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Machine Learning

1959 articles · Page 5 of 20
DaTaobao Tech
DaTaobao Tech
Aug 19, 2024 · Frontend Development

Challenges and Solutions in AI-Powered Front-End Code Generation for B2C Platforms

The article details how Taobao’s AI team automated repetitive UI tasks for B2C front‑end development, achieving a 15 % efficiency gain across five projects, and outlines key challenges—prompt cost, low OCR accuracy, hallucinations, excess nodes, and customization variance—along with practical solutions such as a dedicated evaluation platform, OCR translation, model upgrades, prompt segmentation, output simplification, and a reusable component library.

AIMachine LearningPrompt Engineering
0 likes · 9 min read
Challenges and Solutions in AI-Powered Front-End Code Generation for B2C Platforms
DataFunSummit
DataFunSummit
Aug 18, 2024 · Artificial Intelligence

Challenges and Solutions in Recommendation AB Testing on Xiaohongshu's Experiment Platform

The article examines the key challenges of recommendation AB testing at Xiaohongshu—including change stability, single‑experiment precision, and multi‑strategy packaging—and presents a series of engineering and statistical solutions such as SDK‑based AB architecture, virtual PreAA experiments, CUPED/DID adjustments, and reverse experiments to improve reliability and metric impact.

AB testingCUPEDExperiment Platform
0 likes · 15 min read
Challenges and Solutions in Recommendation AB Testing on Xiaohongshu's Experiment Platform
21CTO
21CTO
Aug 17, 2024 · Artificial Intelligence

Understanding Large Language Models: Training, Uses, and a Llama 3 Code Demo

This article explains what large language models (LLMs) are, how they are trained, their diverse applications across industries, the challenges they face, and provides a practical Python example using Replicate to run Meta's Llama 3‑70b‑instruct model.

AILLMMachine Learning
0 likes · 11 min read
Understanding Large Language Models: Training, Uses, and a Llama 3 Code Demo
Hacker Afternoon Tea
Hacker Afternoon Tea
Aug 15, 2024 · Artificial Intelligence

Open-Source Binance AI Trading Bot: ML & Feature Engineering for Automated Crypto Signals

This article details an open‑source Binance trading bot that uses machine‑learning models and extensive feature‑engineering pipelines to generate and broadcast automated cryptocurrency buy‑sell signals via a Telegram channel, covering data acquisition, offline training, hyper‑parameter tuning, and live service deployment.

BinanceMachine LearningPython
0 likes · 18 min read
Open-Source Binance AI Trading Bot: ML & Feature Engineering for Automated Crypto Signals
Baidu Geek Talk
Baidu Geek Talk
Aug 14, 2024 · Artificial Intelligence

Sparse Tensor Basics in PaddlePaddle

The article explains how to use PaddlePaddle’s sparse computing features—including basic sparse tensor formats, creation and manipulation of sparse tensors, and building and training sparse neural networks such as a sparse ResNet—to improve memory efficiency and accelerate training on large, zero‑rich datasets.

AICOO FormatCSR Format
0 likes · 22 min read
Sparse Tensor Basics in PaddlePaddle
Python Programming Learning Circle
Python Programming Learning Circle
Aug 12, 2024 · Artificial Intelligence

Common Python Libraries for Computer Vision Projects

This article introduces and compares ten widely used Python libraries for computer vision, including Pillow, OpenCV, Mahotas, Scikit‑Image, TensorFlow Image, PyTorch Vision, SimpleCV, Imageio, Albumentations, and timm, highlighting their features, typical use cases, and providing code examples for each.

LibrariesMachine LearningOpenCV
0 likes · 10 min read
Common Python Libraries for Computer Vision Projects
DataFunTalk
DataFunTalk
Aug 9, 2024 · Artificial Intelligence

Modeling User Propagation Ability for Social Recommendation and Influence Maximization in Games

This article presents a comprehensive study on leveraging user propagation ability metrics for friend recommendation and influence maximization in gaming environments, introducing a conversion‑funnel‑aware diffusion model, novel influence‑maximization variants, efficient greedy algorithms, and extensive offline and online experiments that demonstrate significant performance gains over traditional methods.

GamingGraph AlgorithmsMachine Learning
0 likes · 16 min read
Modeling User Propagation Ability for Social Recommendation and Influence Maximization in Games
Meituan Technology Team
Meituan Technology Team
Aug 8, 2024 · Artificial Intelligence

BlackPearl Team Wins All Three Tracks of KDD 2024 OAG‑Challenge Cup with Large‑Model Solutions

The BlackPearl team from Meituan’s Dazhong Dianping division swept all three KDD 2024 OAG‑Challenge Cup tracks—WhoIsWho, PST, and AQA—by deploying innovative large‑model techniques such as iterative text clustering, graft‑learning‑enhanced BERT RAG pipelines, and a Boosting LLM‑for‑Vector search, and have released the code publicly on GitHub.

