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1966 articles · Page 16 of 20
58 Tech
58 Tech
May 31, 2019 · Artificial Intelligence

Summary of 58 Group Technical Salon: Recommendation System Architecture and Search Ranking Algorithm Practices

The article summarizes the 58 Group technical salon where experts presented the microservice‑based recommendation system architecture, data and strategy layers, and the internally built search ranking platform covering sampling, feature engineering, and model training, highlighting practical implementations and lessons learned.

AIMicroservicesdata pipeline
0 likes · 7 min read
Summary of 58 Group Technical Salon: Recommendation System Architecture and Search Ranking Algorithm Practices
Sohu Tech Products
Sohu Tech Products
May 29, 2019 · Artificial Intelligence

Third Sohu Content Recognition Algorithm Competition Concludes with Highlights and Insights

The third Sohu Content Recognition Algorithm Competition wrapped up after 65 days, drawing 218 universities and 982 teams, showcasing over 5,000 submissions, awarding prizes to top university teams, and sharing key lessons on data focus, model fusion, and engineering practice for AI development.

AI competitionNatural Language ProcessingSohu
0 likes · 5 min read
Third Sohu Content Recognition Algorithm Competition Concludes with Highlights and Insights
DataFunTalk
DataFunTalk
May 29, 2019 · Artificial Intelligence

A Comprehensive Overview of Statistical Learning Methods for Machine Learning Interview Preparation

This article provides a detailed, English-language summary of key statistical learning concepts—including perceptron, k‑nearest neighbors, Naive Bayes, decision trees, logistic regression, support vector machines, boosting, EM, HMM, neural networks, K‑Means, bagging, Apriori and dimensionality reduction—complete with formulas, algorithm steps, and illustrative diagrams to aid interview preparation.

ClassificationSupport Vector Machinemachine learning
0 likes · 44 min read
A Comprehensive Overview of Statistical Learning Methods for Machine Learning Interview Preparation
DataFunTalk
DataFunTalk
May 28, 2019 · Artificial Intelligence

Alibaba XiaoMi: Intelligent Service Architecture and Emotion Response Capabilities

This article presents an in‑depth overview of Alibaba's XiaoMi chatbot, covering its evolution from traditional customer service to an intelligent service platform, the design of emotion recognition and response models, customer emotion soothing pipelines, service quality detection, generative emotional dialogue, and future work on session satisfaction estimation.

AIEmotion Detectionchatbot
0 likes · 12 min read
Alibaba XiaoMi: Intelligent Service Architecture and Emotion Response Capabilities
Beike Product & Technology
Beike Product & Technology
May 23, 2019 · Artificial Intelligence

Practical Applications and Challenges of Machine Learning and AI at QCon Beijing 2019

At QCon Beijing 2019, four Beike technology experts presented the practical use and challenges of machine learning for user profiling, deep‑learning‑based house‑quality scoring, intelligent customer‑service systems, and AI‑driven floor‑plan generation, summarizing the architecture, data pipelines, model evolution, and future improvement directions.

AIGaNNLP
0 likes · 16 min read
Practical Applications and Challenges of Machine Learning and AI at QCon Beijing 2019
Alibaba Cloud Developer
Alibaba Cloud Developer
May 22, 2019 · Artificial Intelligence

Mastering Anomaly Detection: From Moving Averages to Isolation Forests

This comprehensive guide explores a wide range of anomaly detection techniques—including time‑series methods, statistical models, distance‑based approaches, tree‑based isolation forests, graph algorithms, behavior‑sequence Markov models, and supervised machine‑learning models—detailing their principles, formulas, and practical scenarios for detecting outliers in advertising, fraud, and system monitoring.

Graph AlgorithmsIsolation ForestTime Series
0 likes · 19 min read
Mastering Anomaly Detection: From Moving Averages to Isolation Forests
DataFunTalk
DataFunTalk
May 21, 2019 · Artificial Intelligence

Deep Learning Foundations: Mathematics, Modern Network Practices, and Research Overview

This article provides a comprehensive overview of deep learning, covering essential mathematics and machine learning fundamentals, modern deep network architectures and regularization techniques, advanced research topics such as structured probabilistic models and generative methods, and a curated reading list for practitioners.

AI Fundamentalsmachine learningmathematics
0 likes · 4 min read
Deep Learning Foundations: Mathematics, Modern Network Practices, and Research Overview
DataFunTalk
DataFunTalk
May 15, 2019 · Artificial Intelligence

AI‑Driven Audio Content Understanding and Safety for Live Streams

Using AI to automatically understand and secure audio content, this article discusses the challenges of manual audio analysis, outlines a four‑step pipeline—audio segmentation, speech‑to‑text, labeling, and synthesis—and describes models such as VAD, ASR, sound classification, text recognition, and behavior detection for live‑stream moderation.

AIaudio processingcontent safety
0 likes · 11 min read
AI‑Driven Audio Content Understanding and Safety for Live Streams
DataFunTalk
DataFunTalk
May 13, 2019 · Artificial Intelligence

Financial Risk Management: Business Requirements and Technical Solutions

This article presents a comprehensive overview of financial risk management, detailing business challenges such as identity verification and fraud, and describing technical solutions including feature engineering, sample handling, model optimization, and online validation, emphasizing the integration of data-driven AI techniques throughout the process.

Risk Managementbig datafinancial modeling
0 likes · 13 min read
Financial Risk Management: Business Requirements and Technical Solutions
AntTech
AntTech
May 7, 2019 · Artificial Intelligence

SQLFlow: Bridging SQL Engines and AI Platforms for End‑to‑End Machine Learning

SQLFlow is an open‑source project that connects diverse SQL engines (MySQL, Hive, SparkSQL, etc.) with AI frameworks (TensorFlow, PyTorch, XGBoost, etc.) through extended SQL syntax, enabling analysts to train and predict models using only a few SQL statements while aiming for high scalability and performance.

AI IntegrationGoSQL
0 likes · 13 min read
SQLFlow: Bridging SQL Engines and AI Platforms for End‑to‑End Machine Learning
Qunar Tech Salon
Qunar Tech Salon
Apr 29, 2019 · Artificial Intelligence

Multi‑Level Deep Model Fusion for Fake News Detection Using BERT – Winning Solution of WSDM Cup 2019

The article details the Travel team's award‑winning solution for the WSDM Cup 2019 fake‑news detection task, describing data analysis, preprocessing, label‑propagation augmentation, a BERT‑based baseline, a three‑stage multi‑level model‑fusion framework, experimental results, and future directions.

