BestHub
Sign in
Tagged articles

machine learning

1966 articles · Page 6 of 20
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 ScienceLibrariesPython
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.

Causal InferenceDemand Modelingdynamic 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 Qualitymachine learningmonitoring
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.

AMFormerTransformerdeep learning
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.

AIOpsIndex OptimizationMySQL
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.

AdvertisingOffline Evaluationdual-tower model
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 TestingKnowledge GraphTagging System
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.

ClassificationKNNLogistic Regression
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.

AILaravelPHP-ML
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.

Financial Technologycredit riskdata fusion
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.

AlgorithmsNLPSystem Design
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.

Causal InferenceDomain Adaptationcausal discovery
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.

BilibiliNatural Language ProcessingText classification
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 CorrectionCausal Inference
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.

Causal InferenceKuaishouduration 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.

LLM distillationModel Specializationchain-of-thought
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 IntelligenceTime Seriesmachine learning
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.

AIOpsOperationsRoot Cause Analysis
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 SegmentationPythondata mining
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.

Business IntelligencePythonbig data
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 Neural Networksgraph embedding
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 IntegrationMindsDBSQL
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 ScienceLibraries
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.

AIOpsNLPOperations
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.

Causal Inferencedata fusiondebiasing
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%.

AdvertisingOffline Evaluationlarge-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.

Multimodalmachine learningtext-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 DevelopmentPythonStreamlit
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.

Offline Evaluationdata leakagelarge language models
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.

Causal InferenceDomain Adaptationcausal discovery
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 ModelsVariational AutoEncoder
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 ScienceNLPVOC
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.

LibrariesPythoncomputer vision
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.

AlinkFlinkincremental training
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.

Contrastive Learningad rankingmachine 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.

AIOpsIT OperationsSelf-Healing
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.

AIKubernetesPredictive Autoscaling
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.

Causal Inferencecausal learninggeneralization
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.

dynamic pricinglogisticsmachine 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.

Causal Inferencedata-drivenmachine 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.

animal recognitioncomputer visiondeep learning
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)
ZhongAn Tech Team
ZhongAn Tech Team
Oct 20, 2023 · Artificial Intelligence

Document Analytics & Anti‑Fraud Support Platform for Hong Kong Virtual Banking

This article describes the design and implementation of a Document Analytics & Anti‑Fraud Support platform for Hong Kong virtual banking, detailing its OCR/NLP‑driven pipeline, dynamic rule engine, multi‑template PDF processing, model training, and the resulting improvements in fraud detection and operational efficiency.

NLPOCRanti-fraud
0 likes · 18 min read
Document Analytics & Anti‑Fraud Support Platform for Hong Kong Virtual Banking
Test Development Learning Exchange
Test Development Learning Exchange
Oct 19, 2023 · Artificial Intelligence

Common Machine Learning Algorithms for Data Prediction with Python Code Examples

This article introduces ten widely used machine learning algorithms for data prediction, explains their core concepts, and provides complete Python code snippets using scikit‑learn and related libraries to help readers implement regression, classification, and time‑series forecasting tasks.

ClassificationPythondata prediction
0 likes · 12 min read
Common Machine Learning Algorithms for Data Prediction with Python Code Examples
Zhuanzhuan Tech
Zhuanzhuan Tech
Oct 18, 2023 · Artificial Intelligence

Design and Implementation of a Home‑Page Recommendation System Using Reinforcement Learning and DPP

This article presents a comprehensive design for Zhuanzhuan's home‑page recommendation pipeline, detailing the system architecture, challenges of traffic efficiency and diversity, and a two‑stage solution that applies Proximal Policy Optimization reinforcement learning in the re‑ranking module and Determinantal Point Process optimization in the coarse‑ranking and traffic‑pool stages, followed by offline simulation, online deployment, and evaluation metrics.

DPPmachine learningranking
0 likes · 18 min read
Design and Implementation of a Home‑Page Recommendation System Using Reinforcement Learning and DPP
DataFunSummit
DataFunSummit
Oct 17, 2023 · Artificial Intelligence

DataFunSummit2023: Deep Learning‑Driven Multi‑Experiment Causal Inference and Distributed Causal Tools

The DataFunSummit2023 online conference brings together experts from Tencent and Kuaishou to present cutting‑edge research on causal inference for large‑scale A/B testing, including deep‑learning‑based multi‑experiment effect estimation, a distributed causal inference framework (Fast‑Causal‑Inference), and strategies for evaluating long‑term policy impacts.

