JD Retail Technology
Apr 15, 2024 · Artificial Intelligence

Design and Evolution of JD.com Recommendation Advertising Ranking Auction Mechanism

The article analyzes JD.com's recommendation advertising ranking auction mechanism, detailing its objectives, challenges in traffic value estimation, user interest exploration, and multi‑item auction fairness, and describing the technical evolution from traditional auctions to deep‑learning‑driven solutions.

Advertisingauctione‑commerce
0 likes · 18 min read
Design and Evolution of JD.com Recommendation Advertising Ranking Auction Mechanism
21CTO
21CTO
Apr 13, 2024 · Artificial Intelligence

Why Amazon’s CEO Calls Generative AI the Biggest Tech Shift Since the Cloud

In a shareholder letter, Amazon CEO Andy Jassy outlines the company’s generative AI strategy, describing it as the most significant technological transformation since cloud computing, detailing AWS’s infrastructure investments, new services like Bedrock, and the broader impact on developers and customers.

AWSArtificial Intelligencefoundation-models
0 likes · 8 min read
Why Amazon’s CEO Calls Generative AI the Biggest Tech Shift Since the Cloud
Sohu Tech Products
Sohu Tech Products
Apr 10, 2024 · Artificial Intelligence

Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations

The presentation surveys recent causal‑inference research for recommendation systems, introducing the DICE framework to separate user interest from conformity, the CLSR model to disentangle long‑term and short‑term preferences, and the DVR approach with WTG metrics to debias short‑video recommendations, demonstrating improved accuracy, fairness, and interpretability.

bias mitigationcausal inferenceinterest disentanglement
0 likes · 23 min read
Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations
DataFunTalk
DataFunTalk
Apr 7, 2024 · Artificial Intelligence

Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This presentation reviews recent research on applying causal inference to recommendation systems, covering causal embedding for separating user interest and conformity, contrastive learning for disentangling long‑term and short‑term interests, and a debiasing framework for short‑video recommendation that uses watch‑time‑gain metrics and adversarial learning to mitigate duration bias.

bias mitigationcausal inferenceinterest disentanglement
0 likes · 23 min read
Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations
Test Development Learning Exchange
Test Development Learning Exchange
Apr 4, 2024 · Artificial Intelligence

Scikit‑Optimize (skopt): Features, Use Cases, and Code Examples

Scikit‑Optimize is a Python library for black‑box optimization that offers adaptable, efficient algorithms, hyper‑parameter tuning, interactive monitoring, and seamless Scikit‑Learn integration, illustrated with five comprehensive code examples covering basic usage, constrained and interactive optimization, and visualization.

Bayesian OptimizationBlack-Box Optimizationhyperparameter tuning
0 likes · 7 min read
Scikit‑Optimize (skopt): Features, Use Cases, and Code Examples
Python Programming Learning Circle
Python Programming Learning Circle
Apr 2, 2024 · Artificial Intelligence

Overview of Common Python Libraries for Artificial Intelligence and Data Science with Code Examples

This article provides a comprehensive introduction to popular Python libraries for artificial intelligence, computer vision, data analysis, and machine learning—such as NumPy, OpenCV, scikit‑image, Pillow, TensorFlow, PyTorch, and many others—accompanied by concise code snippets and performance comparisons to help beginners select suitable tools.

AI librariesCode ExamplesData Science
0 likes · 33 min read
Overview of Common Python Libraries for Artificial Intelligence and Data Science with Code Examples
DataFunTalk
DataFunTalk
Mar 28, 2024 · Artificial Intelligence

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

This article presents a comprehensive overview of multi‑task and multi‑scenario recommendation algorithms, detailing background challenges, algorithm classifications such as TAML, CausalInt, and DFFM, their modular designs, experimental validations, and practical Q&A insights for large‑scale advertising systems.

advertising algorithmsmachine learningmulti-task learning
0 likes · 19 min read
Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications
NewBeeNLP
NewBeeNLP
Mar 28, 2024 · Industry Insights

How Meta’s HSTU Architecture Scales Recommendation Systems Beyond Decades of Deep Models

Meta introduces a generative recommendation framework (GR) built on the Hierarchical Sequential Transduction Unit (HSTU) that unifies heterogeneous features, treats user behavior as a new modality, and leverages novel encoder and inference optimizations to achieve order‑of‑magnitude scaling in model size, training compute, and online latency while delivering 12‑18% online gains over traditional deep recommendation models.

Generative ModelsHSTUMeta
0 likes · 36 min read
How Meta’s HSTU Architecture Scales Recommendation Systems Beyond Decades of Deep Models
Python Programming Learning Circle
Python Programming Learning Circle
Mar 23, 2024 · Artificial Intelligence

Eight Python Libraries to Accelerate Data‑Science Workflows

This article introduces eight Python libraries—including Optuna, ITMO_FS, shap‑hypetune, PyCaret, floWeaver, Gradio, Terality, and Torch‑Handle—that streamline data‑science tasks such as hyperparameter optimization, feature selection, model building, visualization, and deployment, helping users save coding time and improve productivity.

Data SciencePythonautomation
0 likes · 12 min read
Eight Python Libraries to Accelerate Data‑Science Workflows
Liangxu Linux
Liangxu Linux
Mar 23, 2024 · Artificial Intelligence

Understanding AI Neurons: A Storytelling Guide to Basics of Neural Networks

This article uses a narrative of an AI neuron to explain fundamental concepts of neural networks, including neuron structure, weighted sums, activation functions, loss functions, gradient descent, and learning rate, making complex AI topics accessible to beginners.

