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Architecture Digest
Architecture Digest
May 19, 2018 · Artificial Intelligence

Optical Flow: Principles, Evolution, and Applications in Computer Vision

This article explains the fundamentals of optical flow, traces its development from early variational methods to modern deep‑learning models like FlowNet, and discusses practical applications such as video object detection, semantic segmentation, and novel view synthesis, highlighting both technical challenges and future research directions.

Computer VisionDeep LearningFlowNet
0 likes · 14 min read
Optical Flow: Principles, Evolution, and Applications in Computer Vision
AntTech
AntTech
May 10, 2018 · Artificial Intelligence

MISA – Ant Financial’s AI Voice Service Assistant: Architecture, Deep‑Learning Models, and the AI Competition

The article introduces MISA, Ant Financial’s AI‑driven voice service assistant that uses deep‑learning models such as CNN and RNN for problem guessing, identification, and interactive clarification, details its system components and evaluation metrics, and describes the related AI competition focused on sentence‑similarity calculation.

AIDeep LearningVoice Assistant
0 likes · 14 min read
MISA – Ant Financial’s AI Voice Service Assistant: Architecture, Deep‑Learning Models, and the AI Competition
21CTO
21CTO
May 8, 2018 · Artificial Intelligence

How Optical Flow Powers 360° Product Views and Advanced Vision Applications

This article explores the evolution and principles of optical flow—from early Horn‑Schunck models and Lucas‑Kanade to modern deep‑learning approaches like FlowNet—detailing its role in JD’s 360° product imaging, video detection, segmentation, view synthesis, and future research challenges in computer vision.

Deep LearningImage Processingoptical flow
0 likes · 15 min read
How Optical Flow Powers 360° Product Views and Advanced Vision Applications
JD Tech
JD Tech
May 4, 2018 · Artificial Intelligence

Optical Flow: Principles, Methods, and Applications in Computer Vision

This article introduces the fundamentals and evolution of optical flow, covering classic algorithms such as Horn‑Schunck and Lucas‑Kanade, modern deep‑learning approaches like FlowNet, and their practical applications in video detection, semantic segmentation, and novel view synthesis.

CNNDeep LearningImage Processing
0 likes · 15 min read
Optical Flow: Principles, Methods, and Applications in Computer Vision
Suning Technology
Suning Technology
Apr 26, 2018 · Artificial Intelligence

Inside Suning’s Scalable Real‑Time Face Recognition Architecture and Algorithms

Suning’s face recognition solution combines front‑end detection, optimal photo selection, alignment, and cloud‑based feature extraction and matching, leveraging deep‑learning models, weight and feature normalization, angular margins, and triplet loss, while optimizing hardware, bandwidth, and data quality for large‑scale 1:N deployments.

Deep Learningdata augmentationface recognition
0 likes · 18 min read
Inside Suning’s Scalable Real‑Time Face Recognition Architecture and Algorithms
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 25, 2018 · Artificial Intelligence

How cw2vec Beats Word2Vec: Leveraging Chinese Stroke N‑grams for Superior Word Embeddings

This article introduces cw2vec, a novel Chinese word‑embedding algorithm that exploits stroke‑level subword information, outlines its theoretical foundations, compares it with word2vec, GloVe, CWE and other models on multiple benchmarks, and demonstrates its superior performance across word similarity, analogy, text classification and named‑entity recognition tasks.

Chinese NLPDeep LearningUnsupervised Learning
0 likes · 14 min read
How cw2vec Beats Word2Vec: Leveraging Chinese Stroke N‑grams for Superior Word Embeddings
Architects' Tech Alliance
Architects' Tech Alliance
Apr 23, 2018 · Fundamentals

Why Heterogeneous Parallel Computing Is the Future of High‑Performance Computing

The article explains how heterogeneous parallel computing—leveraging CPUs, GPUs, FPGAs and other specialized units—addresses the performance limits of traditional serial programming by distributing tasks across diverse hardware, detailing its concepts, architectures, development models, and relevance to AI and cloud workloads.

CPUDeep LearningFPGA
0 likes · 9 min read
Why Heterogeneous Parallel Computing Is the Future of High‑Performance Computing
Suning Technology
Suning Technology
Apr 23, 2018 · Artificial Intelligence

How Suning’s Facial Recognition Powers Unmanned Stores and Beats Global Benchmarks

At QCon 2018 in Beijing, Suning’s Silicon Valley Research Institute showcased its cutting‑edge facial‑recognition system—leveraging ResNet and Inception‑ResNet architectures—to achieve top global rankings and enable real‑time, contact‑less services such as unmanned stores, employee access control, and intelligent store video analytics.