BERTData AugmentationNLP
0 likes · 12 min read
Multi‑Level Deep Model Fusion for Fake News Detection Using BERT – Winning Solution of WSDM Cup 2019
DataFunTalk
DataFunTalk
Apr 25, 2019 · Artificial Intelligence

Comparison of Classification and Ranking Models in Recommendation Systems

This article examines the differences and similarities between classification (pointwise) and ranking (pairwise) models for recommendation systems, covering their probabilistic foundations, loss functions, parameter updates, and practical implications such as sensitivity to statistical features and robustness.

classification modelloss functionmachine learning
0 likes · 10 min read
Comparison of Classification and Ranking Models in Recommendation Systems
Tencent Advertising Technology
Tencent Advertising Technology
Apr 23, 2019 · Artificial Intelligence

Tencent Advertising Algorithm Competition: Experience and Tips from the Runner‑Up

This article shares the experience of Xu An, runner‑up in the 2019 Tencent Advertising Algorithm Competition, detailing practical advice on feature engineering, model selection, efficiency tricks, personal habits, contest rhythm, and learning resources for aspiring participants.

Algorithm ContestLightGBMTencent Advertising Competition
0 likes · 6 min read
Tencent Advertising Algorithm Competition: Experience and Tips from the Runner‑Up
DataFunTalk
DataFunTalk
Apr 22, 2019 · Artificial Intelligence

Understanding Personalized Recommendation: From Machine Learning Basics to E‑commerce Applications

This article explains how personalized recommendation systems work in e‑commerce platforms like Taobao, covering the fundamentals of machine learning, feature engineering, model building, evaluation, and the business motivations behind delivering unique, user‑specific product suggestions.

AIPersonalized RecommendationUser Modeling
0 likes · 22 min read
Understanding Personalized Recommendation: From Machine Learning Basics to E‑commerce Applications
MaGe Linux Operations
MaGe Linux Operations
Apr 20, 2019 · Artificial Intelligence

Master Python Speech Recognition: Install, Record, and Transcribe Audio

This comprehensive guide walks you through the fundamentals of speech recognition, explains how it works, compares Python packages, shows step‑by‑step installation of SpeechRecognition, demonstrates processing audio files and live microphone input, and offers techniques for handling noise and multilingual transcription.

PythonSpeechRecognitionaudio processing
0 likes · 16 min read
Master Python Speech Recognition: Install, Record, and Transcribe Audio
DataFunTalk
DataFunTalk
Apr 19, 2019 · Artificial Intelligence

E-commerce Search and User Guidance: Concepts, Techniques, and Product Design

This article examines the role of search as a user guidance channel in e-commerce, outlining product requirements, user flow stages, and various algorithmic solutions—including query understanding, suggestion, rewriting, retrieval, and ranking—while also comparing implementations across major Chinese platforms.

e-commercemachine learningquery understanding
0 likes · 29 min read
E-commerce Search and User Guidance: Concepts, Techniques, and Product Design
DataFunTalk
DataFunTalk
Apr 17, 2019 · Artificial Intelligence

Evolution of Ctrip Financial Risk Control Models: From Data Platform to AI‑Driven Scoring and Anti‑Fraud Systems

This report details Ctrip Financial's end‑to‑end risk control development, covering business overview, a three‑layer data platform, the progression of credit scoring and anti‑fraud models from rule‑based to advanced AI techniques, and the evaluation, monitoring, and social‑network‑based fraud detection strategies employed.

Risk Modelinganti-fraudbig data
0 likes · 16 min read
Evolution of Ctrip Financial Risk Control Models: From Data Platform to AI‑Driven Scoring and Anti‑Fraud Systems
Qunar Tech Salon
Qunar Tech Salon
Apr 17, 2019 · Artificial Intelligence

Understanding AdaBoost: Theory, Scikit‑learn Library, and Practical Implementation in Python

This article introduces the AdaBoost algorithm, explains its boosting principle, describes the AdaBoostClassifier and AdaBoostRegressor classes in scikit‑learn, provides a complete Python example with data loading, model training, prediction, evaluation, and visualisation, and discusses the algorithm’s advantages, disadvantages, and detailed iterative process.

AdaBoostPythonboosting
0 likes · 12 min read
Understanding AdaBoost: Theory, Scikit‑learn Library, and Practical Implementation in Python
21CTO
21CTO
Apr 12, 2019 · Artificial Intelligence

Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know

This article provides a concise overview of ten fundamental machine learning algorithms—linear regression, logistic regression, linear discriminant analysis, naive Bayes, K‑nearest neighbors, learning vector quantization, support vector machines, decision trees, bagging/random forest, and boosting/AdaBoost—explaining their principles, typical use cases, and key characteristics.

KNNLinear RegressionLogistic Regression
0 likes · 13 min read
Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know
Tencent Cloud Developer
Tencent Cloud Developer
Apr 12, 2019 · Cloud Computing

Predictive Modeling for Hot Migration in Cloud Computing Using Ensemble Machine Learning

The study introduces a voting ensemble of Random Forest, AdaBoost, and XGBoost to predict hot‑migration success in cloud environments, achieving 97.44% accuracy and cutting timeout failures by roughly 80%, while quantifying feature importance—primarily CPU, network traffic, and memory—to guide proactive resource allocation.

Hot Migrationensemble modelsmachine learning
0 likes · 11 min read
Predictive Modeling for Hot Migration in Cloud Computing Using Ensemble Machine Learning
iQIYI Technical Product Team
iQIYI Technical Product Team
Apr 4, 2019 · Artificial Intelligence

Principles, Methodology, and Tools for Machine Learning Performance Optimization

The article presents a systematic, top‑down methodology for machine‑learning performance optimization—covering principles, benchmark‑driven loops, foundational hardware and software checks, profiling tools, throughput and latency metrics, and practical techniques for IO, compute, mixed‑precision, and distributed training to maximize resource utilization.

ComputeDistributed TrainingIO
0 likes · 22 min read
Principles, Methodology, and Tools for Machine Learning Performance Optimization
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 1, 2019 · Fundamentals

Must-Read Technical Books Recommended by Alibaba Experts

Alibaba’s senior engineers share their curated list of essential technical books—from software testing and design patterns to AI, machine learning, reinforcement learning, Rust programming, and database architecture—explaining why each title is valuable for developers seeking deeper knowledge and practical insights.