AI basicsNeural Networkactivation function
0 likes · 9 min read
Understanding AI Neurons: A Storytelling Guide to Basics of Neural Networks
TAL Education Technology
TAL Education Technology
Mar 20, 2024 · Artificial Intelligence

Understanding AI: From Brain Differences to Data Science Practices and Large Model Applications

This article explains why current AI cannot achieve self‑awareness, outlines data‑science steps for large models—including preprocessing, exploratory analysis, modeling, and evaluation—then surveys general and vertical applications of large language models and details a complete machine‑learning workflow with transformer fine‑tuning techniques.

AIApplicationsData Science
0 likes · 14 min read
Understanding AI: From Brain Differences to Data Science Practices and Large Model Applications
DataFunSummit
DataFunSummit
Mar 19, 2024 · Artificial Intelligence

Modeling Price-Demand Relationships for Online Hotel Booking: Demand Functions, Causal Inference, and Multi-Scenario Joint Modeling

This article explores the challenges of estimating hotel occupancy in online booking platforms and presents four comprehensive approaches—background analysis, demand‑function based quantity‑price modeling, causal‑inference modeling, and multi‑scenario joint modeling—highlighting novel models, datasets, and experimental results for dynamic pricing optimization.

Demand Modelingcausal inferencedynamic pricing
0 likes · 11 min read
Modeling Price-Demand Relationships for Online Hotel Booking: Demand Functions, Causal Inference, and Multi-Scenario Joint Modeling
Bitu Technology
Bitu Technology
Mar 15, 2024 · Artificial Intelligence

Monitoring Quality Issues in Tubi’s Recommendation System

This article explains how Tubi monitors the quality of its recommendation system by identifying potential failure points, tracking key data streams such as model input, final recommendation output, and training data, and designing a scalable, real‑time monitoring solution with clear protocols and extensible metrics.

Data QualityReal-TimeScalability
0 likes · 11 min read
Monitoring Quality Issues in Tubi’s Recommendation System
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 15, 2024 · Artificial Intelligence

Why Arithmetic Feature Interaction Is Key to Deep Tabular Learning

Researchers from Alibaba Cloud AI and Zhejiang University present AMFormer, a Transformer‑based model that incorporates arithmetic feature interaction, demonstrating superior fine‑grained modeling, sample efficiency, and generalization on synthetic and real‑world tabular datasets, establishing a new state‑of‑the‑art in deep tabular learning.

AMFormerDeep LearningTransformer
0 likes · 12 min read
Why Arithmetic Feature Interaction Is Key to Deep Tabular Learning
ITPUB
ITPUB
Mar 13, 2024 · Artificial Intelligence

From AlphaGo to ChatGPT: Unraveling the Secrets Behind Modern AI Breakthroughs

This article walks readers through the evolution of artificial intelligence—from early expert systems and machine learning basics to convolutional neural networks, the AlphaGo series, MuZero's rule‑free learning, and the generative power of large language models like ChatGPT—highlighting how deep learning, Monte Carlo tree search, and self‑play collaborate to achieve unprecedented performance across games, science, and language.

AIAlphaGoChatGPT
0 likes · 39 min read
From AlphaGo to ChatGPT: Unraveling the Secrets Behind Modern AI Breakthroughs
21CTO
21CTO
Mar 12, 2024 · Artificial Intelligence

Top 10 Python Libraries Every Data Scientist Must Master in 2024

Discover the essential Python libraries for data science in 2024, from versatile tools like Taipy and Pandas to powerful machine‑learning frameworks such as TensorFlow, PyTorch, and Scikit‑Learn, each with key features, use‑cases, and GitHub links to boost your analytics career.

AIData SciencePython
0 likes · 7 min read
Top 10 Python Libraries Every Data Scientist Must Master in 2024
Python Programming Learning Circle
Python Programming Learning Circle
Mar 12, 2024 · Fundamentals

Visual Guide to NumPy: Creating Arrays, Operations, Indexing, and Applications

This tutorial provides a visual, step‑by‑step guide to NumPy, covering array creation, arithmetic and broadcasting, indexing, aggregation, matrix operations, reshaping, and practical examples such as computing mean‑squared error for machine‑learning models, illustrated with code snippets and diagrams.

Array OperationsData SciencePython
0 likes · 10 min read
Visual Guide to NumPy: Creating Arrays, Operations, Indexing, and Applications
DataFunTalk
DataFunTalk
Mar 11, 2024 · Artificial Intelligence

Anomaly Detection and Attribution Diagnosis Practices at Ant Financial

This article presents Ant Financial's practical approaches to anomaly detection and attribution diagnosis, detailing the underlying concepts, four methodological categories, specific algorithms such as VBEM, AnoSVGD and Autoformer, multi‑dimensional factor analysis, real‑world challenges, and operational benefits for KPI monitoring and incident response.

AIAttribution Analysisanomaly detection
0 likes · 13 min read
Anomaly Detection and Attribution Diagnosis Practices at Ant Financial
Efficient Ops
Efficient Ops
Mar 10, 2024 · Databases

How Machine Learning Can Automate MySQL Index Optimization

This article explains how applying machine learning to database operations—specifically AIOps for MySQL—can automate index recommendation by parsing SQL, extracting semantic and statistical features, generating candidate index combinations, and training an XGBoost model to predict optimal indexes, reducing reliance on manual DBA work.

Index OptimizationMySQLaiops
0 likes · 10 min read
How Machine Learning Can Automate MySQL Index Optimization
DataFunSummit
DataFunSummit
Mar 9, 2024 · Artificial Intelligence

OPPO Advertising Recall Algorithm: Architecture, Model Selection, Offline Evaluation, Sample Optimization, and Future Directions

This article presents OPPO's comprehensive advertising recall system, detailing the transition from the old to the new architecture with ANN support, the selection of main‑road recall models, the construction of offline evaluation metrics, sample optimization techniques, model enhancements, multi‑scenario training strategies, and outlook for future improvements.