AIBenchmarkDeep Learning
0 likes · 7 min read
How Suning’s Facial Recognition Powers Unmanned Stores and Beats Global Benchmarks
JD Tech
JD Tech
Apr 19, 2018 · Artificial Intelligence

Key Insights from Prof. Zhou Zhihua’s Talk on Deep Learning, Model Complexity, and the Deep Forest Method

In his JD AI Innovation Summit presentation, Prof. Zhou Zhihua examined why deep neural networks have succeeded, identified three essential conditions—layer‑wise processing, internal feature transformation, and sufficient model complexity—highlighted their limitations, introduced the gcforest/deep forest alternative, and emphasized the need for large data, powerful hardware, training tricks, and talent to advance AI research and education.

AI educationDeep LearningNeural Networks
0 likes · 23 min read
Key Insights from Prof. Zhou Zhihua’s Talk on Deep Learning, Model Complexity, and the Deep Forest Method
Suning Technology
Suning Technology
Apr 16, 2018 · Artificial Intelligence

How Suning’s AI‑Powered Banner Design Platform Revolutionizes E‑Commerce Advertising

This article explains how Suning’s intelligent design platform automates banner creation for online retail by combining deep‑learning image segmentation, rule‑based layout generation, multi‑task evaluation models, and adaptive coloring, dramatically reducing manual effort while boosting personalization and conversion rates.

AIAutomationDeep Learning
0 likes · 17 min read
How Suning’s AI‑Powered Banner Design Platform Revolutionizes E‑Commerce Advertising
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 16, 2018 · Artificial Intelligence

How Alibaba’s Deep Learning Transformed CTR Prediction: From MLR to Multi‑Interest Networks

This article recounts Alibaba‑Mama researcher Jing Shi’s presentation on the evolution of deep learning for click‑through‑rate (CTR) estimation, covering the shift from handcrafted features and linear models to piecewise linear MLR, end‑to‑end neural networks, multi‑interest user modeling, and large‑scale distributed training challenges.

AdvertisingCTR predictionDeep Learning
0 likes · 16 min read
How Alibaba’s Deep Learning Transformed CTR Prediction: From MLR to Multi‑Interest Networks
Ctrip Technology
Ctrip Technology
Apr 3, 2018 · Artificial Intelligence

Ctrip Hotel Image Intelligence: From Pre‑Processing to Smart Applications

This article describes Ctrip's end‑to‑end hotel image intelligence platform, covering image pre‑audit, deduplication, watermark detection, quality enhancement, content classification, aesthetic assessment, and downstream applications such as smart display, image‑text integration, and automated video generation, all driven by computer‑vision and deep‑learning techniques.

Deep LearningHotel Industry
0 likes · 18 min read
Ctrip Hotel Image Intelligence: From Pre‑Processing to Smart Applications
Meituan Technology Team
Meituan Technology Team
Mar 29, 2018 · Artificial Intelligence

Deep Learning Model Applications and Optimizations for Recommendation Ranking at Meituan

The paper describes how Meituan tackles information overload on its lifestyle platform by training multi‑task deep neural networks on billions of interaction logs using a distributed PS‑Lite framework, employing sophisticated feature engineering, missing‑value imputation, KL‑regularization and Neural Factorization Machines to boost offline AUC and online CTR in the “Guess You Like” recommendation feed, while introducing training‑time optimizations and outlining future multi‑task and contextual enhancements.

Deep LearningRecommendation Systemsfeature engineering
0 likes · 16 min read
Deep Learning Model Applications and Optimizations for Recommendation Ranking at Meituan
Baobao Algorithm Notes
Baobao Algorithm Notes
Mar 28, 2018 · Artificial Intelligence

Mastering CTR/CVR Prediction: Core Techniques and Resources from Recent Competitions

This article reviews the fundamentals of click‑through‑rate (CTR) and conversion‑rate (CVR) prediction, explains why the problem is challenging due to high‑dimensional sparse features, and summarizes classic and modern modeling approaches—including feature engineering, linear models, factorization machines, GBDT‑LR, and deep neural networks—while providing practical code snippets and useful research links.

CTRCVRDeep Learning
0 likes · 8 min read
Mastering CTR/CVR Prediction: Core Techniques and Resources from Recent Competitions
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 28, 2018 · Artificial Intelligence

How Tree‑Based Deep Match Revolutionizes Large‑Scale Recommendation Systems

This article introduces the Tree‑based Deep Match (TDM) framework, which uses a novel max‑heap tree structure to enable efficient, hierarchical retrieval over massive candidate sets, allowing any advanced deep learning model to improve matching accuracy, recall, and novelty in industrial recommendation systems.

Deep Learninglarge-scale recommendationmachine learning
0 likes · 27 min read
How Tree‑Based Deep Match Revolutionizes Large‑Scale Recommendation Systems
Tencent Cloud Developer
Tencent Cloud Developer
Mar 21, 2018 · Artificial Intelligence

Abusive Comment Detection Using TextCNN: A Strategy + Algorithm Approach

The article proposes a hybrid approach that first filters blacklist words and then classifies suspicious comments with a character-level TextCNN, achieving around 89% precision and 87% recall, demonstrating that simple convolutional networks outperform keyword filters and RNNs for short, noisy abusive Chinese text.

