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Architects' Tech Alliance
Architects' Tech Alliance
Jan 29, 2021 · Artificial Intelligence

Comprehensive Overview of Machine Learning: Types, Industry Chain, and Key Technologies

This article provides a detailed introduction to machine learning, covering its definition, learning modes such as supervised, unsupervised and reinforcement learning, shallow versus deep learning, the full industry chain from AI chips to cloud and big‑data services, and the major open‑source frameworks and platforms driving the field.

AI chipsBig DataUnsupervised Learning
0 likes · 11 min read
Comprehensive Overview of Machine Learning: Types, Industry Chain, and Key Technologies
DataFunTalk
DataFunTalk
Jan 29, 2021 · Artificial Intelligence

Content Embedding Practices and Challenges at Hulu

This article presents Hulu's multi‑layered approach to content understanding and embedding, describing tag‑based graph embeddings, metadata‑BERT enhancements, multimodal video/audio feature aggregation, and various applications such as similarity search, ranking, cold‑start retrieval, and collection modeling, while also discussing current limitations and open research questions.

HuluMetadataRecommendation Systems
0 likes · 12 min read
Content Embedding Practices and Challenges at Hulu
MaGe Linux Operations
MaGe Linux Operations
Jan 28, 2021 · Fundamentals

Unlock the Power of NumPy: Visual Guide to Arrays and Operations

This article provides a visual, step‑by‑step introduction to NumPy’s core concepts—vectors, matrices, higher‑dimensional arrays, creation, indexing, arithmetic, broadcasting, and common functions—helping developers and researchers understand how the library works and apply it efficiently in Python data‑science workflows.

Array OperationsNumPyPython
0 likes · 18 min read
Unlock the Power of NumPy: Visual Guide to Arrays and Operations
Meituan Technology Team
Meituan Technology Team
Jan 28, 2021 · Artificial Intelligence

Trajectory Prediction Algorithm for Autonomous Vehicles: Winning Solutions in NeurIPS 2020 INTERPRET Challenge

Meituan’s unmanned delivery team secured first place in the Generalizability track and second in the Regular track of the NeurIPS 2020 INTERPRET trajectory‑prediction challenge by employing a mixed‑attention graph‑transformer with dual‑channel GRU and adaptive map processing, achieving ADEs of 0.5339 m and 0.1912 m respectively.

Graph Neural NetworkNeurIPSautonomous vehicles
0 likes · 15 min read
Trajectory Prediction Algorithm for Autonomous Vehicles: Winning Solutions in NeurIPS 2020 INTERPRET Challenge
DataFunTalk
DataFunTalk
Jan 23, 2021 · Artificial Intelligence

Feature Engineering: Mapping Raw Data to Machine‑Learning Features and Best Practices

This article explains how feature engineering transforms raw data into numerical representations for machine‑learning models, covering mapping of numeric and categorical values, one‑hot and multi‑hot encoding, sparse representations, scaling, handling outliers, binning, data quality checks, and feature interactions to capture non‑linear relationships.

EncodingFeature EngineeringScaling
0 likes · 20 min read
Feature Engineering: Mapping Raw Data to Machine‑Learning Features and Best Practices
58 Tech
58 Tech
Jan 22, 2021 · Artificial Intelligence

AI + CRM: Improving Enterprise Performance and Efficiency

This article describes how 58.com’s AI Lab integrated machine‑learning and recommendation techniques into its CRM system, redesigning sales workflows, introducing the “Michigan” model, and deploying XGBoost and MMoE models to boost key metrics such as transfer rate and 60‑second effective call rate, achieving significant performance gains.

AICRMmachine learning
0 likes · 20 min read
AI + CRM: Improving Enterprise Performance and Efficiency
21CTO
21CTO
Jan 20, 2021 · Databases

Why Time Series Databases Are the Future of Your Data

Time series databases let you retain full historical records, enabling analysis, visualization, machine learning and automation across domains like finance, weather and IoT, and the article explains why they’re essential, how they differ from traditional databases, and how to start using them.

DatabasesTime-seriesmachine learning
0 likes · 7 min read
Why Time Series Databases Are the Future of Your Data
DataFunTalk
DataFunTalk
Jan 18, 2021 · Artificial Intelligence

Graph Algorithm Design and Optimization for Detecting Black Market Users in Virtual Networks

This article presents a comprehensive overview of using graph representation learning and clustering, particularly GraphSAGE and its optimizations, to identify and mitigate black‑market (malicious) accounts in virtual networks, discussing background, objectives, challenges such as isolation and heterogeneity, and evaluation results.

GraphSAGEblack market detectiongraph algorithms
0 likes · 13 min read
Graph Algorithm Design and Optimization for Detecting Black Market Users in Virtual Networks
MaGe Linux Operations
MaGe Linux Operations
Jan 17, 2021 · Artificial Intelligence

Top 10 Must‑Know Python Libraries of 2020 (Plus Bonus Picks)

This article presents the 2020 Python library ranking, explaining the selection criteria and highlighting ten standout libraries—ranging from CLI tools like Typer and Rich to AI‑focused frameworks such as Hydra, PyTorch Lightning, Hummingbird, and HiPlot—plus several honorable mentions.

AICLIPython
0 likes · 13 min read
Top 10 Must‑Know Python Libraries of 2020 (Plus Bonus Picks)
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Jan 15, 2021 · Artificial Intelligence

Recommendation System Architecture and Engineering Overview

This article presents a comprehensive overview of a recommendation system, covering its business background, purpose, detailed engineering architecture—including data sources, computation, storage, online learning, service and access layers—and discusses key challenges, module design, and practical reflections.

AB testingData engineeringTensorFlow
0 likes · 14 min read
Recommendation System Architecture and Engineering Overview
DataFunTalk
DataFunTalk
Jan 15, 2021 · Artificial Intelligence

Zhihu Search Text Relevance Evolution and BERT Knowledge Distillation Practices

This talk by Zhihu search algorithm engineer Shen Zhan details the evolution of text relevance models from TF‑IDF/BM25 to deep semantic matching and BERT, explains the challenges of deploying BERT at scale, and describes practical knowledge‑distillation techniques that improve both online latency and offline storage while maintaining search quality.

BERTKnowledge Distillationmachine learning
0 likes · 14 min read
Zhihu Search Text Relevance Evolution and BERT Knowledge Distillation Practices
21CTO
21CTO
Jan 11, 2021 · Artificial Intelligence

How to Build a Recommendation System from Scratch: Key Concepts and Strategies

This article explains the fundamentals of recommendation systems, covering data collection, user and content profiling, system architecture, algorithmic pipelines such as recall, filtering, ranking, and evaluation metrics, while also discussing practical challenges like echo chambers and long‑term user value.