A/B testingCausal InferenceData Science
0 likes · 7 min read
DataFunSummit2023: Deep Learning‑Driven Multi‑Experiment Causal Inference and Distributed Causal Tools
DaTaobao Tech
DaTaobao Tech
Oct 13, 2023 · Artificial Intelligence

Understanding Stable Diffusion: Core Principles and Technical Architecture

The article demystifies Stable Diffusion by explaining its low‑cost latent‑space design and conditioning mechanisms, comparing it to autoregressive, VAE, flow‑based and GAN models, detailing the iterative noise‑to‑image process, token‑based text‑to‑image control, version differences, common generation issues, and providing implementation code examples.

AI image generationStable DiffusionVAE
0 likes · 15 min read
Understanding Stable Diffusion: Core Principles and Technical Architecture
php Courses
php Courses
Oct 13, 2023 · Artificial Intelligence

Top 10 Python Libraries for Data Augmentation in Machine Learning

This article introduces ten popular Python libraries—Augmentor, imgaug, albumentations, nlpaug, textaugment, pytorch‑geometric, audiomentations, nlpaugment, keras‑augment, and OpenCV—that provide powerful image, text, audio, and graph data augmentation techniques to improve model generalization and robustness.

Data AugmentationPythonaudio augmentation
0 likes · 8 min read
Top 10 Python Libraries for Data Augmentation in Machine Learning
DataFunTalk
DataFunTalk
Oct 11, 2023 · Artificial Intelligence

Kuaishou Content Cold-Start Recommendation: Challenges, Modeling Solutions, and Future Directions

This article presents Kuaishou's approach to solving the content cold-start problem by analyzing its impact on video growth, detailing the challenges of sparse and biased training data, and describing a suite of graph‑neural‑network, I2U/U2I, TDM, and debiasing techniques that improve early video exposure and long‑term ecosystem health.

Graph Neural NetworkI2UKuaishou
0 likes · 18 min read
Kuaishou Content Cold-Start Recommendation: Challenges, Modeling Solutions, and Future Directions
DataFunSummit
DataFunSummit
Oct 9, 2023 · 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 algorithms applied to recommendation systems, covering background challenges, algorithm taxonomy, recent research, detailed model architectures such as TAML, CausalInt and DFFM, experimental results on public and private datasets, and a Q&A discussion.

Advertisingmachine learningmulti-task learning
0 likes · 20 min read
Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications
21CTO
21CTO
Oct 8, 2023 · Artificial Intelligence

Why Hugging Face’s New Rust‑Based Candle Framework Could Redefine AI Inference

Hugging Face has released Candle, a Rust‑written machine‑learning framework aimed at serverless inference, offering lightweight binaries, GPU support, and performance gains over Python‑based PyTorch, while sparking debate over Rust’s learning curve and the future of AI deployment.

AI FrameworkCandlePyTorch
0 likes · 7 min read
Why Hugging Face’s New Rust‑Based Candle Framework Could Redefine AI Inference
DataFunSummit
DataFunSummit
Oct 5, 2023 · Artificial Intelligence

Fairness in Recommendation Systems: Consumer and Provider Perspectives

This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, and proposes causal embedding models to mitigate unfairness while ensuring sustainable system performance.

Causal Inferenceconsumer perspectivefairness
0 likes · 9 min read
Fairness in Recommendation Systems: Consumer and Provider Perspectives
DataFunSummit
DataFunSummit
Oct 3, 2023 · Artificial Intelligence

Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, Algorithm Practice, and Future Outlook

This article presents a comprehensive case study of NIO's Power swap‑station ecosystem, detailing the business context, key forecasting challenges, the evolution from classical statistical models to deep‑learning architectures with specialized embeddings, and the practical outcomes and future plans for improving prediction accuracy.

Electric VehicleEmbeddingNIO Power
0 likes · 16 min read
Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, Algorithm Practice, and Future Outlook
DataFunTalk
DataFunTalk
Oct 1, 2023 · Artificial Intelligence

Research and Product Applications of Causal Inference for Solving Recommendation System Bias

In this talk, senior researcher Dai Quanyu from Huawei Noah's Ark Lab presents his work on applying causal inference to identify and correct various biases in recommendation systems, detailing underlying theoretical frameworks, bias‑mitigation algorithms such as inverse propensity weighting and robust learning, and real‑world product deployments.