AIDesign PatternsRust
0 likes · 9 min read
Must-Read Technical Books Recommended by Alibaba Experts
360 Tech Engineering
360 Tech Engineering
Mar 27, 2019 · Artificial Intelligence

Understanding Gradient Descent: Basics, Advantages, and Limitations

This article explains the fundamental principle of gradient descent as the steepest‑descent optimization method, derives its direction using Taylor expansion and the Cauchy‑Schwarz inequality, illustrates why it can be slow on functions like Rosenbrock, and discusses its advantages and convergence properties.

Cauchy-Schwarz inequalityOptimizationRosenbrock function
0 likes · 6 min read
Understanding Gradient Descent: Basics, Advantages, and Limitations
Qunar Tech Salon
Qunar Tech Salon
Mar 27, 2019 · Artificial Intelligence

Profiling TensorFlow Performance with TensorBoard and Timeline

This article explains how to use TensorBoard and the Timeline tool to monitor TensorFlow GPU utilization, identify operation bottlenecks, and visualize execution times, including code examples and steps for exporting and merging profiling data for repeated runs.

GPU monitoringTensorBoardTensorFlow
0 likes · 7 min read
Profiling TensorFlow Performance with TensorBoard and Timeline
Efficient Ops
Efficient Ops
Mar 26, 2019 · Artificial Intelligence

How Live-Streaming Platforms Build Scalable Recommendation Systems

This article explains the design of a live‑streaming recommendation system, covering its overall architecture, ranking, content‑based and collaborative‑filtering methods, similarity calculations, multi‑algorithm fusion, sorting, user profiling, and evaluation metrics with practical examples and diagrams.

collaborative filteringcontent-basedevaluation metrics
0 likes · 17 min read
How Live-Streaming Platforms Build Scalable Recommendation Systems
Hulu Beijing
Hulu Beijing
Mar 26, 2019 · Artificial Intelligence

Meta-Learning Explained: Core Concepts, Scenarios, and Few-Shot Learning Benefits

This article introduces meta‑learning (learning to learn), its historical roots, explains why it excels in small‑sample and multi‑task settings, contrasts it with supervised and reinforcement learning, and outlines the theoretical reasons it enables rapid few‑shot adaptation.

Few-Shot Learningmachine learningmeta-learning
0 likes · 8 min read
Meta-Learning Explained: Core Concepts, Scenarios, and Few-Shot Learning Benefits
58 Tech
58 Tech
Mar 25, 2019 · Artificial Intelligence

Machine Learning‑Based Threshold‑Free Monitoring for Business Metrics

This article describes a monitoring system that leverages machine learning to perform threshold‑free, real‑time anomaly detection on macro business indicators such as network traffic and access volume, detailing its architecture, sample labeling, model training, and multi‑level alarm strategies.

AIBusiness MetricsOperations
0 likes · 7 min read
Machine Learning‑Based Threshold‑Free Monitoring for Business Metrics
Architecture Digest
Architecture Digest
Mar 24, 2019 · Artificial Intelligence

Beginner Resources for Machine Learning: Languages, Books, Videos, Blogs, Competitions, and Papers

This article compiles a comprehensive set of beginner-friendly machine‑learning resources—including recommended programming languages, essential textbooks, video courses, influential blogs, competition platforms, and notable conference papers—to help newcomers build a solid foundation and practical experience.

AIBooksbeginner resources
0 likes · 9 min read
Beginner Resources for Machine Learning: Languages, Books, Videos, Blogs, Competitions, and Papers
Beike Product & Technology
Beike Product & Technology
Mar 21, 2019 · Artificial Intelligence

Optimization Foundations and Applications in Machine Learning and Computer Vision

This article introduces how machine learning problems are formulated as optimization tasks, explains the construction of objective functions with examples such as linear regression, robust fitting, regularization, and demonstrates various applications ranging from K‑means clustering to image inpainting and 3D reconstruction.

Linear RegressionOptimizationRegularization
0 likes · 9 min read
Optimization Foundations and Applications in Machine Learning and Computer Vision
Hulu Beijing
Hulu Beijing
Mar 21, 2019 · Artificial Intelligence

How GANs’ Objective Functions Evolved: From JS Divergence to Modern Variants

This article explores the evolution of Generative Adversarial Networks' objective functions, detailing the shift from Jensen‑Shannon divergence to f‑divergence, IPM‑based approaches, and auxiliary losses, while highlighting their impact on stability and performance across image, audio, and text generation tasks.

GANsGenerative Adversarial Networksdeep learning
0 likes · 9 min read
How GANs’ Objective Functions Evolved: From JS Divergence to Modern Variants
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 20, 2019 · Artificial Intelligence

How Taobao’s Search & Recommendation Algorithms Evolved: From Rules to Cognitive AI

This article reviews the evolution of Taobao’s search and recommendation technology, tracing its journey from simple statistical models and rule‑based systems through large‑scale machine learning and real‑time online learning to modern deep‑learning and cognitive intelligence approaches that drive e‑commerce innovation.

Search Algorithmscognitive AIdeep learning
0 likes · 16 min read
How Taobao’s Search & Recommendation Algorithms Evolved: From Rules to Cognitive AI
DataFunTalk
DataFunTalk
Mar 19, 2019 · Artificial Intelligence

Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems

This article explores how Field-aware Factorization Machines (FFM) can be employed to replace multi‑path recall strategies in industrial recommendation systems, detailing model principles, embedding construction, integration of user, item and context features, performance considerations, and potential for unifying recall and ranking stages.

EmbeddingFFMRecall
0 likes · 51 min read
Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems
Hulu Beijing
Hulu Beijing
Mar 19, 2019 · Artificial Intelligence

Understanding Variational Autoencoders: Core Concepts and Training Explained

This article introduces Variational Autoencoders (VAEs), compares them with GANs, explains the underlying variational inference principle, and details how VAEs are trained using the evidence lower bound, complemented by visual diagrams and key equations.

Generative ModelsVAEVariational AutoEncoder
0 likes · 4 min read
Understanding Variational Autoencoders: Core Concepts and Training Explained
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 19, 2019 · Artificial Intelligence

Unlocking Anomaly Detection: Techniques from Time Series to Deep Learning

This comprehensive guide explores anomaly (outlier) detection across diverse methods—including time‑series analysis, statistical tests, distance metrics, matrix factorization, graph approaches, behavior‑sequence modeling, and supervised machine‑learning models—highlighting their principles, formulas, and practical use cases such as fraud prevention and system monitoring.