Evaluationalgorithmmachine learning
0 likes · 16 min read
How to Build a Recommendation System from Scratch: Key Concepts and Strategies
DataFunTalk
DataFunTalk
Jan 8, 2021 · Artificial Intelligence

Deconstructing E‑commerce Recommendation Systems: Architecture, Challenges, and Strategies

This article provides a comprehensive overview of e‑commerce recommendation systems, detailing their end‑to‑end workflow, key challenges such as multi‑scenario objectives and data loops, core components like recall and ranking, model evolution, feature engineering, evaluation metrics, and practical considerations for building a healthy, multi‑objective recommendation ecosystem.

e‑commercemachine learningpersonalization
0 likes · 17 min read
Deconstructing E‑commerce Recommendation Systems: Architecture, Challenges, and Strategies
DataFunTalk
DataFunTalk
Jan 7, 2021 · Artificial Intelligence

User Preference Mining and Modeling Practices at Beike

This article introduces the concept of user preference mining, discusses challenges such as accurate expression, interpretability, and high-dimensional preferences, reviews statistical and model-based approaches including weighting, decay, XGBoost, DNN, LSTM, Seq4Rec, and Deep Interest Network, and describes their practical implementation at Beike.

BeikeDeep LearningEmbedding
0 likes · 19 min read
User Preference Mining and Modeling Practices at Beike
Architects Research Society
Architects Research Society
Jan 6, 2021 · Artificial Intelligence

DVC: Data Version Control for Machine Learning Projects

DVC is an open‑source data version control system that extends Git to manage large machine‑learning models, datasets, and pipelines, enabling reproducible experiments, low‑friction branching, metric tracking, and seamless collaboration across various storage backends.

DVCML PipelinesReproducibility
0 likes · 9 min read
DVC: Data Version Control for Machine Learning Projects
DataFunTalk
DataFunTalk
Jan 3, 2021 · Artificial Intelligence

iQIYI Machine Learning Platform: Development History, Features, and Practical Experience

This article details the evolution of iQIYI's machine learning platform—from its early Javis‑based deep‑learning system to three major versions that introduced visual workflow, distributed scheduling, auto‑tuning, large‑scale training support, model management, and online prediction—while sharing practical lessons and a real anti‑cheat use case.

Big DataModel ManagementPlatform
0 likes · 13 min read
iQIYI Machine Learning Platform: Development History, Features, and Practical Experience
DataFunSummit
DataFunSummit
Dec 29, 2020 · Artificial Intelligence

Graph Neural Networks for Recommendation: Principles, Frameworks, and Tencent Practice

This article introduces graph neural networks, explains their fundamentals and GraphSAGE/DGI algorithms, and demonstrates how Tencent applies them to recommendation scenarios such as video and WeChat content, highlighting network construction, feature engineering, sampling and aggregation techniques, and practical performance gains.

DGIGraphSAGERecommendation Systems
0 likes · 8 min read
Graph Neural Networks for Recommendation: Principles, Frameworks, and Tencent Practice
Amap Tech
Amap Tech
Dec 24, 2020 · Artificial Intelligence

Advancing Mobile Navigation Accuracy: Lessons from the IPIN2020 Competition and VDR Technology

The Wuhan‑Amap team won IPIN2020’s vehicle‑navigation track by using big‑data mining and neural‑network‑enhanced Vehicle‑Dead Reckoning to fuse smartphone GNSS, IMU, and barometer data, overcoming GPS outages and sensor limitations, and demonstrating that machine‑learning‑driven inertial navigation can achieve vehicle‑grade accuracy on consumer phones.

Indoor positioningVDRmachine learning
0 likes · 8 min read
Advancing Mobile Navigation Accuracy: Lessons from the IPIN2020 Competition and VDR Technology
JD Tech Talk
JD Tech Talk
Dec 18, 2020 · Artificial Intelligence

Model Online Inference System: Architecture, Components, and Deployment Strategies

This article examines the challenges of moving machine‑learning models from offline training to online serving, proposes a modular architecture—including model gateway, data source gateway, business service center, monitoring, and RPC components—to enable rapid model deployment, version management, traffic mirroring, gray‑release, and real‑time monitoring.

Model Servingmachine learningmonitoring
0 likes · 10 min read
Model Online Inference System: Architecture, Components, and Deployment Strategies
DataFunTalk
DataFunTalk
Dec 18, 2020 · Artificial Intelligence

Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions

This article explains the evolution of federated learning, contrasts Google’s cross‑device horizontal approach with China’s cross‑silo vertical implementations, analyzes their security vulnerabilities, and demonstrates how secure multi‑party computation—including differential privacy, secure aggregation, and secret‑sharing techniques—can address these challenges while highlighting performance trade‑offs.

Secure Aggregationcross-silodifferential privacy
0 likes · 18 min read
Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions
TAL Education Technology
TAL Education Technology
Dec 17, 2020 · Artificial Intelligence

Web Front‑End Intelligent Computing: Concepts, Implementation, and Applications

This article explains how AI technologies are transitioning from labs to the web, covering neural network fundamentals, the distinction between cloud and edge intelligence, implementation pipelines, offline model optimization, online inference backends like WebGL and WASM, and practical web front‑end AI use cases.

FrontendWeb AImachine learning
0 likes · 10 min read
Web Front‑End Intelligent Computing: Concepts, Implementation, and Applications
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 16, 2020 · Artificial Intelligence

How to Detect and Prevent Advertising Fraud with Advanced AI Techniques

This article explains the scale of online ad fraud, outlines common advertising billing models, describes how fake traffic generates revenue, defines invalid clicks, and presents a comprehensive anti‑fraud system that combines rule‑based methods, feature engineering, and AI models such as TextCNN, BiLSTM, BERT, Wide&Deep and GraphSage to identify and block fraudulent ad clicks.

AIAd FraudAdvertising
0 likes · 33 min read
How to Detect and Prevent Advertising Fraud with Advanced AI Techniques
Python Programming Learning Circle
Python Programming Learning Circle
Dec 16, 2020 · Artificial Intelligence

Linear Regression Theory and Python Implementation with Iris and Boston Datasets

This article explains the fundamentals of linear regression, including regression formulas, loss functions, and error metrics, and provides complete Python code using scikit‑learn to perform both simple and multiple linear regression on the Iris and Boston housing datasets, along with model evaluation and visualization.