Academic DisambiguationKDD CupMachine Learning
0 likes · 4 min read
BlackPearl Team Wins All Three Tracks of KDD 2024 OAG‑Challenge Cup with Large‑Model Solutions
Baidu Geek Talk
Baidu Geek Talk
Aug 7, 2024 · Artificial Intelligence

Detecting Time‑Series Anomalies in Embedding Space: A Practical AI Approach

This article presents an embedding‑based method for time‑series anomaly detection in security and anti‑cheat scenarios, explains how to vectorise logs, sample and compute distribution features, details implementation code, and validates the approach with four synthetic experiments showing precision‑recall improvements at day and hour granularity.

ClusteringMachine Learninganomaly detection
0 likes · 12 min read
Detecting Time‑Series Anomalies in Embedding Space: A Practical AI Approach
DataFunTalk
DataFunTalk
Aug 7, 2024 · Artificial Intelligence

Multi-Scenario Modeling for NetEase Cloud Music Recommendation: Architecture, Challenges, and Results

This article presents NetEase Cloud Music's multi‑scenario recommendation modeling work, detailing background, overall system architecture, key modules, modeling goals, technical difficulties, performance improvements, future outlook, and a comprehensive Q&A session that addresses practical deployment challenges.

AB testingAIMachine Learning
0 likes · 14 min read
Multi-Scenario Modeling for NetEase Cloud Music Recommendation: Architecture, Challenges, and Results
Open Source Linux
Open Source Linux
Aug 6, 2024 · Artificial Intelligence

What Is AI? A Beginner’s Guide to Definitions, Types, and Real‑World Impact

This article explains what artificial intelligence (AI) is, how it differs from traditional programming, outlines its main categories, introduces machine learning, deep learning, neural network models such as CNN, RNN, and Transformer, describes large models and GPT, and discusses AI’s wide‑range applications and societal implications.

AIAI ApplicationsArtificial Intelligence
0 likes · 16 min read
What Is AI? A Beginner’s Guide to Definitions, Types, and Real‑World Impact
Python Programming Learning Circle
Python Programming Learning Circle
Jul 27, 2024 · Artificial Intelligence

Numpy‑ML: A Pure NumPy Implementation of Machine Learning Algorithms

The Numpy‑ML project, created by UC Berkeley’s David Bourgin, provides a comprehensive pure‑NumPy implementation of over 30 machine‑learning algorithms—including probabilistic models, neural‑network layers, optimizers, and reinforcement‑learning agents—along with extensive data‑preprocessing utilities, all in a single open‑source repository.

AIAlgorithmsMachine Learning
0 likes · 6 min read
Numpy‑ML: A Pure NumPy Implementation of Machine Learning Algorithms
iQIYI Technical Product Team
iQIYI Technical Product Team
Jul 26, 2024 · Artificial Intelligence

Optimizing Advertising Feature Evaluation Process with the Opal Machine Learning Platform

By migrating iQIYI’s advertising feature‑evaluation workflow to the Opal machine‑learning platform, the team replaced a manual, engineer‑heavy process with a unified, automated pipeline that cut evaluation cycles from five days to 1.5 days, tripling iteration speed while lowering barriers and improving consistency for future feature optimization.

Feature EvaluationMachine LearningModel Optimization
0 likes · 6 min read
Optimizing Advertising Feature Evaluation Process with the Opal Machine Learning Platform
Meituan Technology Team
Meituan Technology Team
Jul 25, 2024 · Artificial Intelligence

Selected Meituan Papers Accepted at KDD 2024: Summaries of Five Long Papers

Meituan’s five long papers accepted at KDD 2024 introduce a dual‑intent model for search‑recommendation, a joint auction mechanism for ads, a robust ATE estimator for heavy‑tailed metrics, a decision‑focused causal learning framework for marketing, and an efficient on‑demand order‑pooling system for real‑time courier assignments.

Controlled ExperimentsKDD 2024Machine Learning
0 likes · 12 min read
Selected Meituan Papers Accepted at KDD 2024: Summaries of Five Long Papers
DataFunSummit
DataFunSummit
Jul 25, 2024 · Artificial Intelligence

LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control

This article presents the latest advances from the Chinese Academy of Sciences in graph machine learning for user behavior risk control, introducing the LOGIN framework that leverages large language models as consultants to iteratively enhance GNN training, and demonstrates its effectiveness through extensive experiments on homogeneous and heterogeneous graph benchmarks.