Time Seriesanomaly detectiondeep learning
0 likes · 17 min read
Unlocking Anomaly Detection: Techniques from Time Series to Deep Learning
DataFunTalk
DataFunTalk
Mar 12, 2019 · Artificial Intelligence

Demand Forecasting Practices in Alibaba Retail: From Mean Models to Deep Learning

This article outlines Alibaba Retail's demand forecasting workflow, describing the evolution from simple mean and time‑series models to machine‑learning and deep‑learning approaches, the incorporation of feature engineering, operational plans, and methods for estimating prediction uncertainty to support intelligent replenishment.

AIInventoryTime Series
0 likes · 13 min read
Demand Forecasting Practices in Alibaba Retail: From Mean Models to Deep Learning
DataFunTalk
DataFunTalk
Mar 11, 2019 · Artificial Intelligence

Practical Implementation of Personalized Recommendation Systems: Overview, Algorithms, Challenges, and Architecture

This article presents a comprehensive overview of personalized recommendation systems, covering their purpose, common algorithms, development challenges, the multi‑layer architecture used at DataGrand, optimization techniques, and the range of services offered to enterprise customers.

big datacollaborative filteringmachine learning
0 likes · 18 min read
Practical Implementation of Personalized Recommendation Systems: Overview, Algorithms, Challenges, and Architecture
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 11, 2019 · Artificial Intelligence

How Adversarial Attacks Threaten AI: Real-World Cases & Alibaba’s Defense

AI brings convenience but also new security challenges; this article explains the two main sources of AI safety issues, details adversarial example techniques, showcases applications such as face‑recognition attacks and robust captcha designs, and highlights Alibaba’s research and the IJCAI‑19 AI adversarial competition.

AI securityCAPTCHAadversarial examples
0 likes · 8 min read
How Adversarial Attacks Threaten AI: Real-World Cases & Alibaba’s Defense
MaGe Linux Operations
MaGe Linux Operations
Mar 1, 2019 · Artificial Intelligence

Master Python Data Mining & Machine Learning: From Preprocessing to Classification

This comprehensive guide introduces data mining and machine learning concepts, walks through Python data preprocessing techniques, reviews common classification algorithms, demonstrates an Iris flower classification case, and offers practical tips for selecting the most suitable algorithm for a given problem.

Pythonclassification algorithmsdata mining
0 likes · 21 min read
Master Python Data Mining & Machine Learning: From Preprocessing to Classification
JD Tech Talk
JD Tech Talk
Mar 1, 2019 · Artificial Intelligence

Introduction to H2O AutoML: Overview, Practical Workflow, and Model Deployment

This article introduces the open‑source H2O platform, explains how to install and use its Python API for data loading, preprocessing, model training with GBM and AutoML, evaluates results with AUC, and describes model deployment via POJO/MOJO as well as the visual Flow UI, concluding with reflections on the role of automated modeling in data science.

AutoMLData ScienceH2O
0 likes · 12 min read
Introduction to H2O AutoML: Overview, Practical Workflow, and Model Deployment
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 27, 2019 · Artificial Intelligence

Can AI Crush Online Rumors? Inside Alibaba’s Rumor‑Crushing Machine

The article explores how Alibaba’s DAMO Academy uses AI to detect and dismantle online misinformation through a three‑step analysis of source credibility, content verification, and propagation paths, highlighting a record‑breaking 81% accuracy in the SemEval fake‑news competition.

AINatural Language ProcessingSemEval
0 likes · 10 min read
Can AI Crush Online Rumors? Inside Alibaba’s Rumor‑Crushing Machine
Vipshop Quality Engineering
Vipshop Quality Engineering
Feb 22, 2019 · Artificial Intelligence

How Vipshop Built an AI‑Powered Sentiment Analysis System for Real‑Time Customer Feedback

Vipshop's in‑house sentiment monitoring platform integrates web‑scraped reviews, WeChat comments and internal service messages, applying lexical sentiment scoring, dictionary‑based Chinese word segmentation, TF‑IDF keyword ranking and lightweight classification to deliver real‑time insights, alerts and actionable reports for thousands of daily user comments.

NLPSentiment Analysisbig data
0 likes · 17 min read
How Vipshop Built an AI‑Powered Sentiment Analysis System for Real‑Time Customer Feedback
58 Tech
58 Tech
Feb 21, 2019 · Artificial Intelligence

Threshold‑Free Business Metric Monitoring Using Machine Learning

This article describes how a machine‑learning‑driven monitoring system replaces fixed thresholds with personalized, anomaly‑based detection for business‑level metrics such as network traffic and access volume, detailing the architecture, sample labeling, model training, alarm grading, and operational benefits.

AI OpsBusiness Metricsalarm grading
0 likes · 8 min read
Threshold‑Free Business Metric Monitoring Using Machine Learning
ITPUB
ITPUB
Feb 16, 2019 · Artificial Intelligence

A 1.59 Million‑Image NSFW Dataset Released for Advanced Content Filtering

Data scientist Evgeny Bazarov has open‑sourced a 1.589 million‑image NSFW dataset organized into 159 fine‑grained categories, providing GitHub links, download scripts, and a 500 GB storage requirement, enabling researchers to build more precise adult‑content detection models.

GitHubLarge DatasetNSFW dataset
0 likes · 3 min read
A 1.59 Million‑Image NSFW Dataset Released for Advanced Content Filtering
58 Tech
58 Tech
Feb 15, 2019 · Artificial Intelligence

Precise Push Notification Architecture and Algorithm Optimization at 58.com

This article describes the evolution of 58.com's user‑set service architecture, the transition from MongoDB to RoaringBitmap storage, and the machine‑learning‑driven algorithm optimizations that enable real‑time, multi‑dimensional, and localized push notifications for millions of users.

RoaringBitmapalgorithm optimizationbitmap storage
0 likes · 13 min read
Precise Push Notification Architecture and Algorithm Optimization at 58.com
Ctrip Technology
Ctrip Technology
Feb 13, 2019 · Artificial Intelligence

Understanding TensorFlow Extended (TFX): Concepts, Data Preparation, and Model Deployment

This article introduces TensorFlow Extended (TFX), illustrating practical TensorFlow examples such as ship trajectory classification, insurance premium adjustments, and car auction pricing, then explains TFX’s data validation, schema generation, model analysis, and deployment options to streamline machine‑learning pipelines.