Pythondata-sciencelinear regression
0 likes · 7 min read
Linear Regression Theory and Python Implementation with Iris and Boston Datasets
DataFunTalk
DataFunTalk
Dec 9, 2020 · Artificial Intelligence

DataFunTalk Year-End Knowledge Graph Forum – Schedule, Speakers, and Registration Details

The DataFunTalk Year-End Knowledge Graph Forum on December 19, 2023, will be streamed live and feature four expert speakers from Baidu, Alibaba, Meituan, and Beike who will share cutting‑edge knowledge‑graph technologies, applications, and practical techniques for industry and research audiences.

Artificial IntelligenceKnowledge Graphconference
0 likes · 7 min read
DataFunTalk Year-End Knowledge Graph Forum – Schedule, Speakers, and Registration Details
21CTO
21CTO
Nov 29, 2020 · Artificial Intelligence

Decode Math Symbols with Python: From Summation to Matrix Multiplication

Learn how to translate common mathematical symbols such as summation, product, factorial, conditional expressions, and matrix multiplication into clear Python code, revealing the underlying computations and helping data scientists and ML practitioners deepen their mathematical intuition through practical examples.

Code ExamplesMatrix MultiplicationPython
0 likes · 7 min read
Decode Math Symbols with Python: From Summation to Matrix Multiplication
DataFunTalk
DataFunTalk
Nov 28, 2020 · Artificial Intelligence

Building Fast-Iterating Machine Learning Systems at Tubi: A/B Testing, Simple Models, and Embedding Strategies

This article shares Tubi's practical experience in rapidly iterating machine‑learning systems, emphasizing the early importance of simple end‑to‑end A/B testing platforms, clear launch plans, heat‑based and embedding‑based ranking models, and a culture of fast experimentation over complex deep‑learning research.

A/B testingArtificial IntelligenceData engineering
0 likes · 8 min read
Building Fast-Iterating Machine Learning Systems at Tubi: A/B Testing, Simple Models, and Embedding Strategies
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 27, 2020 · Artificial Intelligence

Evolution and Experience of iQIYI's Machine Learning Platform

iQIYI’s Machine Learning Platform evolved from the specialized Javis deep‑learning system into a unified, low‑threshold solution for algorithm engineers, analysts, and developers, adding visual pipeline building, multi‑framework scheduling, automatic hyper‑parameter tuning, parameter‑server training, and scalable online prediction, dramatically boosting business efficiency and detection performance.

AIPlatform Engineeringauto-tuning
0 likes · 13 min read
Evolution and Experience of iQIYI's Machine Learning Platform
DeWu Technology
DeWu Technology
Nov 26, 2020 · Artificial Intelligence

Automated Captcha Recognition Using Machine Learning

The article outlines a machine‑learning pipeline for automated captcha recognition, covering dataset generation, image preprocessing, segmentation via clustering or watershed methods, and classification using classic models and CNNs, achieving roughly 94% accuracy while noting the growing complexity of modern captchas and recommending developer collaboration when feasible.

CaptchaPythondata augmentation
0 likes · 23 min read
Automated Captcha Recognition Using Machine Learning
Alibaba Terminal Technology
Alibaba Terminal Technology
Nov 26, 2020 · Frontend Development

How AI Is Transforming Front‑End Development: Inside Alibaba’s imgcook and D2C Innovations

This article examines the evolution of AI‑driven front‑end code generation—from early research like pix2code to Alibaba’s imgcook platform—detailing technical architectures, performance metrics, intelligent capability upgrades, and future directions for automated UI‑to‑code solutions.

AI Code GenerationD2CPipcook
0 likes · 22 min read
How AI Is Transforming Front‑End Development: Inside Alibaba’s imgcook and D2C Innovations
Bitu Technology
Bitu Technology
Nov 20, 2020 · Artificial Intelligence

Building a Model-Driven Machine Learning System at Tubi: From Simple A/B Tests to Embedding-Based Recommendations

The article shares Tubi's practical experience in building a fast‑iterating machine‑learning platform, emphasizing early measurement, simple end‑to‑end A/B testing, clear launch plans, lightweight popularity and embedding models, and rapid experimentation to drive product decisions.

A/B testingArtificial IntelligenceEmbedding
0 likes · 8 min read
Building a Model-Driven Machine Learning System at Tubi: From Simple A/B Tests to Embedding-Based Recommendations
Taobao Frontend Technology
Taobao Frontend Technology
Nov 20, 2020 · Frontend Development

How AI Is Transforming Front‑End Development: Inside Alibaba’s imgcook Success

This article examines the evolution of AI‑driven code generation for front‑end development, detailing the imgcook platform’s technical principles, performance metrics, intelligent capability upgrades, and its impact on development efficiency and workflow during Alibaba’s 2020 Double‑11 campaign.

AI Code GenerationD2CUI to code
0 likes · 22 min read
How AI Is Transforming Front‑End Development: Inside Alibaba’s imgcook Success
DataFunTalk
DataFunTalk
Nov 19, 2020 · Artificial Intelligence

58 Tongzhen Home Feed Recommendation System: Architecture, Features, and Evolution

This talk details the design, data pipeline, feature engineering, model evolution, and operational insights of the 58 Tongzhen home feed recommendation system, covering its architecture, localization strategies, recall and ranking models, online learning, and future directions for AI-driven content delivery in the down‑market.

AIFeature EngineeringOnline Learning
0 likes · 34 min read
58 Tongzhen Home Feed Recommendation System: Architecture, Features, and Evolution
DataFunTalk
DataFunTalk
Nov 15, 2020 · Artificial Intelligence

Query Intent Recognition in Vertical Search: Challenges, Methods, and Case Studies

The article reviews the importance of query intent recognition in vertical search, outlines its definition, highlights practical challenges such as ambiguous input, multi‑intent queries, timeliness and cold‑start issues, and surveys common rule‑based, statistical, and machine‑learning solutions together with real‑world case studies.

NLUSearchcategory classification
0 likes · 17 min read
Query Intent Recognition in Vertical Search: Challenges, Methods, and Case Studies
DataFunTalk
DataFunTalk
Nov 11, 2020 · Artificial Intelligence

Cold-Start Optimization for Feed Ads: Algorithm Design and Experimental Evaluation

In this live talk, Dr. Zhang Renyu, an assistant professor at NYU Shanghai and economist at Kuaishou, presents his research on optimizing cold-start problems in feed advertising using a novel Shadow Bidding with Learning (SBL) algorithm, detailing its design, implementation, and experimental results.