Large Language ModelsMachine LearningUser Behavior
0 likes · 14 min read
LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control
Baidu Tech Salon
Baidu Tech Salon
Jul 23, 2024 · Artificial Intelligence

Linear Algebra Fundamentals and PaddlePaddle Applications

The article reviews core linear algebra concepts—vectors, matrices, eigenvalues, and transformations—and demonstrates how PaddlePaddle’s paddle.linalg API enables practical tasks such as least‑squares regression, image compression via SVD, PCA‑based dimensionality reduction, and broader machine‑learning, graphics, cryptography, and optimization applications.

Machine LearningPCAPaddlePaddle
0 likes · 10 min read
Linear Algebra Fundamentals and PaddlePaddle Applications
21CTO
21CTO
Jul 23, 2024 · Artificial Intelligence

What Is Agentic AI? How Autonomous Agents Boost Productivity and Transform Industries

Agentic AI, also known as autonomous AI agents, enables systems to perceive environments, make decisions, act, and continuously learn, offering higher productivity, smarter decision‑making, and industry‑wide transformation across sectors such as customer service, healthcare, finance, and manufacturing.

AI automationAI frameworksAutonomous Agents
0 likes · 13 min read
What Is Agentic AI? How Autonomous Agents Boost Productivity and Transform Industries
Architect
Architect
Jul 19, 2024 · Artificial Intelligence

Can Machine Learning Beat the Odds? A Deep Dive into Football Match Prediction

This article presents a data‑driven football match prediction system that extracts match features, builds machine‑learning models—including linear, SVM, random forest, and deep neural networks—and evaluates their accuracy on European league data, then analyzes betting strategies, limitations, and extensions to stock forecasting.

Artificial IntelligenceMachine LearningModel Evaluation
0 likes · 24 min read
Can Machine Learning Beat the Odds? A Deep Dive into Football Match Prediction
DataFunSummit
DataFunSummit
Jul 19, 2024 · Artificial Intelligence

Risk Control in the Bulk Commodity Industry: Data‑Driven Solutions and Credit‑Risk Modeling by Ant Group

This article presents Ant Group's data‑driven approach to digital transformation and risk control in the bulk commodity sector, covering background challenges, data‑application pain points, core capabilities, credit‑risk models, data‑asset construction, indicator frameworks, and secure data integration for B2B scenarios.

Machine Learningcommodity industrycredit risk
0 likes · 14 min read
Risk Control in the Bulk Commodity Industry: Data‑Driven Solutions and Credit‑Risk Modeling by Ant Group
Sohu Tech Products
Sohu Tech Products
Jul 17, 2024 · Artificial Intelligence

How Weak Supervision Powers Ant Group’s Real‑World AI Challenges

This article presents a comprehensive technical overview of weak‑supervision machine learning at Ant Group, covering its fundamentals, cross‑domain causal effect estimation, strategies for scarce or noisy labels, novel framework components, experimental validation, and practical application scenarios.

AIMachine LearningWeak Supervision
0 likes · 18 min read
How Weak Supervision Powers Ant Group’s Real‑World AI Challenges
Continuous Delivery 2.0
Continuous Delivery 2.0
Jul 15, 2024 · Artificial Intelligence

Safely Repairing Broken Builds with Machine Learning

Google's research demonstrates that a machine‑learning model trained on build logs and code snapshots can automatically suggest safe, high‑quality fixes for broken builds, boosting developer productivity by about two percent without introducing detectable security risks.

Code safetyML-assisted debuggingMachine Learning
0 likes · 10 min read
Safely Repairing Broken Builds with Machine Learning
DataFunSummit
DataFunSummit
Jul 14, 2024 · Artificial Intelligence

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

This article surveys recent advances in applying causal inference to recommender systems, presenting three lines of work—causal embedding for interest‑conformity disentanglement, contrastive learning for long‑term and short‑term interest separation, and adversarial debiasing of duration bias in short‑video recommendation—along with experimental validation and insights.

Bias MitigationMachine Learningcausal inference
0 likes · 24 min read
Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations
DataFunTalk
DataFunTalk
Jul 14, 2024 · Artificial Intelligence

Time Series and Machine Learning – An Overview and Book Introduction

The article introduces the rapid rise of large language models, the abundance of time‑series data in many sectors, and explains how combining machine‑learning and deep‑learning techniques with time‑series analysis has become a research hotspot, culminating in a new book that systematically covers theory, methods, and real‑world applications.

AIMachine Learninganomaly detection
0 likes · 10 min read
Time Series and Machine Learning – An Overview and Book Introduction
Python Programming Learning Circle
Python Programming Learning Circle
Jul 12, 2024 · Artificial Intelligence

Building a Simple Neural Network from Scratch in Python

This article walks through constructing a basic neural network using only Python and NumPy, explains the underlying concepts such as neurons, training cycles, sigmoid activation, and weight‑adjustment formulas, and provides complete, runnable code with sample inputs and outputs.