AITFXTensorFlow
0 likes · 12 min read
Understanding TensorFlow Extended (TFX): Concepts, Data Preparation, and Model Deployment
JD Tech
JD Tech
Feb 12, 2019 · Artificial Intelligence

Content‑Based Filtering: Concepts, Implementation, and Pros/Cons

The article explains content‑based filtering for recommendation systems, covering its basic concepts, feature requirements, implementation using vector representations and cosine similarity, advantages and disadvantages, and supplementary algorithms such as k‑Nearest Neighbor, Rocchio, decision trees, linear classifiers, and Naive Bayes.

KNNNaive BayesRocchio
0 likes · 11 min read
Content‑Based Filtering: Concepts, Implementation, and Pros/Cons
DataFunTalk
DataFunTalk
Feb 11, 2019 · Artificial Intelligence

Machine Learning Applications in Credit Anti‑Fraud

This article explains how machine learning, deep learning, and graph‑based techniques are applied to credit anti‑fraud in finance, covering fraud risk characteristics, the anti‑fraud lifecycle, rule limitations, supervised models, common algorithms, neural networks, time‑series models, and graph analytics for detecting individual and group fraud.

AIFinancial Securitycredit risk
0 likes · 11 min read
Machine Learning Applications in Credit Anti‑Fraud
Architects Research Society
Architects Research Society
Feb 9, 2019 · Artificial Intelligence

Introduction to TensorFlow and Building a Simple Neural Network for Image Classification

This article introduces TensorFlow, explains when neural networks are appropriate, outlines the general workflow for solving image‑based problems, and provides a step‑by‑step Python implementation of a multilayer perceptron that classifies handwritten digits, while also discussing TensorFlow's strengths, limitations, and alternatives.

PythonTensorFlowdeep learning
0 likes · 14 min read
Introduction to TensorFlow and Building a Simple Neural Network for Image Classification
DataFunTalk
DataFunTalk
Jan 23, 2019 · Artificial Intelligence

Deep Learning Technologies Applied to Sogou Search Advertising

This talk by Sogou search advertising researcher Shupeng explains how deep learning techniques are applied to search ad tasks such as automated creative generation and click‑through‑rate prediction, covering system workflow, data pipelines, model evolution from linear models to Wide&Deep and NFM, evaluation metrics, and future directions.

CTR estimationOnline Advertisingautomated creative
0 likes · 33 min read
Deep Learning Technologies Applied to Sogou Search Advertising
DataFunTalk
DataFunTalk
Jan 21, 2019 · Artificial Intelligence

Applying Automated Feature Engineering and Auto Modeling to Risk Control Scenarios

This article explains how automated feature engineering and auto‑modeling techniques dramatically reduce development time and improve performance in fraud‑risk detection, detailing the underlying RFM concepts, feature generation workflow, model selection, evaluation, deployment, and continuous monitoring within a risk‑control platform.

auto modelingautomated feature engineeringfraud detection
0 likes · 14 min read
Applying Automated Feature Engineering and Auto Modeling to Risk Control Scenarios
DataFunTalk
DataFunTalk
Jan 18, 2019 · Artificial Intelligence

Efficiency Optimization Practices for 58.com Search Ranking

This article presents a comprehensive overview of 58.com’s search efficiency optimization, detailing the business background, ranking framework, data, algorithm, and engineering components, describing the three-stage ranking process, strategy and platform optimizations, feature engineering, model upgrades, and the resulting performance improvements.

Online Advertisingalgorithmefficiency optimization
0 likes · 12 min read
Efficiency Optimization Practices for 58.com Search Ranking
21CTO
21CTO
Jan 16, 2019 · Artificial Intelligence

Google AI 2018: Breakthroughs in Ethics, Quantum Computing, and AutoML

Google's 2018 AI review highlights major advances across ethical AI principles, social‑impact projects, assistive technologies, quantum computing, natural‑language models like BERT, perception research, algorithms, TPU hardware, open‑source releases, robotics, healthcare applications, and plans for an even broader impact in 2019.

Artificial Intelligencehealthcaremachine learning
0 likes · 23 min read
Google AI 2018: Breakthroughs in Ethics, Quantum Computing, and AutoML
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 16, 2019 · Artificial Intelligence

How Machine Learning Can Clean Up Low‑Quality E‑Commerce Product Materials

This article explains a machine‑learning‑driven system that automatically detects and classifies poor‑quality e‑commerce product materials—such as misleading titles, exaggerated benefits, and over‑promotion—to protect consumers, reduce platform risk, and improve conversion rates during major sales events.

AITF-IDFcontent moderation
0 likes · 13 min read
How Machine Learning Can Clean Up Low‑Quality E‑Commerce Product Materials
Qunar Tech Salon
Qunar Tech Salon
Jan 16, 2019 · Artificial Intelligence

Introduction to Naive Bayes Classifier with scikit-learn

This article introduces the Naive Bayes classification algorithm, explains its theoretical basis, demonstrates how to use scikit-learn's GaussianNB class with Python code, evaluates model performance, and discusses advantages, limitations, and practical examples of the method.

ClassificationNaive BayesPython
0 likes · 11 min read
Introduction to Naive Bayes Classifier with scikit-learn
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 15, 2019 · Artificial Intelligence

How Alibaba Engineers Boost SEO with Reinforcement Learning and Attention Models

This article details Alibaba.com engineers' application of reinforcement learning, attention mechanisms, and weakly supervised techniques to extract product summaries, improve content quality, and significantly raise SEO rankings, supported by offline experiments, online A/B testing, and future research directions.

AlibabaSEOText Summarization
0 likes · 16 min read
How Alibaba Engineers Boost SEO with Reinforcement Learning and Attention Models
58 Tech
58 Tech
Jan 11, 2019 · Artificial Intelligence

Design and Implementation of an End-to-End Efficiency Optimization Platform for 58.com Classified Listings

This article describes the design and implementation of a comprehensive efficiency‑optimization platform at 58.com, detailing its end‑to‑end workflow—from log aggregation and feature extraction through machine learning model training and online experimentation—highlighting modular, configurable, and scalable solutions for multi‑business, multi‑product ranking.

click-through rateconversion ratedata pipelines
0 likes · 25 min read
Design and Implementation of an End-to-End Efficiency Optimization Platform for 58.com Classified Listings
Meituan Technology Team
Meituan Technology Team
Jan 10, 2019 · Artificial Intelligence

Deep Learning and Ranking Model Evolution for Hotel Search at Meituan

The talk explains how Meituan transformed its O2O hotel search by layering a multi‑stage retrieval pipeline with intent‑aware NLP, then progressively upgrading ranking—from XGBoost to MLPs, feature‑embedding networks, and finally a Wide‑Deep multi‑task model—while tackling data sparsity, diverse scenarios, and deploying the system via TensorFlow‑Serving and the in‑house MLX platform.