Advertisingdual-tower modellarge-scale classification
0 likes · 24 min read
OPPO Advertising Recall Algorithm: Architecture, Model Selection, Offline Evaluation, Sample Optimization, and Future Directions
Model Perspective
Model Perspective
Mar 8, 2024 · Artificial Intelligence

Master the Three Machine Learning Types and Model Paradigms

This article introduces the three core machine learning categories—supervised, unsupervised, and reinforcement learning—detailing their definitions, typical algorithms, and real‑world applications, and then compares generative and discriminative models, highlighting key examples, characteristics, and use‑case differences.

Discriminative ModelsGenerative ModelsUnsupervised Learning
0 likes · 13 min read
Master the Three Machine Learning Types and Model Paradigms
Sohu Tech Products
Sohu Tech Products
Mar 6, 2024 · Artificial Intelligence

Mastering Regression: A Comprehensive Guide to Linear and Non‑Linear Models

This article provides an in‑depth overview of regression prediction, covering linear models like OLS, Lasso, Ridge, and Bayesian approaches, as well as non‑linear techniques such as tree ensembles, SVR, KNN, neural networks, and advanced deep learning frameworks for tabular data.

Deep Learninggradient boostinglinear models
0 likes · 13 min read
Mastering Regression: A Comprehensive Guide to Linear and Non‑Linear Models
IT Services Circle
IT Services Circle
Mar 6, 2024 · Artificial Intelligence

Comprehensive Overview of Ten Regression Algorithms with Core Concepts and Code Examples

This article provides a comprehensive summary of ten regression algorithms—including linear, ridge, Lasso, decision tree, random forest, gradient boosting, SVR, XGBoost, LightGBM, and neural network regression—detailing their principles, advantages, disadvantages, suitable scenarios, and offering core Python code examples for each.

Pythongradient boostingmachine learning
0 likes · 33 min read
Comprehensive Overview of Ten Regression Algorithms with Core Concepts and Code Examples
DataFunTalk
DataFunTalk
Mar 6, 2024 · Artificial Intelligence

Construction and Practical Application of a User Profile Tagging System

This article details the design, integration, and operational practices of a comprehensive user and item profiling tag system, covering tag taxonomy, construction methods, update cycles, access strategies, algorithmic implementations, and real‑world applications such as marketing, attribution analysis, and A/B testing.

AB testingTagging Systemdata mining
0 likes · 20 min read
Construction and Practical Application of a User Profile Tagging System
IT Xianyu
IT Xianyu
Mar 5, 2024 · Artificial Intelligence

Open-Source AI Platform A‑SOiD Enables Video‑Based Behavior Recognition and Prediction

Researchers from Carnegie Mellon University and the University of Bonn have released the open‑source A‑SOiD platform, which learns and predicts user‑defined behaviors solely from video, offering transparent, bias‑aware AI that can be applied to animal studies, human actions, and diverse pattern‑recognition domains.

AIbehavior recognitionmachine learning
0 likes · 6 min read
Open-Source AI Platform A‑SOiD Enables Video‑Based Behavior Recognition and Prediction
MaGe Linux Operations
MaGe Linux Operations
Mar 5, 2024 · Cloud Native

How to Run GPU‑Accelerated AI Workloads on Kubernetes

This article explains how Kubernetes supports GPU workloads for AI and machine learning, covering device plugins, pod GPU requests, oversubscription, security isolation, cloud‑provider node setup, and protecting GPU nodes from non‑GPU pods.

AI workloadsCloud NativeDevice Plugin
0 likes · 8 min read
How to Run GPU‑Accelerated AI Workloads on Kubernetes
php Courses
php Courses
Mar 5, 2024 · Artificial Intelligence

Anomaly Detection and Outlier Handling in PHP Using Machine Learning

This article explains how to detect and handle outliers in datasets using PHP and machine‑learning techniques, covering Z‑Score and Isolation Forest algorithms as well as methods to delete or replace anomalous values to improve data quality and model accuracy.

Isolation ForestPHPanomaly detection
0 likes · 5 min read
Anomaly Detection and Outlier Handling in PHP Using Machine Learning
DaTaobao Tech
DaTaobao Tech
Mar 4, 2024 · Artificial Intelligence

Iris Classification with Machine Learning: Data Exploration and Classic Algorithms

This beginner-friendly guide walks through loading the classic Iris dataset, performing exploratory data analysis, and implementing four fundamental classifiers—Decision Tree, Logistic Regression, Support Vector Machine, and K‑Nearest Neighbors—complete with training, visualization, and accuracy evaluation, illustrating a full machine‑learning workflow.

classificationdecision treeiris dataset
0 likes · 22 min read
Iris Classification with Machine Learning: Data Exploration and Classic Algorithms
php Courses
php Courses
Mar 4, 2024 · Artificial Intelligence

Integrating AI and Machine Learning into Laravel Web Development

This article explores how Laravel can serve as a flexible backend platform for integrating artificial intelligence and machine learning technologies—such as predictive analytics, chatbots, image/video analysis, and recommendation systems—by presenting practical code examples, discussing opportunities, challenges, and best‑practice tools.

AIChatbotLaravel
0 likes · 9 min read
Integrating AI and Machine Learning into Laravel Web Development
DataFunTalk
DataFunTalk
Mar 2, 2024 · Artificial Intelligence

Construction and Application of User Portraits in Credit Scenarios

This article explains how to build a comprehensive user‑portrait feature system for credit business, covering business goals, data collection, labeling, modeling workflow, technical challenges, multi‑source fusion, deployment, evaluation, management, practical applications, and future extensions using AI and big‑data techniques.

credit riskdata fusionfinancial technology
0 likes · 18 min read
Construction and Application of User Portraits in Credit Scenarios
DataFunSummit
DataFunSummit
Feb 27, 2024 · Artificial Intelligence

Algorithmic Approaches for Hotel Category Planning, Group Recommendation, and Large‑Promotion Selection in Fliggy Travel

This article presents Fliggy Travel's end‑to‑end algorithmic solutions for hotel category planning, introduces the LINet group‑recommendation model that incorporates location and travel intent, and details the PETS two‑stage model for selecting hot‑sale hotels under recall constraints, together with experimental results and practical insights.