Artificial IntelligenceMachine LearningNeural Network
0 likes · 9 min read
Building a Simple Neural Network from Scratch in Python
Ximalaya Technology Team
Ximalaya Technology Team
Jul 12, 2024 · Artificial Intelligence

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

In real‑time bidding advertising, a multi‑path recall framework quickly filters billions of ads using parallel non‑personalized and personalized strategies—such as hot‑item rules, collaborative‑filtering, skip‑gram vectors, and GraphSAGE embeddings—while respecting targeting constraints, before a ranking stage optimizes eCPM, with effectiveness measured offline and online and future extensions planned with large language models.

AdvertisingGraph Neural NetworkMachine Learning
0 likes · 18 min read
Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems
DataFunTalk
DataFunTalk
Jul 12, 2024 · Artificial Intelligence

Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications

This article presents a comprehensive overview of weak supervision machine learning techniques applied to Ant Group's business problems, covering theoretical foundations, cross‑domain causal effect estimation, noisy‑label denoising frameworks, experimental results, and practical use cases such as risk modeling and marketing interventions.

Machine LearningWeak Supervisioncausal inference
0 likes · 16 min read
Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications
AntTech
AntTech
Jul 11, 2024 · Information Security

Enhancing Fraud Transaction Detection via Unlabeled Suspicious Records (GIANTESS Framework)

The paper presents GIANTESS, a novel semi‑supervised fraud detection framework that leverages online‑identified suspicious transactions to augment the feature space, generating pseudo‑labels for out‑of‑distribution samples and employing a hybrid loss to improve detection of covert fraudulent activities, achieving notable recall gains on real‑world datasets.

GIANTESSMachine Learningsemi-supervised learning
0 likes · 6 min read
Enhancing Fraud Transaction Detection via Unlabeled Suspicious Records (GIANTESS Framework)
Python Programming Learning Circle
Python Programming Learning Circle
Jul 9, 2024 · Artificial Intelligence

Principal Component Analysis (PCA) with Python: Theory and Practical Example on the Breast Cancer Dataset

This article explains the fundamentals of Principal Component Analysis (PCA), demonstrates its application on the Breast Cancer Wisconsin dataset using Python code, and shows how scaling, PCA transformation, scree plots, and feature-group comparisons can reveal data structure and improve predictive modeling.

Breast Cancer DatasetMachine LearningPCA
0 likes · 11 min read
Principal Component Analysis (PCA) with Python: Theory and Practical Example on the Breast Cancer Dataset
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 7, 2024 · Artificial Intelligence

Daily and Sports Activities Dataset: Description, Preprocessing Pipeline, and CNN Classification Results

This article introduces the Daily_and_Sports_Activities sensor dataset, details its structure and characteristics, provides a Python preprocessing pipeline with sliding‑window segmentation and Z‑score normalization, and reports CNN training results achieving 87.93% accuracy on activity classification.

CNNMachine LearningUCI
0 likes · 9 min read
Daily and Sports Activities Dataset: Description, Preprocessing Pipeline, and CNN Classification Results
Ops Development & AI Practice
Ops Development & AI Practice
Jul 6, 2024 · Artificial Intelligence

How Backpropagation Powers Modern Deep Learning: A Deep Dive

This article explains the backpropagation algorithm—its origins, mathematical basis, step‑by‑step workflow, importance for efficient neural network training, and widespread applications in image recognition, natural language processing, and recommendation systems.

BackpropagationDeep LearningMachine Learning
0 likes · 6 min read
How Backpropagation Powers Modern Deep Learning: A Deep Dive
21CTO
21CTO
Jul 5, 2024 · Artificial Intelligence

15 Real-World Ways Companies Leverage Large Language Models

This article explores fifteen detailed examples of how major companies across sectors—from streaming and e‑commerce to transportation and social platforms—are harnessing large language models to improve search, personalize communications, detect fraud, and enhance operational efficiency.

AI case studiesEnterprise AILLM applications
0 likes · 9 min read
15 Real-World Ways Companies Leverage Large Language Models
DataFunSummit
DataFunSummit
Jul 5, 2024 · Artificial Intelligence

Building and Applying a User Profile Tagging System: Practices and Insights

This article presents a comprehensive overview of constructing and deploying a user and item profiling tag system at Qunar, covering tag taxonomy, integration challenges, technical architectures, algorithmic methods such as classification, recommendation, knowledge‑graph and causal inference, as well as real‑time streaming, ID‑mapping, and practical applications in marketing, attribution and A/B testing.