Advertisingalgorithmcold-start
0 likes · 4 min read
Cold-Start Optimization for Feed Ads: Algorithm Design and Experimental Evaluation
DataFunSummit
DataFunSummit
Nov 8, 2020 · Artificial Intelligence

Architecture and Evolution of 58 Tongzhen Local Feed Recommendation System

This article details the design, data pipeline, feature engineering, model development, and iterative optimization of the 58 Tongzhen local feed recommendation system, covering business background, user profiling, recall strategies, ranking models such as XGBoost, XDeepFM, and online learning, and future directions.

AIFeature EngineeringOnline Learning
0 likes · 33 min read
Architecture and Evolution of 58 Tongzhen Local Feed Recommendation System
Amap Tech
Amap Tech
Nov 6, 2020 · Operations

Full-Link Load Testing Platform TestPG: Architecture, Corpus Production, and Intelligent Features

Gaode’s TestPG platform solves full‑link load‑testing bottlenecks by unifying traffic capture with Iflow, converting logs into standardized corpora via a Flink pipeline, and applying corpus‑intelligence that extracts seasonal feature statistics and predicts distributions for precise, feature‑level throttling, enabling faster, more reliable testing and future autonomous optimization.

FlinkPlatform EngineeringTraffic Capture
0 likes · 16 min read
Full-Link Load Testing Platform TestPG: Architecture, Corpus Production, and Intelligent Features
Tencent Advertising Technology
Tencent Advertising Technology
Nov 5, 2020 · Artificial Intelligence

Graph-based Evidence Aggregating and Reasoning (GEAR): A Graph Neural Network Approach to Fact Verification

The article introduces the GEAR model, a graph‑based evidence aggregation and reasoning framework that leverages BERT representations and graph neural networks to improve multi‑evidence fact verification, discusses its challenges, experimental gains on the FEVER dataset, and potential applications such as fake‑news detection and knowledge‑graph validation.

Evidence AggregationFact Verificationmachine learning
0 likes · 8 min read
Graph-based Evidence Aggregating and Reasoning (GEAR): A Graph Neural Network Approach to Fact Verification
Taobao Frontend Technology
Taobao Frontend Technology
Nov 4, 2020 · Frontend Development

How Front‑End AI is Transforming Development: From Design to Code

Front‑end intelligentization leverages AI and machine learning to empower developers with design‑to‑code automation, smart UI generation, and Pipcook integration, aiming to solve frontline engineers' pain points, boost productivity, and evolve the front‑end technology stack through deterministic, robust, and evolutionary approaches.

AIautomationdesign-to-code
0 likes · 14 min read
How Front‑End AI is Transforming Development: From Design to Code
DataFunTalk
DataFunTalk
Nov 4, 2020 · Artificial Intelligence

Intelligent E‑commerce Search: Architecture, Techniques, and Real‑World Impact

This article explores the evolution of e‑commerce search, detailing why search matters, the technical pipeline—including query preprocessing, entity and intent recognition, knowledge‑graph construction, recall, coarse and fine ranking—and demonstrates substantial performance gains through real‑world case studies.

AIKnowledge GraphSearch
0 likes · 16 min read
Intelligent E‑commerce Search: Architecture, Techniques, and Real‑World Impact
Programmer DD
Programmer DD
Nov 3, 2020 · Artificial Intelligence

Can Your Cough Reveal COVID‑19? AI Model Detects Asymptomatic Infections via Smartphone

A new MIT‑developed artificial‑intelligence system analyzes cough recordings captured on a phone or computer to identify COVID‑19 infections, achieving 98.5% accuracy and 100% detection of asymptomatic cases, offering a potential free, non‑invasive screening tool pending regulatory approval.

Artificial IntelligenceCOVID-19cough detection
0 likes · 4 min read
Can Your Cough Reveal COVID‑19? AI Model Detects Asymptomatic Infections via Smartphone
DataFunTalk
DataFunTalk
Oct 30, 2020 · Artificial Intelligence

Evolution of Display Advertising Effect Optimization at 360: System Architecture, Smart Bidding, and Model Advances

This article details the end‑to‑end evolution of 360's display advertising optimization, covering business flow, common ad formats, system architecture, CPC settings, traffic layering, smart bidding, creative combination, model progression from simple to deep learning, multi‑task learning, and latency reduction techniques.

Ad TechModel EvolutionOCPC
0 likes · 13 min read
Evolution of Display Advertising Effect Optimization at 360: System Architecture, Smart Bidding, and Model Advances
DataFunTalk
DataFunTalk
Oct 28, 2020 · Artificial Intelligence

All-Rounder Recall Representation Algorithm Practice

This article presents a comprehensive overview of NetEase Yanxuan’s recall representation algorithms, detailing problem definition, model value, iterative implementations—including session-based embedding, GCN, GraphSAGE, LightGCN, and multi-interest models—along with engineering solutions, performance comparisons, and real-world deployment outcomes in search and recommendation systems.

EmbeddingGraph Neural NetworkSearch
0 likes · 16 min read
All-Rounder Recall Representation Algorithm Practice
Hulu Beijing
Hulu Beijing
Oct 26, 2020 · Artificial Intelligence

Hulu’s AI Innovations: Graph Neural Networks, Ad Targeting & Content Embeddings

The Hulu AI Class event showcased a series of technical talks covering large‑scale graph neural network optimizations, multi‑factor video ad placement algorithms, recommendation and search engine techniques, machine‑learning‑driven video codec improvements, and advanced content‑embedding methods, highlighting practical engineering experiences from Hulu’s Beijing office.

Ad TargetingRecommendation Systemscontent embedding
0 likes · 9 min read
Hulu’s AI Innovations: Graph Neural Networks, Ad Targeting & Content Embeddings
DataFunTalk
DataFunTalk
Oct 20, 2020 · Artificial Intelligence

From Biological Neurons to Artificial Neural Networks: Perceptrons, Multilayer Perceptrons, and Backpropagation

This article traces the evolution of artificial neural networks from their biological inspiration, explains the McCulloch‑Pitts neuron model, details perceptron architecture and learning rule with a Scikit‑Learn example, and introduces multilayer perceptrons and the back‑propagation algorithm together with common activation functions.