MeituanNLPRanking Models
0 likes · 33 min read
Deep Learning and Ranking Model Evolution for Hotel Search at Meituan
Qunar Tech Salon
Qunar Tech Salon
Jan 10, 2019 · Operations

Applying AIOps for Zero‑Downtime Operations at China Aviation Information

The talk by chief architect Luo Hao explains how China Aviation Information tackles heavy legacy systems, non‑standard architectures, and zero‑downtime requirements by using AIOps techniques such as automated configuration discovery, cluster analysis, fault prediction, anomaly detection, event compression and rapid root‑cause automation.

AIOpsautomationfault prediction
0 likes · 22 min read
Applying AIOps for Zero‑Downtime Operations at China Aviation Information
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 9, 2019 · Artificial Intelligence

Master Deep Learning Foundations and 14 Cutting-Edge Recommendation Models

This article introduces core deep‑learning architectures—including MLP, RNN, CNN, auto‑encoders, and RBM—explains common activation and loss functions, and then surveys fourteen influential deep‑learning‑based recommendation algorithms such as FM, wide&deep, deepFM, NCF, GBDT+LR, seq2seq and YouTube DNN, complete with model diagrams and reference links.

AIdeep learningmachine learning
0 likes · 18 min read
Master Deep Learning Foundations and 14 Cutting-Edge Recommendation Models
DataFunTalk
DataFunTalk
Jan 8, 2019 · Artificial Intelligence

Yoo Video Bottom‑Page Recommendation System: From Zero to One Practice

This article details the end‑to‑end design, recall and ranking techniques, engineering implementation, and future research directions of Tencent's Yoo video bottom‑page recommendation system, illustrating how large‑scale video recommendation is built from business needs to deep learning models.

Embeddinglarge-scale systemsmachine learning
0 likes · 13 min read
Yoo Video Bottom‑Page Recommendation System: From Zero to One Practice
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 8, 2019 · Artificial Intelligence

Unlocking Recommendation Systems: 10 Classic Machine Learning Algorithms Explained

This article surveys ten classic recommendation system algorithms—including collaborative filtering, association rules, Bayesian methods, K‑Nearest Neighbors, decision trees, random forests, matrix factorization, neural networks, word2vec, and logistic regression—detailing their principles, mathematical formulas, and practical implementation steps for real‑world applications.

Logistic Regressionassociation rulescollaborative filtering
0 likes · 25 min read
Unlocking Recommendation Systems: 10 Classic Machine Learning Algorithms Explained
Ctrip Technology
Ctrip Technology
Jan 7, 2019 · Artificial Intelligence

AIOps Practices and Exploration at Ctrip: Challenges, Solutions, and Future Outlook

This article presents Ctrip's extensive AIOps exploration, detailing operational challenges caused by massive monitoring data, the evolution of DevOps practices, the design of intelligent anomaly detection and diagnosis systems, practical use cases, and a forward‑looking perspective on the future of AI‑driven operations.

AIOpsFourier TransformOperations
0 likes · 20 min read
AIOps Practices and Exploration at Ctrip: Challenges, Solutions, and Future Outlook
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Jan 6, 2019 · Artificial Intelligence

Can JavaScript Teach k‑Nearest Neighbors? Build a Visual kNN Classifier from Scratch

This article walks through the theory of k‑nearest‑neighbors, explains feature selection and normalization, shows how to implement the algorithm with plain JavaScript classes, visualizes results on an HTML canvas, and discusses practical limitations and extensions.

JavaScriptalgorithm implementationfeature normalization
0 likes · 22 min read
Can JavaScript Teach k‑Nearest Neighbors? Build a Visual kNN Classifier from Scratch
DataFunTalk
DataFunTalk
Jan 4, 2019 · Artificial Intelligence

AI‑Powered Automated Advertising Platform: 360 Easy Placement Overview

This article presents the design and technical details of 360 Easy Placement, an AI‑driven end‑to‑end advertising platform that automates creative generation, fast review, and optimization, addressing the challenges faced by small‑and‑medium advertisers through data‑rich models, multi‑task learning, and intelligent scene recommendation.

AIOptimizationadvertising automation
0 likes · 20 min read
AI‑Powered Automated Advertising Platform: 360 Easy Placement Overview
DataFunTalk
DataFunTalk
Jan 3, 2019 · Artificial Intelligence

Machine Learning and Recommendation System Practice

This article presents a comprehensive overview of applying machine learning to recommendation systems, covering fundamental challenges such as user cold‑start, precise interest modeling, collaborative filtering, and both offline and online evaluation methods, while illustrating concepts with numerous diagrams.

AIcold startcollaborative filtering
0 likes · 9 min read
Machine Learning and Recommendation System Practice
Node Underground
Node Underground
Jan 2, 2019 · Backend Development

19 Must‑Learn Skills for Node.js Developers in 2019

In this article, independent Node.js consultant Yoni Goldberg outlines 19 essential skills and topics—from TypeScript and async‑hooks to Kubernetes, blockchain, and machine learning—that developers should explore in 2019 to boost their expertise and stay ahead in the evolving backend ecosystem.

DevOpsKubernetesNode.js
0 likes · 3 min read
19 Must‑Learn Skills for Node.js Developers in 2019
58 Tech
58 Tech
Dec 26, 2018 · Operations

Overview of the 58 Intelligent Monitoring System and Its Multi‑Dimensional Architecture

The 58 Intelligent Monitoring System provides a flexible, 24/7, multi‑dimensional monitoring solution that covers network, server, system, application and business layers, incorporates AI‑driven prediction, anomaly detection, alarm merging, root‑cause analysis and self‑healing, and offers both PC and WeChat interfaces for operators.

AlertingOperationsautomation
0 likes · 16 min read
Overview of the 58 Intelligent Monitoring System and Its Multi‑Dimensional Architecture
dbaplus Community
dbaplus Community
Dec 25, 2018 · Operations

How Ctrip Leverages AI to Revolutionize Application Operations: AIOps Practices and Insights

This article details Ctrip's journey of applying AI-driven AIOps to address application operation pain points, describing their evolution from manual scripts to intelligent automation, the implementation of anomaly detection, smart diagnosis, online/offline mixed deployment, and future considerations for scalable, cost‑effective operations.