AB testingMachine LearningTagging System
0 likes · 21 min read
Building and Applying a User Profile Tagging System: Practices and Insights
Ops Development & AI Practice
Ops Development & AI Practice
Jul 4, 2024 · Artificial Intelligence

Discriminative vs Generative Models: When to Use Each in AI

The article explains the fundamental differences between discriminative and generative models, detailing their learning objectives, typical algorithms, key characteristics, example implementations, and practical application scenarios, helping readers choose the appropriate model for classification or data‑generation tasks.

AIDiscriminative ModelsGenerative Models
0 likes · 6 min read
Discriminative vs Generative Models: When to Use Each in AI
Tencent Cloud Developer
Tencent Cloud Developer
Jul 4, 2024 · Artificial Intelligence

Football Match Outcome Prediction and Betting Strategy Using Machine Learning

The study combines team statistics and bookmaker odds with machine‑learning models—including Poisson, regression, Bayesian, SVM, Random Forest, DNN, and LSTM—to predict football match outcomes, identify confidence‑based betting intervals that yield profit, and suggests extensions to broader data, features, and financial trading.

Machine LearningRandom ForestSVM
0 likes · 23 min read
Football Match Outcome Prediction and Betting Strategy Using Machine Learning
Ops Development & AI Practice
Ops Development & AI Practice
Jul 3, 2024 · Artificial Intelligence

How Do Artificial Neural Networks Mirror Animal Brains? An In‑Depth Overview

This article explains the fundamental concepts and architecture of artificial neural networks, describes their learning process, compares them with biological neural systems, and highlights both the similarities and key differences in structure, learning mechanisms, flexibility, and energy efficiency.

Artificial IntelligenceBiological InspirationDeep Learning
0 likes · 7 min read
How Do Artificial Neural Networks Mirror Animal Brains? An In‑Depth Overview
Continuous Delivery 2.0
Continuous Delivery 2.0
Jul 2, 2024 · Artificial Intelligence

Dynamic Integrated Developer Activity (DIDACT): Large Sequence Models for Software Development

The article introduces DIDACT, a large‑scale multitask machine‑learning framework that trains on the full software‑development workflow—including edits, builds, reviews, and tool interactions—to create AI assistants that can predict and suggest developer actions throughout the coding process.

AI for CodeLarge Language ModelsMachine Learning
0 likes · 11 min read
Dynamic Integrated Developer Activity (DIDACT): Large Sequence Models for Software Development
Python Programming Learning Circle
Python Programming Learning Circle
Jun 27, 2024 · Artificial Intelligence

Eight Python Libraries to Accelerate Data Science and Machine Learning Workflows

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

LibrariesMachine LearningPython
0 likes · 11 min read
Eight Python Libraries to Accelerate Data Science and Machine Learning Workflows
Ops Development & AI Practice
Ops Development & AI Practice
Jun 26, 2024 · Fundamentals

Why Jupyter Notebooks Revolutionized Data Science and Machine Learning

This article explores the origins, key innovations, and lasting impact of Jupyter notebooks, highlighting how their multi‑language support, interactive computing, reproducibility, and extensibility have transformed data exploration, collaboration, education, and research in modern data science and machine learning.

Interactive ComputingJupyterMachine Learning
0 likes · 5 min read
Why Jupyter Notebooks Revolutionized Data Science and Machine Learning
JD Tech Talk
JD Tech Talk
Jun 25, 2024 · Artificial Intelligence

Understanding Large Language Models: From Parameters to Transformer Architecture

This article explains the fundamental concepts behind large language models, including their two-file structure, training process, neural network basics, perceptron examples, weight and threshold calculations, the TensorFlow Playground, and a detailed walkthrough of the Transformer architecture with tokenization, positional encoding, self‑attention, normalization, and feed‑forward layers.

AILarge Language ModelsMachine Learning
0 likes · 20 min read
Understanding Large Language Models: From Parameters to Transformer Architecture
JavaEdge
JavaEdge
Jun 23, 2024 · Artificial Intelligence

Mapping the Generative AI Landscape: From Infrastructure to Applications

This article provides a comprehensive overview of the generative AI industry, detailing its upstream foundation layer, midstream large‑model and tool layers, downstream application scenarios, and an extensive glossary of models, techniques, platforms, and concepts.

AI architectureIndustry OverviewMachine Learning
0 likes · 12 min read
Mapping the Generative AI Landscape: From Infrastructure to Applications
DataFunSummit
DataFunSummit
Jun 22, 2024 · Artificial Intelligence

Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice

This article introduces causal inference fundamentals, distinguishes correlation from causation, reviews major methodological streams, and demonstrates how uplift and gain models—implemented with T‑learner, S‑learner, and tree‑based approaches—can be applied to user growth and marketing scenarios, including evaluation metrics and future challenges.