AIBackpropagationDeep Learning
0 likes · 19 min read
From Biological Neurons to Artificial Neural Networks: Perceptrons, Multilayer Perceptrons, and Backpropagation
Meituan Technology Team
Meituan Technology Team
Oct 15, 2020 · Artificial Intelligence

AIOps at Meituan: Architecture and Practice of Time‑Series Anomaly Detection (Part 1)

Meituan’s AIOps initiative replaces manual rule‑based monitoring with the Horae platform, which automatically classifies time‑series metrics, applies CNN and XGBoost models to detect periodic anomalies, achieves over 90 % precision in production, and paves the way for broader metric types, forecasting, and advanced fault‑localization.

HoraeMeituanOperations
0 likes · 33 min read
AIOps at Meituan: Architecture and Practice of Time‑Series Anomaly Detection (Part 1)
Laravel Tech Community
Laravel Tech Community
Oct 14, 2020 · Fundamentals

Ten Fundamental Algorithms: Sorting, Searching, Graph Traversal, and More

This article introduces ten essential algorithms—including Quick Sort, Heap Sort, Merge Sort, Binary Search, BFPRT, Depth‑First Search, Breadth‑First Search, Dijkstra's shortest‑path, Dynamic Programming, and Naive Bayes—explaining their principles, typical use cases, and step‑by‑step procedures.

AlgorithmsSearchSorting
0 likes · 12 min read
Ten Fundamental Algorithms: Sorting, Searching, Graph Traversal, and More
DataFunTalk
DataFunTalk
Oct 1, 2020 · Artificial Intelligence

Building and Applying a Vector System for Search and Recommendation at NetEase Yanxuan

This article describes how NetEase Yanxuan has designed, trained, and deployed a unified vector representation system to power various e‑commerce search and recommendation scenarios, covering model choices, incremental learning strategies, large‑scale similarity computation, and practical lessons from real‑world deployments.

e‑commercelarge-scale similaritymachine learning
0 likes · 18 min read
Building and Applying a Vector System for Search and Recommendation at NetEase Yanxuan
DataFunTalk
DataFunTalk
Sep 29, 2020 · Artificial Intelligence

Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems

This article introduces the Deep Sparse Network (NON), a new deep neural architecture for recommendation systems that combines field‑wise networks, across‑field interaction networks, and an operation‑fusion network, and demonstrates its superior performance through extensive experiments and ablation studies.

CTR predictionDeep Learningfeature interaction
0 likes · 14 min read
Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems
Tencent Advertising Technology
Tencent Advertising Technology
Sep 29, 2020 · Artificial Intelligence

The Power of Data and AI: Highlights from the 2020 Tencent Advertising Algorithm Live Week

The 2020 Tencent Advertising Algorithm Live Week presented expert insights on federated learning, machine learning, big data, and deep‑learning applications in advertising, offering a comprehensive Q&A that explains how massive data fuels AI breakthroughs and reshapes business problem solving.

Big Datamachine learning
0 likes · 11 min read
The Power of Data and AI: Highlights from the 2020 Tencent Advertising Algorithm Live Week
JD Cloud Developers
JD Cloud Developers
Sep 28, 2020 · Artificial Intelligence

Cloud Revenue Soars 20% & AI Models Challenge BERT: This Week’s Tech Highlights

This weekly roundup covers a 20% surge in cloud service revenue, Google's new pQRNN model rivaling BERT, China's first NFC chip stamp, TSMC's 3nm capacity boost, Intel's IoT‑focused processors, Apple's open‑source Swift System for Linux, a novel FGPM adversarial text method, and multimodal translation advances, highlighting key trends across cloud, AI, and hardware.

Cloud ComputingNFCmachine learning
0 likes · 7 min read
Cloud Revenue Soars 20% & AI Models Challenge BERT: This Week’s Tech Highlights
JD Retail Technology
JD Retail Technology
Sep 28, 2020 · Artificial Intelligence

Why AI Testing Is Still Painful and How to Solve It

The talk explores the current pain points of AI testing, outlines data‑quality analysis methods, highlights critical ETL and model‑testing considerations, and shares practical case studies and platform designs to improve machine‑learning quality assurance.

AI testingData QualityETL
0 likes · 5 min read
Why AI Testing Is Still Painful and How to Solve It
DataFunTalk
DataFunTalk
Sep 26, 2020 · Artificial Intelligence

What Makes a Good Model? Understanding Model Concepts, Types, and Evaluation in Data Science

This article explores the definition of a model, distinguishes business, data, and function models, discusses criteria for a good model—including performance, fidelity to real‑world relationships, and interpretability—and examines why a universal model does not exist, all within the context of data science and AI.

AIInterpretabilitydata-science
0 likes · 18 min read
What Makes a Good Model? Understanding Model Concepts, Types, and Evaluation in Data Science
Beike Product & Technology
Beike Product & Technology
Sep 26, 2020 · Artificial Intelligence

Uplift Modeling for Intelligent Marketing: Concepts, Methods, Evaluation, and Business Applications

This article introduces uplift (incremental) modeling as a causal inference technique for intelligent marketing, explains its mathematical formulation, compares response and uplift models, describes various modeling approaches such as two‑model, one‑model, and label‑transformation methods, outlines evaluation metrics like Qini and AUUC, and demonstrates practical deployment in a real‑world real‑estate platform.

A/B testingMarketing AnalyticsQini curve
0 likes · 21 min read
Uplift Modeling for Intelligent Marketing: Concepts, Methods, Evaluation, and Business Applications
Tencent Cloud Developer
Tencent Cloud Developer
Sep 25, 2020 · Artificial Intelligence

Privacy-Preserving Federated Learning for Financial Risk Control Using Homomorphic Encryption

Tencent Shield‑Federated Computing enables banks to jointly train Gradient Boosted Decision Trees and Logistic Regression with external data owners by using homomorphic encryption to perform encrypted variable and split‑point searches, gradient aggregation, and model updates, delivering near‑centralized accuracy, up to 70 % speed gains, and full data confidentiality for financial risk control.