AIgroup recommendationhotel supply chain
0 likes · 14 min read
Algorithmic Approaches for Hotel Category Planning, Group Recommendation, and Large‑Promotion Selection in Fliggy Travel
JD Retail Technology
JD Retail Technology
Feb 26, 2024 · Artificial Intelligence

Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain

The article details JD’s smart supply‑chain innovations, describing an explainable AI forecasting method that boosts prediction accuracy while maintaining interpretability, and an end‑to‑end inventory management model based on multi‑quantile RNNs that improves replenishment decisions, reduces costs, and enhances overall operational efficiency.

Supply Chainexplainable AIforecasting
0 likes · 14 min read
Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain
NewBeeNLP
NewBeeNLP
Feb 25, 2024 · Interview Experience

Comprehensive Interview Question Cheat Sheet for Top Tech Companies

This article compiles a detailed list of interview question topics from leading tech firms—including search, algorithm engineering, NLP, multimodal LLMs, advertising, recommendation, risk control, and big‑data domains—covering algorithms, system design, machine‑learning concepts, and practical coding challenges.

AlgorithmsBig DataNLP
0 likes · 10 min read
Comprehensive Interview Question Cheat Sheet for Top Tech Companies
DataFunTalk
DataFunTalk
Feb 24, 2024 · Artificial Intelligence

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

This article introduces causal learning, explains its distinction from traditional correlation‑based machine learning, outlines its three main parts, discusses the two primary paradigms—learning with known causal graphs and learning via causal discovery—and highlights their advantages, challenges, and recent research directions.

Deep Learningcausal discoverycausal inference
0 likes · 11 min read
Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery
Bilibili Tech
Bilibili Tech
Feb 18, 2024 · Artificial Intelligence

Bilibili Personal Attack Content Governance: Background, Goals, Methods, and Effectiveness

Bilibili combats personal‑attack and trolling comments by combining sector‑specific keyword databases, user‑group analysis, advanced word‑matching (including pinyin and homophone detection) and multiple NLP/graph models, which has cut personal‑attack reports in entertainment, film and gaming by about 32 % and trolling reports by roughly 25 % between June and December 2023.

Bilibiliabusive language detectioncontent moderation
0 likes · 12 min read
Bilibili Personal Attack Content Governance: Background, Goals, Methods, and Effectiveness
DataFunSummit
DataFunSummit
Feb 14, 2024 · Artificial Intelligence

Causal Debiasing Methods for Ant Group's Marketing Recommendation Scenarios

This article presents Ant Group's research on causal debiasing for recommendation and marketing, covering the background of bias, common bias types, causal graph analysis, two correction approaches—data‑fusion based MDI and back‑door adjustment based DMBR—along with experimental results on public and proprietary datasets and real‑world deployment insights.

Ant GroupBias CorrectionMarketing
0 likes · 16 min read
Causal Debiasing Methods for Ant Group's Marketing Recommendation Scenarios
DataFunTalk
DataFunTalk
Feb 4, 2024 · Artificial Intelligence

Applying Causal Inference Techniques to Short‑Video Recommendation at Kuaishou

This article presents how causal inference methods are applied to Kuaishou’s single‑column short‑video recommendation, covering the platform’s recommendation scenario, model representations, duration bias mitigation, viewing‑time prediction techniques such as D2Q and TPM, experimental results, and future research directions.

Kuaishoucausal inferenceduration bias
0 likes · 19 min read
Applying Causal Inference Techniques to Short‑Video Recommendation at Kuaishou
DataFunTalk
DataFunTalk
Feb 2, 2024 · Artificial Intelligence

Utilizing Negative Samples for Knowledge Distillation of Large Language Models

This paper presents a novel framework that leverages negative samples during large language model distillation through three stages—Negative Assistive Training, Negative Calibration Enhancement, and Adaptive Self‑Consistency—demonstrating significant accuracy gains on challenging mathematical reasoning benchmarks and improved generalization to out‑of‑distribution tasks.

Chain-of-ThoughtKnowledge TransferLLM distillation
0 likes · 13 min read
Utilizing Negative Samples for Knowledge Distillation of Large Language Models
Model Perspective
Model Perspective
Feb 1, 2024 · Artificial Intelligence

Discover Top Change & Prediction Model Articles for AI and Data Science

This article compiles a categorized list of recent model papers, covering change models and various prediction models—including time series, machine learning, gray prediction, and deep learning—providing direct references for students and researchers interested in AI and data‑driven modeling.

Artificial IntelligencePredictionTime Series
0 likes · 6 min read
Discover Top Change & Prediction Model Articles for AI and Data Science
Model Perspective
Model Perspective
Feb 1, 2024 · Fundamentals

Essential Guide to Statistical and Probabilistic Model Articles

This curated list gathers recent articles on statistical and probabilistic models, covering clustering analysis, various linear regression techniques, and causal analysis, providing convenient links for students and researchers to explore each topic in depth.

Causal Analysisclusteringlinear regression
0 likes · 3 min read
Essential Guide to Statistical and Probabilistic Model Articles
ByteDance Data Platform
ByteDance Data Platform
Jan 31, 2024 · Artificial Intelligence

How A/B Testing Powers Continuous Improvement in Recommendation Systems

This article explains the role of A/B experiments in recommendation systems, outlines their workflow, shares practical tips and parameter design strategies, and demonstrates how to use experiment parameters and feature flags for efficient testing, optimization, and full‑scale deployment.