AIBias MitigationCausal Inference
0 likes · 3 min read
Research and Product Applications of Causal Inference for Solving Recommendation System Bias
MaGe Linux Operations
MaGe Linux Operations
Sep 25, 2023 · Artificial Intelligence

How ChatGPT Works: Inside the Neural Network That Generates Human‑Like Text

Stephen Wolfram explains the inner workings of ChatGPT, covering its transformer architecture, probability‑based word selection, training on massive text corpora, the role of embeddings, neural network layers, attention mechanisms, and the challenges of modeling language, offering a deep technical overview for AI enthusiasts.

AIChatGPTTransformer
0 likes · 80 min read
How ChatGPT Works: Inside the Neural Network That Generates Human‑Like Text
Model Perspective
Model Perspective
Sep 21, 2023 · Fundamentals

Unlock the Jargon: Essential Terms Every Math Modeling Beginner Must Know

This comprehensive guide demystifies over one hundred core mathematical modeling terms—from basic concepts like models and abstraction to advanced topics such as optimization, dynamic systems, stochastic processes, statistical methods, and machine learning—helping newcomers confidently navigate the field.

Optimizationmachine learningmathematical modeling
0 likes · 20 min read
Unlock the Jargon: Essential Terms Every Math Modeling Beginner Must Know
AntTech
AntTech
Sep 21, 2023 · Artificial Intelligence

AFAC2023 Financial Intelligence Challenge Highlights and the Release of the Fin‑Eval Dataset

The inaugural AFAC2023 Financial Intelligence Challenge, co‑organized by the China Computer Federation and Ant Group, attracted over 4,700 teams, showcased cutting‑edge AI solutions for finance such as market opinion generation, compliance detection, and pet‑age recognition, and culminated in the public launch of the Fin‑Eval benchmark dataset for financial large‑model evaluation.

AICompetitionFin-Eval
0 likes · 12 min read
AFAC2023 Financial Intelligence Challenge Highlights and the Release of the Fin‑Eval Dataset
HomeTech
HomeTech
Sep 21, 2023 · Artificial Intelligence

Homepage Pop‑up Recommendation System for Car Purchase Intent: Background, Feature Engineering, Model and Strategy Optimization, and Results

This article details how AutoHome's homepage pop‑up leverages precise targeting, extensive feature engineering, and multi‑stage DeepFM‑based models with attention and LHUC modules to accurately identify car‑buying users, improve vehicle‑series recommendations, and achieve a 355% conversion rate increase.

AIcar buyingdeep learning
0 likes · 7 min read
Homepage Pop‑up Recommendation System for Car Purchase Intent: Background, Feature Engineering, Model and Strategy Optimization, and Results
DataFunTalk
DataFunTalk
Sep 21, 2023 · Artificial Intelligence

Active Learning and Sample Imbalance in Graph Data for Risk Control

This presentation explores the challenges of label scarcity and class imbalance in graph‑based risk‑control scenarios, proposing semantic‑aware active learning and prototype‑driven sampling strategies to improve node classification performance on imbalanced graph datasets.

Graph Neural Networksactive learninggraph data
0 likes · 16 min read
Active Learning and Sample Imbalance in Graph Data for Risk Control
php Courses
php Courses
Sep 21, 2023 · Artificial Intelligence

Five Free AI Coding Tools to Boost Developer Productivity

This article introduces five free artificial‑intelligence coding assistants, explains how they accelerate and secure software development, and addresses common concerns about AI’s impact on programmers and job security.

AIcoding assistantsdeveloper productivity
0 likes · 6 min read
Five Free AI Coding Tools to Boost Developer Productivity
Ant R&D Efficiency
Ant R&D Efficiency
Sep 19, 2023 · Artificial Intelligence

From the Turing Test to GPT‑4: A Historical Overview of Chatbots and Deep Learning

From Turing’s 1950 imitation game to GPT‑4’s multimodal vision‑language capabilities, the field has evolved from simple rule‑based programs like ELIZA and PARRY, through statistical learning and the 2017 Transformer breakthrough, to large-scale generative models that achieve fluent conversation yet still grapple with hallucination and true understanding.

Artificial IntelligenceChatbot HistoryGPT-4
0 likes · 25 min read
From the Turing Test to GPT‑4: A Historical Overview of Chatbots and Deep Learning
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Sep 15, 2023 · Artificial Intelligence

Understanding Machine Learning vs Deep Learning and a Practical sklearn Regression Tutorial

This article explains the difference between machine learning and deep learning, compares ML algorithms with traditional logic code, introduces the scikit‑learn library, demonstrates data preprocessing, model training with RandomForestRegressor, and shows how to build a voting regressor for disease progression prediction using Python.

Pythonmachine learningregression
0 likes · 18 min read
Understanding Machine Learning vs Deep Learning and a Practical sklearn Regression Tutorial