Gradient Boosted TreesHomomorphic Encryptionfederated learning
0 likes · 15 min read
Privacy-Preserving Federated Learning for Financial Risk Control Using Homomorphic Encryption
Yanxuan Tech Team
Yanxuan Tech Team
Sep 25, 2020 · Artificial Intelligence

How Vector Embeddings Power E‑Commerce Search and Recommendation at NetEase Yanxuan

This article explains how Yanxuan built a comprehensive vector system—from product embeddings and graph models to large‑scale similarity computation—and applied it across search, recommendation, and purchase prediction tasks, highlighting practical algorithms, infrastructure, and future directions.

e-commerce recommendationmachine learningsearch ranking
0 likes · 18 min read
How Vector Embeddings Power E‑Commerce Search and Recommendation at NetEase Yanxuan
JD Tech Talk
JD Tech Talk
Sep 23, 2020 · Artificial Intelligence

Delivery Time Inference Based on Couriers' Trajectories

Leveraging large-scale courier trajectory data and spatiotemporal analytics, the DTInf framework infers parcel delivery times by detecting stay points, correcting delivery locations, and matching delivery events using a trained MLP model, achieving a mean absolute error of 401 seconds and outperforming baselines by over 30%.

Big DataLogisticscourier trajectories
0 likes · 10 min read
Delivery Time Inference Based on Couriers' Trajectories
Tencent Advertising Technology
Tencent Advertising Technology
Sep 22, 2020 · Artificial Intelligence

Automated Machine Learning: Challenges, Techniques, and the SolnML System – Q&A Highlights from the 2020 Tencent Advertising Algorithm Competition Live Series

This article summarizes the Q&A session of the 2020 Tencent Advertising Algorithm Competition live series, covering the fundamentals of automated machine learning, its key technologies, current challenges, and the features and advantages of the SolnML system, while also addressing practical concerns such as hardware support and future research directions.

AIAutoMLMeta Learning
0 likes · 13 min read
Automated Machine Learning: Challenges, Techniques, and the SolnML System – Q&A Highlights from the 2020 Tencent Advertising Algorithm Competition Live Series
DataFunTalk
DataFunTalk
Sep 22, 2020 · Artificial Intelligence

User-Based Collaborative Filtering with Python: A Step-by-Step Guide

This article explains how to implement a user‑based collaborative filtering recommendation system in Python, covering data loading, preprocessing, cosine‑similarity computation, neighbor selection, rating prediction, and generating top‑5 movie recommendations with detailed code examples.

Cosine SimilarityPythoncollaborative filtering
0 likes · 12 min read
User-Based Collaborative Filtering with Python: A Step-by-Step Guide
DataFunTalk
DataFunTalk
Sep 21, 2020 · Artificial Intelligence

Data‑Driven Synonym Transformation for Keyword Matching in Search Advertising

This article explains how keyword matching in search advertising works, outlines the challenges of semantic gaps, matching‑mode determination and scalability, and describes data‑driven synonym transformation techniques—including rule‑based, sequence‑to‑sequence, metric‑space and graph‑based models—to improve recall, efficiency, and robustness.

Ad Techkeyword matchingmachine learning
0 likes · 18 min read
Data‑Driven Synonym Transformation for Keyword Matching in Search Advertising
360 Quality & Efficiency
360 Quality & Efficiency
Sep 18, 2020 · Artificial Intelligence

Data Augmentation Techniques for Improving Object Detection Model Robustness

To enhance object detection robustness, the article discusses various data augmentation methods—including rotation, flipping, random cropping, scaling, color jitter, blurring, transparency adjustment, and image partitioning—providing code examples and illustrating their impact on model performance with before‑and‑after results.

Pythoncomputer visiondata augmentation
0 likes · 7 min read
Data Augmentation Techniques for Improving Object Detection Model Robustness
TAL Education Technology
TAL Education Technology
Sep 17, 2020 · Artificial Intelligence

Comprehensive Guide to Feature Engineering and Data Preprocessing for Machine Learning

This article provides an extensive overview of feature engineering, covering feature understanding, cleaning, construction, selection, transformation, and dimensionality reduction techniques, illustrated with Python code using the Titanic dataset, and offers practical guidelines for improving data quality and model performance in machine learning projects.

Feature EngineeringPythonTitanic dataset
0 likes · 44 min read
Comprehensive Guide to Feature Engineering and Data Preprocessing for Machine Learning
JD Tech Talk
JD Tech Talk
Sep 17, 2020 · Artificial Intelligence

Design and Implementation of a High‑Availability Distributed Machine Learning Model Online Inference System

This article presents a comprehensive technical solution for a distributed online inference system that packages machine‑learning models in Docker containers, orchestrates them with Kubernetes for fault‑tolerant, elastic scaling, and integrates model repositories, image registries, monitoring, and automated model selection to streamline deployment, updates, and resource management.

AIDockerModel Serving
0 likes · 15 min read
Design and Implementation of a High‑Availability Distributed Machine Learning Model Online Inference System
Tencent Advertising Technology
Tencent Advertising Technology
Sep 16, 2020 · Artificial Intelligence

Federated Learning for Advertising and Recommendation: Key Insights and Q&A

This article summarizes the 2020 Tencent Advertising Algorithm Competition live‑stream series, presenting expert explanations of federated learning, its technical background, typical use cases, privacy and security mechanisms, and answers to common questions about applying federated learning to digital marketing.

AIdigital marketingmachine learning
0 likes · 11 min read
Federated Learning for Advertising and Recommendation: Key Insights and Q&A
DataFunTalk
DataFunTalk
Sep 14, 2020 · Artificial Intelligence

New Generation Rank Technology: Search‑Based Interest Model (SIM) and Dynamic Computation Allocation Framework (DCAF) for Alibaba Directed Advertising

This article presents Alibaba's latest ranking innovations for directed e‑commerce advertising, detailing the challenges of long‑term user interest modeling, the Search‑Based Interest Model (SIM) that extends behavior sequences to ten thousand actions, and the Dynamic Computation Allocation Framework (DCAF) that optimizes per‑request compute resources to maximize system revenue.

AdvertisingCTR modeldynamic computation allocation
0 likes · 29 min read
New Generation Rank Technology: Search‑Based Interest Model (SIM) and Dynamic Computation Allocation Framework (DCAF) for Alibaba Directed Advertising
21CTO
21CTO
Sep 12, 2020 · Fundamentals

TikTok Algorithm, VS Code Spaces Shift, Debian Talent Gap, Laravel 8 Highlights

This article summarizes recent tech news, covering TikTok's AI‑driven recommendation system, U.S. pressure on ByteDance, Microsoft's migration of Visual Studio Codespaces to GitHub Codespaces, Debian's developer shortage despite strong funding, and the new features introduced in Laravel 8.