AIOpsOnline/Offline Deploymentanomaly detection
0 likes · 29 min read
How Ctrip Leverages AI to Revolutionize Application Operations: AIOps Practices and Insights
DataFunTalk
DataFunTalk
Dec 21, 2018 · Artificial Intelligence

Iterative Evolution of iQIYI Video Search Ranking Models

This article details iQIYI's practical experience in building and iterating its video search system, covering basic relevance, semantic matching via translation and click models, deep‑learning approaches, and ranking model evolution from heuristic rules to learning‑to‑rank, highlighting challenges, solutions, and performance gains.

machine learningsearch rankingsemantic matching
0 likes · 20 min read
Iterative Evolution of iQIYI Video Search Ranking Models
Meituan Technology Team
Meituan Technology Team
Dec 20, 2018 · Artificial Intelligence

Demystifying Learning to Rank: From Core Algorithms to Scalable Online Sorting Architecture

This article provides a comprehensive, system‑engineer‑focused guide to Learning to Rank, covering fundamental machine‑learning concepts, evaluation metrics such as Precision, nDCG and ERR, training‑testing‑inference stages, pointwise/pairwise/listwise methods, and a detailed multi‑layer online ranking architecture with feature, model and recall governance.

A/B testingDomain-Driven DesignOnline Ranking Architecture
0 likes · 29 min read
Demystifying Learning to Rank: From Core Algorithms to Scalable Online Sorting Architecture
Tencent Cloud Developer
Tencent Cloud Developer
Dec 17, 2018 · Artificial Intelligence

An Overview of Computer Vision: Fundamentals, Traditional Techniques, and Deep Learning Applications

The talk provides a comprehensive overview of computer vision, defining its scope, detailing low‑, mid‑, and high‑level processing pipelines, reviewing classic filters and feature extractors, explaining deep‑learning breakthroughs such as CNNs and YOLO, and showcasing Tencent Cloud AI services, career paths, and learning resources.

AIcomputer visionmachine learning
0 likes · 43 min read
An Overview of Computer Vision: Fundamentals, Traditional Techniques, and Deep Learning Applications
Suning Technology
Suning Technology
Dec 17, 2018 · Artificial Intelligence

How Search & Recommendation Technologies Evolve: Insights from Suning’s 2018 Conference

The 2018 Suning Search & Recommendation Technology Conference in Nanjing gathered over 400 industry experts to discuss search engine evolution, recommendation algorithm models, multi‑source data fusion, multimedia video retrieval, and AI‑driven advertising, highlighting practical implementations and future research directions.

data fusionmachine learningrecommendation
0 likes · 5 min read
How Search & Recommendation Technologies Evolve: Insights from Suning’s 2018 Conference
JD Tech
JD Tech
Dec 17, 2018 · Operations

Improving JD Intelligent Supply Chain Efficiency and System Stability for Major Sales Events

The article details JD's intelligent supply chain enhancements—including machine‑learning demand forecasting, a new "explosive product warehouse" model, non‑stock fulfillment visualization, blockchain‑based product traceability, and comprehensive system‑stability measures such as data‑consistency checkpoints, throughput buffering, and 24/7 incident response—to boost efficiency and reliability during large‑scale promotions.

Operationsbig datablockchain
0 likes · 7 min read
Improving JD Intelligent Supply Chain Efficiency and System Stability for Major Sales Events
Meituan Technology Team
Meituan Technology Team
Dec 13, 2018 · Artificial Intelligence

Advances in Machine Learning for Real‑Time Delivery at Meituan

Meituan’s AI‑driven “Superbrain” platform combines real‑time big‑data processing, fine‑grained location perception, high‑precision ETA forecasting, multi‑rider dispatch and dynamic pricing to cut instant food‑delivery times from about an hour to roughly thirty minutes while boosting efficiency, cost savings and user experience.

AIETA predictionReal-time Delivery
0 likes · 19 min read
Advances in Machine Learning for Real‑Time Delivery at Meituan
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 12, 2018 · Artificial Intelligence

Tackling Pseudo-Exposure in Mobile E-Commerce: A Contextual Multiple-Play Bandit Approach

To address the pseudo-exposure problem that reduces click-through rates in mobile e-commerce recommendation, the authors model the task as a contextual multiple-play bandit, propose weighted sample and similarity-enhanced linear reward extensions, provide sublinear regret proofs, and demonstrate significant CTR gains on real Taobao data.

Bandit AlgorithmsCTR optimizationcontextual multi-play
0 likes · 30 min read
Tackling Pseudo-Exposure in Mobile E-Commerce: A Contextual Multiple-Play Bandit Approach
Efficient Ops
Efficient Ops
Dec 11, 2018 · Operations

How Alibaba’s AI‑Powered Monitoring Tackles Complex Business Anomalies

In this talk, Alibaba senior tech expert Wang Zhaogang explains how intelligent monitoring, powered by machine‑learning algorithms and multi‑metric analysis, addresses the challenges of diverse business scenarios, enhances anomaly detection, improves root‑cause analysis, and shapes the future of smart operations.

OperationsRoot Cause Analysisanomaly detection
0 likes · 23 min read
How Alibaba’s AI‑Powered Monitoring Tackles Complex Business Anomalies
JD Tech
JD Tech
Dec 6, 2018 · Operations

Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain

JD's chief scientist Shen Zuo‑jun explains how shortening the decision chain with end‑to‑end algorithms and intelligent multi‑level replenishment dramatically improves inventory turnover, stock availability, and forecasting accuracy, showcasing a novel supply‑chain research direction that integrates AI, big data, and human expertise.

End-to-EndOperationsforecasting
0 likes · 9 min read
Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain
Beike Product & Technology
Beike Product & Technology
Dec 6, 2018 · Artificial Intelligence

Real Estate Rental Platform: True Listing Model and Credit System Construction

This presentation details how Beike Rental leverages big data and machine‑learning techniques to detect non‑authentic listings, build a four‑criterion true‑listing model, develop pricing and image‑analysis models, and design a merchant credit scoring system that improves service quality and market efficiency.

Credit ScoringData Qualityimage analysis
0 likes · 27 min read
Real Estate Rental Platform: True Listing Model and Credit System Construction
Tencent Cloud Developer
Tencent Cloud Developer
Dec 5, 2018 · Artificial Intelligence

19 AI Technologies That Are Currently Dominating

The article surveys the nineteen leading AI technologies—from natural language generation and speech recognition to digital twins and marketing automation—detailing their core functions, common use cases such as customer service, security, content creation, and the key vendors delivering each solution.