A/B testingMachine LearningUplift Modeling
0 likes · 14 min read
Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice
Continuous Delivery 2.0
Continuous Delivery 2.0
Jun 19, 2024 · Artificial Intelligence

Google Smart Paste: AI‑Powered Context‑Aware Adjustments for Pasted Code

Google's Smart Paste uses generative AI to automatically adapt pasted code to its surrounding context, reducing manual edits and improving developer productivity, as demonstrated by extensive internal studies involving tens of thousands of engineers and detailed model training, calibration, and user‑experience evaluations.

AI code assistanceGoogleMachine Learning
0 likes · 9 min read
Google Smart Paste: AI‑Powered Context‑Aware Adjustments for Pasted Code
Continuous Delivery 2.0
Continuous Delivery 2.0
Jun 18, 2024 · Artificial Intelligence

Google's ML‑Enhanced Code Completion Improves Developer Productivity

Google's research demonstrates that integrating a transformer‑based machine‑learning model with a rule‑based semantic engine for code completion reduces developers' coding iteration time by 6%, increases accepted suggestions to 25‑34%, and completes over 3% of code, highlighting significant productivity gains across multiple programming languages.

IDEMachine LearningTransformer
0 likes · 6 min read
Google's ML‑Enhanced Code Completion Improves Developer Productivity
DataFunTalk
DataFunTalk
Jun 15, 2024 · Artificial Intelligence

DataFunSummit2024 Recommendation System Architecture Summit Overview

The DataFunSummit2024 Recommendation System Architecture Summit invites participants to explore cutting‑edge advances in large‑model recommendation, training and inference optimization, feature engineering, multi‑task modeling, and graph‑based techniques through a series of expert talks and panel discussions from leading industry and academic researchers.

AILarge ModelsMachine Learning
0 likes · 33 min read
DataFunSummit2024 Recommendation System Architecture Summit Overview
php Courses
php Courses
Jun 13, 2024 · Artificial Intelligence

Using PHP for Data Dimensionality Reduction and Feature Extraction

This article explains the importance of data dimensionality reduction and feature extraction in machine learning, and provides a step‑by‑step guide with PHP code examples—including library installation, data preprocessing, PCA‑based reduction, and feature selection techniques—demonstrating how to handle large datasets efficiently.

Machine LearningPCAPHP
0 likes · 6 min read
Using PHP for Data Dimensionality Reduction and Feature Extraction
21CTO
21CTO
Jun 12, 2024 · Artificial Intelligence

How Alan Turing’s Legacy Fuels Today’s AI Revolution

This article chronicles Alan Turing’s groundbreaking work—from the invention of the Turing machine and his wartime code‑breaking feats to the birth of the Turing test—showing how his ideas continue to shape modern artificial intelligence, large language models, and the broader tech culture.

Alan TuringArtificial IntelligenceMachine Learning
0 likes · 10 min read
How Alan Turing’s Legacy Fuels Today’s AI Revolution
Qunar Tech Salon
Qunar Tech Salon
Jun 12, 2024 · Artificial Intelligence

Design and Implementation of Qunar Flight Ticket Intelligent Alert (Radar) System

This article presents a comprehensive analysis and engineering of Qunar's flight‑ticket intelligent pre‑warning (Radar) system, covering the business need, value analysis, architectural redesign, feature extraction, indicator classification, accuracy quantification, multi‑algorithm anomaly detection, automatic parameter tuning, observed effects, and future plans to incorporate large‑model techniques.

Machine LearningMonitoringanomaly detection
0 likes · 17 min read
Design and Implementation of Qunar Flight Ticket Intelligent Alert (Radar) System
DataFunTalk
DataFunTalk
Jun 11, 2024 · Artificial Intelligence

Guide to Fine‑Tuning OpenAI Models for Improved Performance

This guide explains how to fine‑tune OpenAI’s pre‑trained models, covering data preparation, environment setup, API usage, code examples, hyper‑parameter tuning, monitoring, and best practices to achieve better performance with less data and compute resources.

AI modelsAPIData Preparation
0 likes · 16 min read
Guide to Fine‑Tuning OpenAI Models for Improved Performance
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 11, 2024 · Artificial Intelligence

Mastering Retrieval‑Augmented Generation: Challenges, Paradigms, and Engineering Best Practices

This article explores Retrieval‑Augmented Generation (RAG) by outlining its background, inherent challenges such as knowledge limits and hallucinations, describing the Naïve, Advanced, and Modular RAG paradigms, and presenting practical engineering strategies for pre‑retrieval, retrieval, and post‑retrieval optimization.