DebianLaravel 8TikTok
0 likes · 8 min read
TikTok Algorithm, VS Code Spaces Shift, Debian Talent Gap, Laravel 8 Highlights
MaGe Linux Operations
MaGe Linux Operations
Sep 12, 2020 · Artificial Intelligence

10 Essential Machine Learning Engineering Tips Every Data Scientist Should Know

This article shares ten practical Python‑focused machine‑learning engineering tips—from writing abstract classes and fixing random seeds to tracking progress, speeding up pandas, managing cloud costs, and building robust FastAPI services—helping developers write cleaner, more reproducible, and production‑ready code.

API developmentAbstract ClassesBest Practices
0 likes · 13 min read
10 Essential Machine Learning Engineering Tips Every Data Scientist Should Know
Programmer DD
Programmer DD
Sep 10, 2020 · Artificial Intelligence

Can You Predict Speed‑Dating Success? A Data‑Driven Exploration

This article walks through loading the Speed Dating dataset, examining its features and missing values, visualizing match rates by gender and age, performing correlation analysis, and building a logistic regression model with SMOTE oversampling to predict whether a pair will successfully match.

Data AnalysisPythonimbalanced data
0 likes · 11 min read
Can You Predict Speed‑Dating Success? A Data‑Driven Exploration
MaGe Linux Operations
MaGe Linux Operations
Sep 9, 2020 · Artificial Intelligence

Master Machine Learning Basics: Concepts, Types, Algorithms & K‑NN Walkthrough

This comprehensive tutorial introduces machine learning fundamentals, its history, differences from traditional programming, key characteristics, and why Python is the preferred language, then explores supervised, unsupervised, and reinforcement learning, popular algorithms, detailed K‑Nearest Neighbors examples for classification and regression, and the essential steps to build and evaluate models.

PythonUnsupervised LearningkNN
0 likes · 21 min read
Master Machine Learning Basics: Concepts, Types, Algorithms & K‑NN Walkthrough
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 9, 2020 · Artificial Intelligence

Can You Predict Speed‑Dating Success? A Data‑Driven AI Analysis

This article explores the classic Speed Dating dataset, performing data cleaning, exploratory analysis of match rates, gender and age effects, correlation studies, and finally building a logistic regression model with SVMSMOTE oversampling to predict matchmaking success, achieving around 83% accuracy.

Data AnalysisPythonSVMSMOTE
0 likes · 11 min read
Can You Predict Speed‑Dating Success? A Data‑Driven AI Analysis
DataFunTalk
DataFunTalk
Sep 4, 2020 · Artificial Intelligence

Beam Search Aware Training for Optimal Tree-Based Retrieval Models

This article presents a comprehensive study of tree-based deep models for large-scale matching, introduces the theoretical framework of optimal tree models, proposes a Beam Search aware training algorithm (BSAT/OTM) to address training-test mismatch, and demonstrates significant recall improvements on Amazon Books and UserBehavior datasets.

Beam SearchDeep Learninglarge-scale matching
0 likes · 23 min read
Beam Search Aware Training for Optimal Tree-Based Retrieval Models
Programmer DD
Programmer DD
Sep 4, 2020 · Artificial Intelligence

How to Build a Java Spring Boot License Plate Recognition System with OpenCV

An open-source Java Spring Boot project demonstrates license plate detection and recognition using OpenCV, supporting multiple plate colors, with SVM and ANN training, detailed architecture, feature list, installation guide, and visual processing steps, offering a beginner-friendly tutorial for image recognition enthusiasts.

OpenCVSpring Bootimage processing
0 likes · 7 min read
How to Build a Java Spring Boot License Plate Recognition System with OpenCV
DataFunTalk
DataFunTalk
Sep 2, 2020 · Artificial Intelligence

CSCNN: Category‑Specific Convolutional Neural Network for Visual CTR Prediction in JD E‑commerce Advertising

This article presents CSCNN, a category‑specific convolutional neural network that integrates visual priors into click‑through‑rate (CTR) models for JD.com’s e‑commerce advertising, detailing its motivation, architecture, engineering optimizations, offline and online training strategies, and empirical performance gains on both public and industrial datasets.

CTR predictionDeep Learningcategory-specific CNN
0 likes · 19 min read
CSCNN: Category‑Specific Convolutional Neural Network for Visual CTR Prediction in JD E‑commerce Advertising
58 Tech
58 Tech
Aug 31, 2020 · Artificial Intelligence

Deep Learning Practices for Commercial CTR Prediction at 58.com

This article details the end‑to‑end deep‑learning workflow for click‑through‑rate (CTR) prediction in 58.com’s commercial ranking system, covering system architecture, feature engineering, sample construction, model evolution from Wide&Deep to DIN/DIEN, and engineering optimizations that together yielded significant CPM and CVR improvements.

AdvertisingCTR predictionDeep Learning
0 likes · 38 min read
Deep Learning Practices for Commercial CTR Prediction at 58.com
Python Crawling & Data Mining
Python Crawling & Data Mining
Aug 31, 2020 · Fundamentals

20 Essential NumPy Challenges with Complete Solutions

This article presents twenty classic NumPy problems covering array lookup, modification, conversion, sampling, slicing, string operations, rounding, reshaping, linear algebra, and more, each accompanied by concise Python code examples and visual illustrations to help you master advanced data manipulation techniques.

Array OperationsPythonScientific Computing
0 likes · 13 min read
20 Essential NumPy Challenges with Complete Solutions
DataFunTalk
DataFunTalk
Aug 27, 2020 · Artificial Intelligence

Computational Advertising vs Recommendation Systems: Key Differences and Popular Models

This article explains the fundamental differences between computational advertising and recommendation systems, outlines the distinct problems each field addresses, and surveys the most widely used advertising models—including traditional machine‑learning approaches, deep‑learning architectures, and hybrid solutions—providing practical insights for engineers in both domains.

AICTR modelsDeep Learning
0 likes · 11 min read
Computational Advertising vs Recommendation Systems: Key Differences and Popular Models
Tencent Tech
Tencent Tech
Aug 26, 2020 · Artificial Intelligence

How Tencent Engineers Shattered the 128‑GPU ImageNet Training Record in 2m31s

Tencent engineers broke the world record for training ImageNet with 128 V100 GPUs in just 2 minutes 31 seconds, detailing a suite of optimizations—including a new Light distributed training framework, single‑machine speed boosts, multi‑machine communication enhancements, and advanced batch convergence techniques—that together dramatically cut training time while maintaining high accuracy.