A/B testingexperiment parametersfeature flag
0 likes · 15 min read
How A/B Testing Powers Continuous Improvement in Recommendation Systems
dbaplus Community
dbaplus Community
Jan 29, 2024 · Artificial Intelligence

How Meituan Uses AIOps to Revolutionize Incident Management

This article details Meituan's two‑year exploration of AIOps for incident management, covering the challenges of massive, real‑time operational data, the AI‑driven modules for risk prevention, fault detection, diagnosis, and similar‑incident recommendation, and future directions such as intelligent log detection and change recognition.

OperationsRoot Cause Analysisaiops
0 likes · 22 min read
How Meituan Uses AIOps to Revolutionize Incident Management
DataFunSummit
DataFunSummit
Jan 28, 2024 · Artificial Intelligence

Causal Inference and Bias Correction Methods in Ant Financial Risk Control

This article presents how Ant Group applies causal inference techniques—including confounding bias analysis, double‑difference methods, DiDTree, and shrinkage‑based causal trees—to correct biases in risk‑control scenarios, detailing the theoretical background, algorithmic designs, experimental validation, and practical deployment.

Ant FinancialBias Correctioncausal inference
0 likes · 21 min read
Causal Inference and Bias Correction Methods in Ant Financial Risk Control
Test Development Learning Exchange
Test Development Learning Exchange
Jan 26, 2024 · Artificial Intelligence

Data Mining Techniques for Marketing: Customer Segmentation, Purchase Prediction, Recommendation, and More with Python

This article introduces ten data‑mining applications for marketing—including customer segmentation, purchase forecasting, market‑basket analysis, churn prediction, sentiment analysis, response modeling, recommendation systems, brand reputation, competitive analysis, and public‑opinion monitoring—each illustrated with concise Python code examples.

Customer SegmentationPredictionPython
0 likes · 11 min read
Data Mining Techniques for Marketing: Customer Segmentation, Purchase Prediction, Recommendation, and More with Python
DataFunTalk
DataFunTalk
Jan 25, 2024 · Artificial Intelligence

World Models, Reinforcement Learning, and Causal Inference: A Comprehensive Overview

This article presents a detailed overview of world models and their role in reinforcement learning, explains how causal inference can enhance model-based RL, discusses sample efficiency challenges, and shares experimental findings and practical insights from recent research and industry applications.

AIcausal inferencemachine learning
0 likes · 22 min read
World Models, Reinforcement Learning, and Causal Inference: A Comprehensive Overview
Test Development Learning Exchange
Test Development Learning Exchange
Jan 20, 2024 · Big Data

Practical Data Analysis Code Samples for Business Decision Making

This article presents ten practical Python code examples that demonstrate common data analysis techniques—such as handling missing values, sorting, pivot tables, visualization, association rules, outlier detection, time‑series forecasting, clustering, feature selection, and cross‑validation—to help improve business decision effectiveness.

Big DataBusiness IntelligencePython
0 likes · 4 min read
Practical Data Analysis Code Samples for Business Decision Making
Test Development Learning Exchange
Test Development Learning Exchange
Jan 18, 2024 · Fundamentals

Common Statistical Methods for Data Analysis with Python Code Examples

This article introduces ten common statistical techniques used in data analysis—including descriptive statistics, correlation, t‑test, ANOVA, linear regression, PCA, outlier detection, frequency distribution, time‑series analysis, and non‑parametric tests—providing concise explanations and Python code snippets for each method.

machine learningstatistical methodsstatistics
0 likes · 7 min read
Common Statistical Methods for Data Analysis with Python Code Examples
DataFunTalk
DataFunTalk
Jan 11, 2024 · Artificial Intelligence

Graph Models in Baidu Recommendation System: Background, Algorithms, and Evolution

This article introduces the use of graph models in Baidu's recommendation system, covering graph fundamentals, common graph algorithms such as graph embedding and graph neural networks, the evolution of the Feed graph model, and its subsequent promotion across multiple product lines.

Baidugraph embeddinggraph models
0 likes · 10 min read
Graph Models in Baidu Recommendation System: Background, Algorithms, and Evolution
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jan 10, 2024 · Artificial Intelligence

Understanding Backpropagation: From Simple to Advanced Neural Network Implementations in Python

This article explains the back‑propagation algorithm in neural networks, starting with a simple single‑neuron example using ReLU, Sigmoid and MSE, then extending to multi‑layer matrix‑based networks, providing detailed Python code, gradient calculations, and comparisons with TensorFlow implementations.

BackpropagationPythongradient descent
0 likes · 21 min read
Understanding Backpropagation: From Simple to Advanced Neural Network Implementations in Python
NetEase LeiHuo UX Big Data Technology
NetEase LeiHuo UX Big Data Technology
Jan 9, 2024 · Artificial Intelligence

Accelerating Recommendation System Development with MindsDB

The article explains how the data team adopted the open‑source machine‑learning platform MindsDB to simplify data integration, enable SQL‑based model training and inference, manage model versions, and dramatically shorten recommendation system development cycles, achieving up to 30% efficiency gains.

Data IntegrationMindsDBModel Management
0 likes · 5 min read
Accelerating Recommendation System Development with MindsDB
Python Programming Learning Circle
Python Programming Learning Circle
Jan 9, 2024 · Artificial Intelligence

Overview of Common Python Libraries for Artificial Intelligence with Code Examples

This article provides a comprehensive introduction to popular Python libraries used in artificial intelligence, such as NumPy, OpenCV, scikit-image, Pillow, SimpleCV, Mahotas, Ilastik, Scikit-learn, SciPy, NLTK, spaCy, LibROSA, Pandas, Matplotlib, Seaborn, Orange, PyBrain, Theano, Keras, Caffe, MXNet, PaddlePaddle, CNTK, and more, including code snippets and usage examples.