Artificial IntelligenceKnowledge RetrievalMachine Learning
0 likes · 25 min read
Mastering Retrieval‑Augmented Generation: Challenges, Paradigms, and Engineering Best Practices
DataFunSummit
DataFunSummit
Jun 7, 2024 · Artificial Intelligence

Understanding Feature Engineering for Risk Control Systems and Building an Easy-to-Use Feature Platform

Feature engineering, the process of creating input variables for machine learning models, is crucial for banking risk control; this article explains the concepts of features, variables, and metrics, outlines challenges in real‑time feature pipelines, and proposes a practical architecture and best practices for building an efficient, low‑code feature platform.

Machine LearningPlatform designfeature engineering
0 likes · 10 min read
Understanding Feature Engineering for Risk Control Systems and Building an Easy-to-Use Feature Platform
Java Tech Enthusiast
Java Tech Enthusiast
Jun 7, 2024 · Fundamentals

Engineer Builds GPU from Scratch in Two Weeks

In just two weeks, engineer Adam Majmudar designed and implemented a minimalist GPU called tiny‑gpu—complete with a custom 11‑instruction ISA, Verilog RTL, and verified via OpenLane—sharing the open‑source project on GitHub, earning thousands of stars, and preparing it for fabrication through Tiny Tapeout 7, showcasing how modern tools make DIY chip design increasingly accessible.

EDAGPUMachine Learning
0 likes · 8 min read
Engineer Builds GPU from Scratch in Two Weeks
DataFunSummit
DataFunSummit
Jun 4, 2024 · Artificial Intelligence

Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems

This article details eBay's practical experience integrating multimodal data and graph neural networks into its recommendation pipeline, covering pain‑point analysis, a twin‑tower multimodal embedding model with triplet loss and TransH, engineering design, experimental results, and key takeaways for future AI‑driven product development.

GNNGraph Neural NetworkMachine Learning
0 likes · 19 min read
Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems
DataFunSummit
DataFunSummit
Jun 2, 2024 · Artificial Intelligence

Construction and Application of a User Profile Tag System: Methods, Platforms, and Use Cases

This article presents a comprehensive overview of building a user profile tag system—including tag taxonomy, platform architecture, construction methods, update cycles, access patterns, common algorithmic tags, and real‑world applications such as marketing, metric attribution, and A/B testing—illustrated with examples and a detailed Q&A session from a data‑mining senior manager at Qunar.

AB testingMachine Learningcausal inference
0 likes · 21 min read
Construction and Application of a User Profile Tag System: Methods, Platforms, and Use Cases
DataFunSummit
DataFunSummit
Jun 1, 2024 · Artificial Intelligence

Graph Foundation Models: Concepts, Progress, and Future Directions

This article provides a comprehensive overview of Graph Foundation Models (GFMs), covering their definition, key characteristics, historical development of graph machine learning, recent research trends such as PT‑HGNN, Specformer, and GraphTranslator, and discusses future challenges and research directions.

Large Language ModelsMachine Learningfoundation models
0 likes · 23 min read
Graph Foundation Models: Concepts, Progress, and Future Directions
DeWu Technology
DeWu Technology
May 31, 2024 · Artificial Intelligence

In-depth Analysis of Prophet Time Series Forecasting Model

The article offers a thorough examination of Facebook’s Prophet forecasting model, detailing its additive decomposition of trend, seasonality, holidays and regressors, the underlying Bayesian inference via Stan, the full training‑and‑prediction pipeline, data‑normalization tricks, uncertainty estimation, and practical source‑code insights for e‑commerce applications.

Machine LearningProphet modelSource Code Analysis
0 likes · 21 min read
In-depth Analysis of Prophet Time Series Forecasting Model
Alimama Tech
Alimama Tech
May 29, 2024 · Artificial Intelligence

Mixture of Multi‑Modal Experts for Advertising Recall

The Mixed‑Modal Expert Model combines ID features with image and text embeddings through optimized representations and conditional output fusion, dramatically improving advertising recall—especially for long‑tail items—and delivering measurable gains in click‑recall, revenue, CTR, and page views in large‑scale online tests.

Machine LearningModelMultimodal
0 likes · 15 min read
Mixture of Multi‑Modal Experts for Advertising Recall
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
May 29, 2024 · Artificial Intelligence

ContraLSP: Contrastive Sparse Perturbations Transform Time‑Series Explanation

Recent collaboration between Alibaba Cloud’s big‑data team and leading universities introduced ContraLSP, a novel contrastive and locally sparse perturbation framework that outperforms state‑of‑the‑art methods in explaining time‑series models, offering improved interpretability for both white‑box forecasting and black‑box classification tasks.