AI TechnologiesArtificial IntelligenceNatural Language Processing
0 likes · 17 min read
19 AI Technologies That Are Currently Dominating
21CTO
21CTO
Dec 3, 2018 · Information Security

Can Your Keyboard Secrets Be Heard? Inside the Keytap Acoustic Attack

This article explains how the open‑source Keytap project captures short audio snippets from a microphone to reconstruct typed characters, outlines its four‑step process of data collection, model building, keystroke detection, and character identification, and compares it with related acoustic eavesdropping research.

acoustic side-channelaudio keylogginginformation security
0 likes · 8 min read
Can Your Keyboard Secrets Be Heard? Inside the Keytap Acoustic Attack
MaGe Linux Operations
MaGe Linux Operations
Nov 30, 2018 · Artificial Intelligence

Avoid These Common NumPy Pitfalls When Doing Machine Learning

This article examines frequent traps when using NumPy for matrix operations in machine learning, comparing its quirks to MATLAB/Octave and offering practical insights to prevent shape errors, inefficient indexing, confusing syntax, and unintuitive code patterns.

Data AnalysisNumPyPython
0 likes · 7 min read
Avoid These Common NumPy Pitfalls When Doing Machine Learning
Liulishuo Tech Team
Liulishuo Tech Team
Nov 29, 2018 · Cloud Native

Building an Efficient Machine Learning Training Platform on Kubernetes

This article describes how the Liulishuo algorithm team designed and implemented a Kubernetes‑based training platform that addresses the iterative, data‑intensive, and resource‑dynamic characteristics of machine learning workloads by pooling resources, enabling rapid provisioning, and optimizing scheduling and storage.

Cloud NativeKubernetesmachine learning
0 likes · 9 min read
Building an Efficient Machine Learning Training Platform on Kubernetes
37 Interactive Technology Team
37 Interactive Technology Team
Nov 27, 2018 · Artificial Intelligence

37 Xiao Luban: A Machine‑Learning Linear Regression System for Automatic Banner Generation

The article describes a PHP engineer who built a machine‑learning linear regression system called 37 Xiao Luban to automatically generate game banner images, cutting production time from hours to minutes, using polynomial regression on collected scaling data, achieving 80‑90% usability.

AIBanner GenerationLinear Regression
0 likes · 7 min read
37 Xiao Luban: A Machine‑Learning Linear Regression System for Automatic Banner Generation
MaGe Linux Operations
MaGe Linux Operations
Nov 26, 2018 · Artificial Intelligence

Master Python Machine Learning in 14 Steps: From Zero to Expert

This comprehensive guide walks beginners through fourteen practical steps to learn Python machine learning, covering essential Python skills, core scientific libraries, fundamental algorithms, advanced techniques like SVM and ensemble methods, dimensionality reduction, and deep learning with TensorFlow, all using free online resources.

Data SciencePythondeep learning
0 likes · 22 min read
Master Python Machine Learning in 14 Steps: From Zero to Expert
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 21, 2018 · Artificial Intelligence

How Unsupervised Autoencoders Boost International Credit Card Fraud Detection

International credit card fraud, a growing threat, can be more effectively identified by applying unsupervised autoencoder models, which outperform traditional rule‑based systems by tripling recall and increasing accuracy by 40%, while reducing maintenance costs and adapting to new fraud patterns.

AutoencoderUnsupervised Learninganomaly detection
0 likes · 9 min read
How Unsupervised Autoencoders Boost International Credit Card Fraud Detection
DataFunTalk
DataFunTalk
Nov 21, 2018 · Artificial Intelligence

Personalized Recommendation System of 51 Credit Card: Architecture, Challenges, and Growth Cases

This article details how 51 Credit Card leverages artificial intelligence to build a personalized recommendation system, covering business pain points, technical challenges, a three‑layer tagging architecture from bill and app data, model deployment pipelines, and real‑world growth case studies that boosted conversion and ROI.

AIdata engineeringfinance
0 likes · 14 min read
Personalized Recommendation System of 51 Credit Card: Architecture, Challenges, and Growth Cases
Tencent Cloud Developer
Tencent Cloud Developer
Nov 20, 2018 · Artificial Intelligence

Top 18 Machine Learning Platforms Every Developer Should Know

This guide lists and briefly describes 18 open‑source and cloud‑based machine learning platforms—from H2O and TensorFlow to Azure ML and AWS services—highlighting their key features, supported languages, and typical use cases for developers at any skill level.

AI toolsclouddeep learning
0 likes · 12 min read
Top 18 Machine Learning Platforms Every Developer Should Know
Tencent Cloud Developer
Tencent Cloud Developer
Nov 19, 2018 · Artificial Intelligence

10 Open-Source Tools and Frameworks for Artificial Intelligence

The article surveys ten leading open-source AI tools and frameworks—including TensorFlow, SystemML, Caffe, Apache Mahout, OpenNN, Torch, Neuroph, Deeplearning4j, Mycroft, and OpenCog—detailing their primary features, supported languages, hardware compatibility, and typical use cases for research and development.

Artificial IntelligenceFrameworksdeep learning
0 likes · 12 min read
10 Open-Source Tools and Frameworks for Artificial Intelligence
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 16, 2018 · Artificial Intelligence

How Alibaba’s Search Engine Evolved Over a Decade of Double‑11: From Offline Models to Real‑Time AI

This article traces the ten‑year evolution of Alibaba’s e‑commerce search system, detailing four major stages—from the early Pora streaming engine to dual‑link real‑time architectures, the integration of deep and reinforcement learning, and the shift to large‑scale online deep learning—while highlighting the technical drivers and future AI‑enabled search vision.

e-commercemachine learningonline learning
0 likes · 16 min read
How Alibaba’s Search Engine Evolved Over a Decade of Double‑11: From Offline Models to Real‑Time AI
dbaplus Community
dbaplus Community
Nov 11, 2018 · Operations

How 360 Built an AI‑Powered Ops System to Cut Costs and Boost Efficiency

360’s AI‑ops team shares a year‑long journey of turning massive operational data into intelligent solutions—covering background, their AIOps philosophy, practical modules like capacity forecasting, host classification, resource reclamation, smart MySQL scheduling, anomaly detection, alarm reduction, and root‑cause analysis—to dramatically improve cost, efficiency, and reliability.

AIOpsCapacity Forecastinganomaly detection
0 likes · 16 min read
How 360 Built an AI‑Powered Ops System to Cut Costs and Boost Efficiency