AIData SciencePython
0 likes · 34 min read
Overview of Common Python Libraries for Artificial Intelligence with Code Examples
High Availability Architecture
High Availability Architecture
Jan 9, 2024 · Operations

AIOps Practices for Incident Management at Meituan: From Risk Prevention to Post‑Operation

This article presents Meituan's two‑year exploration of AIOps in incident management, detailing risk‑prevention change detection, real‑time anomaly discovery, automated root‑cause diagnosis, multi‑dimensional KPI analysis, and similar‑event recommendation, while sharing architectural designs, algorithmic techniques, performance results, and future directions.

NLPOperationsRoot Cause Analysis
0 likes · 24 min read
AIOps Practices for Incident Management at Meituan: From Risk Prevention to Post‑Operation
DataFunTalk
DataFunTalk
Jan 7, 2024 · Artificial Intelligence

Baidu's Recommendation Ranking: Background, Feature Design, Algorithms, Architecture, and Future Directions

This article presents Baidu's comprehensive approach to feed recommendation ranking, covering business and data background, feature engineering principles, core algorithmic strategies, system architecture design, and upcoming plans to integrate large language models for more intelligent and fair recommendations.

Baidufeature engineeringlarge-scale AI
0 likes · 19 min read
Baidu's Recommendation Ranking: Background, Feature Design, Algorithms, Architecture, and Future Directions
DataFunTalk
DataFunTalk
Jan 6, 2024 · Artificial Intelligence

Causal Debiasing Techniques for Recommendation and Marketing Scenarios

This article presents Ant Group's causal debiasing techniques for recommendation and marketing, covering bias background, data‑fusion based MDI model, back‑door adjustment methods, experimental results on public and industry datasets, and practical applications in advertising and e‑commerce.

Marketingcausal inferencedata fusion
0 likes · 16 min read
Causal Debiasing Techniques for Recommendation and Marketing Scenarios
Sohu Tech Products
Sohu Tech Products
Jan 3, 2024 · Artificial Intelligence

OPPO Advertising Recall Algorithm: Architecture, Model Selection, Evaluation, and Optimization

OPPO revamped its advertising recall system by replacing a latency‑prone directional pipeline with an ANN‑based full‑ad personalized architecture, employing a dual‑tower LTR model, multi‑path auxiliary branches, refined offline metrics, price‑sensitive and hard‑negative sampling, and hybrid joint training, which together boosted ARPU by about 15%.

AdvertisingModel Optimizationlarge-scale classification
0 likes · 24 min read
OPPO Advertising Recall Algorithm: Architecture, Model Selection, Evaluation, and Optimization
DataFunTalk
DataFunTalk
Dec 30, 2023 · Artificial Intelligence

OPPO Advertising Recall Algorithm: Architecture, Model Selection, Evaluation, and Optimization Practices

This article presents OPPO's advertising recall system, detailing the transition from the legacy architecture to a new ANN‑based design, model selection criteria, offline evaluation metrics, sample optimization techniques, and various model improvements that together achieved significant ARPU gains.

AdvertisingOPPOmachine learning
0 likes · 24 min read
OPPO Advertising Recall Algorithm: Architecture, Model Selection, Evaluation, and Optimization Practices
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 22, 2023 · Artificial Intelligence

Machine Learning-Based Text‑Image Correlation Analysis

This article introduces a machine‑learning approach for correlating text and image data, covering preprocessing, feature extraction, model training, experimental results, and future directions, and provides complete Python code examples using NLP and deep‑learning libraries.

machine learningmultimodaltext-image correlation
0 likes · 17 min read
Machine Learning-Based Text‑Image Correlation Analysis
Python Programming Learning Circle
Python Programming Learning Circle
Dec 21, 2023 · Artificial Intelligence

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

Streamlit is a free, open‑source Python framework that lets machine‑learning engineers quickly turn scripts into interactive web apps, featuring top‑to‑bottom script execution, widget‑as‑variable handling, caching, GPU support, and seamless integration with tools like Git.

App DevelopmentGPUOpen-source
0 likes · 9 min read
Introducing Streamlit: A Free Open‑Source Framework for Building Machine‑Learning Apps with Python
AntTech
AntTech
Dec 14, 2023 · Artificial Intelligence

Highlights of Ant Group’s 20 Accepted Papers at NeurIPS 2023

The article summarizes Ant Group's twenty accepted NeurIPS 2023 papers, covering advances in generative AI, time‑series forecasting, 3D image synthesis, and other machine‑learning topics, and provides brief overviews of three highlighted works along with links to the remaining studies.

3D Image SynthesisAnt GroupNeurIPS
0 likes · 10 min read
Highlights of Ant Group’s 20 Accepted Papers at NeurIPS 2023
DataFunTalk
DataFunTalk
Dec 12, 2023 · Artificial Intelligence

Challenges and Considerations of Recommendation Systems: Evaluation, Data Leakage, and the Role of Large Models

This article examines recommendation system problem definitions, differences between academia and industry, offline evaluation pitfalls and data leakage issues, data construction challenges with datasets like MovieLens, and evaluates whether large language models can serve as effective solutions for modern recommendation tasks.

Large Language Modelsdata leakagemachine learning
0 likes · 20 min read
Challenges and Considerations of Recommendation Systems: Evaluation, Data Leakage, and the Role of Large Models
DataFunSummit
DataFunSummit
Dec 9, 2023 · Artificial Intelligence

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

This article reviews the growing interest in causal learning within machine learning, explaining what causal learning is, its advantages over purely correlational methods, and detailing two main paradigms—learning with known causal structures and learning via causal discovery—along with examples, challenges, and future directions.