GPUImageNetTencent Cloud
0 likes · 9 min read
How Tencent Engineers Shattered the 128‑GPU ImageNet Training Record in 2m31s
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 25, 2020 · Artificial Intelligence

Build a Free Cloud AI Speed‑Dating Model with Alibaba PAI‑DSW

This article introduces Alibaba Cloud’s free PAI‑DSW cloud IDE for AI development, explains the evolution of machine learning, guides users through creating notebooks, running Python code, and demonstrates a complete speed‑dating dataset analysis and predictive modeling pipeline using logistic regression and data‑balancing techniques.

AICloud IDEPython
0 likes · 21 min read
Build a Free Cloud AI Speed‑Dating Model with Alibaba PAI‑DSW
DataFunTalk
DataFunTalk
Aug 23, 2020 · Artificial Intelligence

Optimizing Recall in Travel Recommendation Systems: Challenges and Solutions at Alibaba's Fliggy

This article explains how Fliggy's travel recommendation platform tackles recall challenges such as cold‑start users, sparse behavior, itinerary‑specific needs, and periodic repurchase by applying user‑attribute models, graph embeddings, dual‑tower architectures, session‑based methods, and statistical repurchase forecasting to improve candidate selection and overall recommendation performance.

Travelcold startgraph embedding
0 likes · 16 min read
Optimizing Recall in Travel Recommendation Systems: Challenges and Solutions at Alibaba's Fliggy
JD Tech Talk
JD Tech Talk
Aug 21, 2020 · Artificial Intelligence

JD Digits' Intelligent Anti‑Fraud Platform: AI‑Driven Real‑Time Fraud Detection and Knowledge‑Graph Solutions

JD Digits' intelligent anti‑fraud platform leverages machine learning, big‑data processing, graph neural networks and small‑sample knowledge‑graph algorithms to provide millisecond‑level, real‑time protection across 600+ scenarios, while also offering AI‑powered solutions to banks and publishing research at top conferences.

AIGraph Neural NetworkKnowledge Graph
0 likes · 6 min read
JD Digits' Intelligent Anti‑Fraud Platform: AI‑Driven Real‑Time Fraud Detection and Knowledge‑Graph Solutions
DataFunTalk
DataFunTalk
Aug 20, 2020 · Artificial Intelligence

Weibo Recommendation Algorithm Practice and Machine Learning Platform Evolution

This article shares Weibo’s experience in building and evolving its recommendation algorithms, covering the recommendation scenario, machine learning workflow, feature engineering, model upgrades, large‑scale challenges, deployment via the Weiflow platform, and the capabilities of its machine‑learning infrastructure.

Feature EngineeringOnline LearningWeibo
0 likes · 14 min read
Weibo Recommendation Algorithm Practice and Machine Learning Platform Evolution
Meituan Technology Team
Meituan Technology Team
Aug 20, 2020 · Artificial Intelligence

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

The winning KDD Cup 2020 debiasing solution builds a heterogeneous item‑to‑item graph with click‑co‑occurrence and multimodal similarity edges, uses multi‑hop random walks to generate unbiased candidate samples, trains LightGBM with a popularity‑weighted loss, and aggregates scores to lift low‑popularity items, thereby eliminating selection and popularity bias and achieving first place among 1,895 teams.

AdvertisingGraph ModelingKDD Cup
0 likes · 23 min read
Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Aug 19, 2020 · Artificial Intelligence

A Four‑Year Stanford AI Curriculum Guide Compiled by Mihail Eric

This article presents a detailed four‑year study plan for aspiring artificial‑intelligence professionals, curated by Stanford graduate Mihail Eric, listing foundational, intermediate and advanced courses—including CS 106B, CS 107, CS 221, CS 229, CS 231N and more—along with practical advice for projects, research and internships.

AICoursesCurriculum
0 likes · 11 min read
A Four‑Year Stanford AI Curriculum Guide Compiled by Mihail Eric
MaGe Linux Operations
MaGe Linux Operations
Aug 18, 2020 · Artificial Intelligence

Understanding node2vec: Biased Random Walks for Graph Embedding

This article explains the node2vec algorithm, its mathematical foundations, biased random‑walk sampling strategy with parameters p and q, implementation details using the Alias method, and demonstrates its superior performance on node classification and visualization tasks compared with DeepWalk and LINE.

Pythongraph embeddingmachine learning
0 likes · 9 min read
Understanding node2vec: Biased Random Walks for Graph Embedding
DataFunTalk
DataFunTalk
Aug 18, 2020 · Artificial Intelligence

COLD: A Next‑Generation Pre‑Ranking System for Online Advertising

The article introduces COLD, a computing‑power‑aware online and lightweight deep pre‑ranking system for Alibaba's targeted ads, detailing its evolution from static CTR models to vector‑inner‑product models, its flexible network architecture with feature‑selection via SE blocks, engineering optimizations such as parallelism, column‑wise computation, Float16 and MPS, and demonstrates superior offline and online performance through extensive experiments.

COLDfeature selectionmachine learning
0 likes · 11 min read
COLD: A Next‑Generation Pre‑Ranking System for Online Advertising
DataFunTalk
DataFunTalk
Aug 14, 2020 · Artificial Intelligence

Illustrated Guide to the Complete Machine Learning Workflow

This article presents a hand‑drawn, illustrated walkthrough of the entire machine‑learning pipeline—from dataset definition, exploratory data analysis, preprocessing, and data splitting to model building, algorithm selection, hyper‑parameter tuning, feature selection, and evaluation for both classification and regression tasks.

classificationcross-validationdata preprocessing
0 likes · 17 min read
Illustrated Guide to the Complete Machine Learning Workflow
Architects Research Society
Architects Research Society
Aug 9, 2020 · Artificial Intelligence

Roadmap to Becoming a Machine Learning Engineer by 2020

This article presents a set of charts outlining various learning paths and technologies for aspiring machine‑learning engineers, offering guidance on what to study and why, while also providing community resources for deeper discussion and support.

2020AI EngineeringLearning Path
0 likes · 5 min read
Roadmap to Becoming a Machine Learning Engineer by 2020
MaGe Linux Operations
MaGe Linux Operations
Aug 6, 2020 · Artificial Intelligence

Build a Python Chatbot with ChatterBot: Step‑by‑Step Guide

This article introduces chatbots, explains rule‑based and self‑learning types, describes how AI‑driven bots work, and provides a complete Python tutorial using the ChatterBot library—including environment setup, installation, and full source code for a functional chatbot.

ChatbotChatterBotNLP
0 likes · 4 min read
Build a Python Chatbot with ChatterBot: Step‑by‑Step Guide