Machine Learningcontrastive learninginterpretability
0 likes · 8 min read
ContraLSP: Contrastive Sparse Perturbations Transform Time‑Series Explanation
Architects Research Society
Architects Research Society
May 21, 2024 · Artificial Intelligence

27 Essential AI Papers Recommended by Ilya Sutskever for John Carmack

Ilya Sutskever, former OpenAI chief scientist, shared a curated list of 27 seminal AI research papers—including the Annotated Transformer, Attention Is All You Need, and Deep Residual Learning—with links, claiming mastering them covers roughly 90% of today’s essential artificial‑intelligence knowledge.

AIDeep LearningMachine Learning
0 likes · 7 min read
27 Essential AI Papers Recommended by Ilya Sutskever for John Carmack
Test Development Learning Exchange
Test Development Learning Exchange
May 21, 2024 · Artificial Intelligence

Step-by-Step Data Analysis and Machine Learning Workflow with Pandas, Matplotlib, and Scikit-learn

This guide walks through loading CSV data with pandas, cleaning missing values, filtering, grouping, visualizing, performing correlation and time‑series analysis, detecting outliers, and applying linear and logistic regression models using scikit‑learn, all illustrated with complete Python code snippets.

Machine LearningPandasdata cleaning
0 likes · 6 min read
Step-by-Step Data Analysis and Machine Learning Workflow with Pandas, Matplotlib, and Scikit-learn
Model Perspective
Model Perspective
May 20, 2024 · Artificial Intelligence

How Dimensionality Reduction and Graph Theory Simplify Complex Systems

The article explains how dimensionality reduction techniques—such as PCA, LDA, and t‑SNE—combined with graph theory can transform high‑dimensional data into simpler, low‑dimensional representations, enabling clearer analysis of complex systems like neural networks and image data, and enhancing machine‑learning efficiency.

Machine Learningdata visualizationdimensionality reduction
0 likes · 6 min read
How Dimensionality Reduction and Graph Theory Simplify Complex Systems
DataFunSummit
DataFunSummit
May 16, 2024 · Artificial Intelligence

DataFun Data Science Summit: Cutting‑Edge Research on Causal Inference, Retrieval‑Augmented Generation, and LLM Content Detection

The DataFun Data Science Summit on May 25 brings together leading experts to present cutting‑edge research on pairwise data causal inference, Retrieval‑Augmented Generation applications, large language model content detection, user growth analytics, and advanced machine‑learning techniques across finance, e‑commerce, and AI domains.

AILLM detectionMachine Learning
0 likes · 14 min read
DataFun Data Science Summit: Cutting‑Edge Research on Causal Inference, Retrieval‑Augmented Generation, and LLM Content Detection
DataFunSummit
DataFunSummit
May 11, 2024 · Artificial Intelligence

Why Causal Inference Matters in Machine Learning and Its Banking Applications

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

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

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

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

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

Graph Model Practices and Applications in Baidu Recommendation System

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Causal Solutions for Recommendation System Bias and Practical Applications

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

Bias MitigationMachine Learningcausal inference
0 likes · 12 min read
Causal Solutions for Recommendation System Bias and Practical Applications
JD Cloud Developers
JD Cloud Developers
Apr 30, 2024 · Artificial Intelligence

Build a Handwritten Digit Recognizer in Java with TensorFlow

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

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

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

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

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

How Precise Testing Boosts Software Quality and Efficiency

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

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

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

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

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

Five Essential Python Libraries for Machine Learning Engineers

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

ELI5FastAPIMLflow
0 likes · 8 min read
Five Essential Python Libraries for Machine Learning Engineers
JD Retail Technology
JD Retail Technology
Apr 24, 2024 · Backend Development

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Bias MitigationMachine Learningcausal inference
0 likes · 23 min read
Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations
DataFunTalk
DataFunTalk
Apr 7, 2024 · Artificial Intelligence

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

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

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

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

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

Black-Box OptimizationMachine Learningbayesian optimization
0 likes · 7 min read
Scikit‑Optimize (skopt): Features, Use Cases, and Code Examples
Python Programming Learning Circle
Python Programming Learning Circle
Apr 2, 2024 · Artificial Intelligence

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

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

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

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

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

Machine LearningRecommendation Systemsadvertising algorithms
0 likes · 19 min read
Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications
NewBeeNLP
NewBeeNLP
Mar 28, 2024 · Industry Insights

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

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

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

Eight Python Libraries to Accelerate Data‑Science Workflows

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

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

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

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

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

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

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

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

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

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

Demand ModelingMachine Learningcausal inference
0 likes · 11 min read
Modeling Price-Demand Relationships for Online Hotel Booking: Demand Functions, Causal Inference, and Multi-Scenario Joint Modeling