Deep Learningcausal discoverycausal inference
0 likes · 12 min read
Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery
HomeTech
HomeTech
Dec 8, 2023 · Mobile Development

Automotive Home Push Platform Architecture and Future Development

This article introduces the architecture and core functions of Automotive Home Push Platform, covering its development history, technical implementation, monitoring system, and future plans for intelligent message distribution.

ArchitectureCloud NativeMicroservices
0 likes · 9 min read
Automotive Home Push Platform Architecture and Future Development
Test Development Learning Exchange
Test Development Learning Exchange
Dec 4, 2023 · Fundamentals

Common Data Cleaning Techniques with Python Code Examples

This article presents a comprehensive collection of Python code snippets demonstrating essential data cleaning methods—including handling missing values, outlier detection, type conversion, formatting, duplicate removal, normalization, one‑hot encoding, text preprocessing, and dataset merging—providing practical guidance for preparing data for analysis or machine‑learning tasks.

data cleaningdata preprocessingmachine learning
0 likes · 7 min read
Common Data Cleaning Techniques with Python Code Examples
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 3, 2023 · Artificial Intelligence

Probability Basics, Discriminative vs Generative Models, and Autoencoders (including Variational Autoencoders)

This article introduces fundamental probability notation, explains the difference between discriminative and generative models, and provides a comprehensive overview of autoencoders and variational autoencoders, covering their architectures, loss functions, latent spaces, and practical applications in image manipulation.

Discriminative ModelsGenerative ModelsLatent Space
0 likes · 17 min read
Probability Basics, Discriminative vs Generative Models, and Autoencoders (including Variational Autoencoders)
DataFunTalk
DataFunTalk
Dec 2, 2023 · Artificial Intelligence

OPPO's Unified Modeling for App Distribution: Balancing Cost Reduction and User Value

The article examines how OPPO tackles the challenges of sparse, multi‑scenario app‑distribution data by deploying a unified modeling framework, leveraging MMoe and oCPX techniques to enhance recommendation performance, reduce costs, and preserve user value across its software store and game center.

OPPOdata sparsitymachine learning
0 likes · 11 min read
OPPO's Unified Modeling for App Distribution: Balancing Cost Reduction and User Value
DaTaobao Tech
DaTaobao Tech
Dec 1, 2023 · Artificial Intelligence

Design, Evaluation, and Production of a VOC Tagging System for Taobao User Experience

Taobao’s Technical Industry Data team designed a four‑level VOC tagging hierarchy to unify fragmented user‑feedback sources, evaluated label similarity with vector‑based distance matrices, optimized tag groups via entropy‑driven re‑grouping, built a stacking ensemble of FastText and TextCNN achieving over 90% accuracy, and deployed an automated production pipeline that generates tags, maintains ODPS tables, and provides APIs for rapid experimentation.

Data ScienceNLPTagging
0 likes · 18 min read
Design, Evaluation, and Production of a VOC Tagging System for Taobao User Experience
Python Programming Learning Circle
Python Programming Learning Circle
Nov 30, 2023 · Artificial Intelligence

Common Python Libraries for Computer Vision Projects

This article introduces ten popular Python libraries for computer vision, describing their main features, typical applications, and providing concise code examples to help beginners and practitioners quickly choose and use the right tools for image processing and deep learning tasks.

Computer VisionImage ProcessingPython
0 likes · 10 min read
Common Python Libraries for Computer Vision Projects
Alimama Tech
Alimama Tech
Nov 28, 2023 · Artificial Intelligence

Evolution of Alibaba's AI-Driven Advertising Decision Technologies

The article traces Alibaba’s Alimama platform from classic control‑based bidding through linear programming and reinforcement‑learning approaches to generative‑AI‑driven strategies, detailing how deep‑learning models, offline and sustainable online RL frameworks, and large‑language‑model‑based bidding reshape automated auctions, fairness, and scalability in e‑commerce advertising.

AIAuction Designauto-bidding
0 likes · 38 min read
Evolution of Alibaba's AI-Driven Advertising Decision Technologies
DataFunSummit
DataFunSummit
Nov 27, 2023 · Artificial Intelligence

Online Learning with Alink Model Flow: From Fundamentals to Model Flow 1.0 and 2.0

This article introduces Alibaba's Alink platform and its online learning capabilities, discusses common challenges in machine‑learning pipelines, explains Alink’s algorithm‑to‑application connection, various computation modes, usage methods, and details the evolution from Model Flow 1.0 to the more versatile Model Flow 2.0, including pipeline integration, incremental training, and embedding prediction services.

AlinkFlinkOnline Learning
0 likes · 9 min read
Online Learning with Alink Model Flow: From Fundamentals to Model Flow 1.0 and 2.0
Kuaishou Tech
Kuaishou Tech
Nov 17, 2023 · Artificial Intelligence

Short Video Recommendation Algorithms Forum

A forum discussing frontiers in short video recommendation algorithms, featuring academic research from Kuaishou and collaborations with universities, including topics like reinforcement learning and graph neural networks for personalized recommendations.

AlgorithmsArtificial Intelligencemachine learning
0 likes · 4 min read
Short Video Recommendation Algorithms Forum
Alimama Tech
Alimama Tech
Nov 15, 2023 · Artificial Intelligence

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)

The HC² framework enhances multi‑scenario ad ranking by jointly applying a generalized contrastive loss on shared representations and an individual contrastive loss on scenario‑specific layers, using label‑aware positive sampling, diffusion‑noise negative sampling, and inverse‑similarity weighting, achieving consistent offline gains and up to 2.5% CVR and 3.7% GMV improvements in Alibaba’s live system.

ad rankingcontrastive learningmachine learning
0 likes · 16 min read
Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)
Efficient Ops
Efficient Ops
Nov 8, 2023 · Operations

How Intelligent Operations (AIOps) Transforms IT Management and Self‑Healing

This article explains what intelligent operations (AIOps) are, outlines a four‑layer platform architecture, and showcases real‑world practices such as load‑balancing link repair, MySQL container self‑healing, composite service tracing, component‑based orchestration, and AI‑driven log analysis, concluding with future prospects.

IT OperationsIntelligent Operationsaiops
0 likes · 7 min read
How Intelligent Operations (AIOps) Transforms IT Management and Self‑Healing
AntTech
AntTech
Nov 8, 2023 · Artificial Intelligence

Kapacity V0.2 Release: AI‑Driven Traffic‑Based Replica Prediction for Cloud‑Native Autoscaling

Kapacity V0.2 introduces an AI‑powered, traffic‑driven replica prediction algorithm for cloud‑native autoscaling, featuring a Linear‑Residual model, a lightweight Swish Net time‑series forecaster, custom metric support, and open‑source tools, aiming to improve resource efficiency and reduce operational risk.

AIKubernetesOpen-source
0 likes · 9 min read
Kapacity V0.2 Release: AI‑Driven Traffic‑Based Replica Prediction for Cloud‑Native Autoscaling
DataFunSummit
DataFunSummit
Nov 7, 2023 · Artificial Intelligence

Instrumental Variable Based Causal Inference and Generalizable Causal Learning

This article presents a comprehensive overview of using instrumental variables for causal inference and causal generalization in machine learning, discussing deep learning limitations, Pearl's causal hierarchy, two‑stage regression, challenges with unobserved confounders, automatic IV generation, and applications in economics and social networks.

Generalizationcausal inferencecausal learning
0 likes · 16 min read
Instrumental Variable Based Causal Inference and Generalizable Causal Learning
Huolala Tech
Huolala Tech
Nov 1, 2023 · Operations

How Dynamic Pricing and Smart Surcharges Boost Freight Platform Efficiency During Peak Seasons

This article examines the challenges of freight‑peak periods, reviews industry surge‑pricing tactics, and presents a comprehensive dynamic‑pricing framework—including data collection, supply‑demand analysis, price adjustment, real‑time monitoring, and optimization models—to improve service quality, reduce disputes, and maximize platform revenue.

Logisticsdynamic pricingmachine learning
0 likes · 28 min read
How Dynamic Pricing and Smart Surcharges Boost Freight Platform Efficiency During Peak Seasons
DataFunSummit
DataFunSummit
Oct 26, 2023 · Big Data

Data‑Driven Metric System Construction and Application: Theory, Methods, and Real‑World Cases

This article explains how to build and apply a data‑driven metric system, covering end‑to‑end design principles, business‑ versus data‑driven approaches, frameworks such as OSM, GSM and HEART, statistical and machine‑learning techniques, causal inference, and practical case studies that illustrate alerting, diagnosis, and strategy deployment in product operations.

Data-drivencausal inferencemachine learning
0 likes · 21 min read
Data‑Driven Metric System Construction and Application: Theory, Methods, and Real‑World Cases
Python Programming Learning Circle
Python Programming Learning Circle
Oct 26, 2023 · Artificial Intelligence

Animal Recognition Techniques Using Deep Learning and Image Processing

This article reviews animal recognition technology, covering its background, basic principles, image‑processing, feature extraction, machine‑learning and deep‑learning methods, dataset construction, preprocessing, and feature‑selection techniques, and provides Python code examples for implementing CNNs and traditional classifiers.

Computer VisionDeep LearningImage Processing
0 likes · 18 min read
Animal Recognition Techniques Using Deep Learning and Image Processing
Zhuanzhuan Tech
Zhuanzhuan Tech
Oct 25, 2023 · Artificial Intelligence

Bayesian Statistics and Causal Inference for SKU‑Level Pricing in E‑commerce

The article presents a comprehensive pricing solution for an e‑commerce platform that combines Bayesian statistical modeling, MCMC sampling, and causal inference (including Dragonnet) to achieve controllable, fine‑grained SKU‑level price estimation and optimization.

Bayesian statisticscausal inferencemachine learning
0 likes · 15 min read
Bayesian Statistics and Causal Inference for SKU‑Level Pricing in E‑commerce
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 23, 2023 · Artificial Intelligence

How the New OLSS Algorithm Supercharges Diffusion Model Sampling

The article announces that Alibaba Cloud’s AI platform PAI and ECNU researchers’ paper on the Optimal Linear Subspace Search (OLSS) algorithm was selected for CIKM 2023, explains how OLSS accelerates diffusion‑model sampling by operating in higher‑dimensional linear subspaces, and provides details of the paper and its visual results.

Diffusion ModelsOLSSgenerative AI
0 likes · 5 min read
How the New OLSS Algorithm Supercharges Diffusion Model Sampling
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Oct 21, 2023 · Artificial Intelligence

Large Models and Recommendation Systems: Challenges, Opportunities, and Industry Insights (CNCC 2023 Technical Forum)

The CNCC 2023 Technical Forum highlighted how large models can boost recommendation systems with stronger generalization and knowledge understanding, while also raising challenges like high computational costs, interpretability, and ethics, featuring talks from experts at Xiaohongshu, USTC, Tsinghua, Renmin University, and Huawei.

CNCC 2023Industry Talkmachine learning
0 likes · 8 min read
Large Models and Recommendation Systems: Challenges, Opportunities, and Industry Insights (CNCC 2023 Technical Forum)