Abusive Comment DetectionDeep LearningNLP
0 likes · 10 min read
Abusive Comment Detection Using TextCNN: A Strategy + Algorithm Approach
Architecture Digest
Architecture Digest
Mar 16, 2018 · Artificial Intelligence

Essential Cheat Sheets for Machine Learning and Deep Learning Researchers

This article introduces a GitHub repository that compiles comprehensive cheat sheets covering key Python libraries such as Keras, NumPy, Pandas, SciPy, Matplotlib, Scikit-learn, and others, providing quick reference resources to help beginners and researchers efficiently navigate machine learning and deep learning workflows.

AIDeep LearningPython
0 likes · 5 min read
Essential Cheat Sheets for Machine Learning and Deep Learning Researchers
Tencent TDS Service
Tencent TDS Service
Mar 15, 2018 · Artificial Intelligence

Step-by-Step TensorFlow Setup on Windows and Build MNIST CNN from Scratch

This guide walks you through installing Anaconda, creating a TensorFlow virtual environment on Windows, configuring CPU and GPU versions, and implementing both a basic softmax regression and a deep convolutional neural network for MNIST digit recognition, complete with code snippets, training tips, and visualization tools.

AnacondaCNNDeep Learning
0 likes · 21 min read
Step-by-Step TensorFlow Setup on Windows and Build MNIST CNN from Scratch
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 15, 2018 · Artificial Intelligence

How Deep Learning Transforms Knowledge Graph Relation Extraction

This article reviews the evolution from rule‑based DeepDive methods to deep‑learning approaches such as PCNNs and attention‑enhanced models for relation extraction, presents experimental results on the NYT dataset, discusses practical challenges in large‑scale deployment, and outlines future research directions.

Attention MechanismDeep LearningKnowledge Graph
0 likes · 14 min read
How Deep Learning Transforms Knowledge Graph Relation Extraction
Architecture Digest
Architecture Digest
Mar 10, 2018 · Blockchain

Why Fully Automated Formal Verification of Smart Contracts Is Impossible

The article argues that automatic formal verification of Ethereum smart contracts using deep learning and Hoare Logic is fundamentally impossible because pre‑ and post‑conditions must be manually specified, and it further critiques the overall concept of smart contracts as an overengineered and unnecessary feature of blockchain systems.

BlockchainDeep LearningHoare logic
0 likes · 12 min read
Why Fully Automated Formal Verification of Smart Contracts Is Impossible
Hulu Beijing
Hulu Beijing
Mar 6, 2018 · Artificial Intelligence

Understanding WGANs: From GAN Pitfalls to Wasserstein Solutions

This article explains the shortcomings of traditional GANs, introduces the Wasserstein GAN (WGAN) as a remedy using the Earth‑Mover distance, describes the theoretical motivations, outlines the algorithmic steps and constraints, and provides illustrative diagrams and references for deeper study.

Deep LearningGenerative Adversarial NetworksWGAN
0 likes · 11 min read
Understanding WGANs: From GAN Pitfalls to Wasserstein Solutions
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 27, 2018 · Artificial Intelligence

How AR Transforms Coffee Retail: Inside Alibaba’s AI‑Powered Cloud Recognition

Alibaba’s AI Lab built an AR‑enhanced Starbucks coffee workshop in Shanghai, using client‑side object detection, deep‑learning cloud recognition, image synthesis, and color‑simulation techniques to overcome challenges like metal reflections, transparency, and varying lighting, illustrating how AR can revamp new‑retail experiences.

ARDeep Learningaugmented reality
0 likes · 8 min read
How AR Transforms Coffee Retail: Inside Alibaba’s AI‑Powered Cloud Recognition
21CTO
21CTO
Feb 24, 2018 · Artificial Intelligence

Why Deep Learning Is Revolutionizing Recommendation Systems

This article explores how deep learning techniques such as item embeddings, autoencoders, Word2Vec, and session‑based neural models are applied to recommendation systems, highlighting their advantages, key architectures, and recent advances from industry and research.

AIDeep LearningRecommendation Systems
0 likes · 17 min read
Why Deep Learning Is Revolutionizing Recommendation Systems
Architecture Digest
Architecture Digest
Feb 24, 2018 · Artificial Intelligence

Eight Neural Network Architectures Every Machine Learning Researcher Should Know

This article explains why machine learning is essential for complex tasks, defines neural networks, outlines three reasons to study them, and provides concise overviews of eight fundamental neural network architectures—including perceptron, CNN, RNN, LSTM, Hopfield, Boltzmann machines, deep belief networks, and deep autoencoders—grouped by their structural categories.

AI architecturesCNNDeep Learning
0 likes · 23 min read
Eight Neural Network Architectures Every Machine Learning Researcher Should Know
Architecture Digest
Architecture Digest
Feb 22, 2018 · Artificial Intelligence

Deep Learning Applications in Recommendation Systems

This article explains why deep learning has become essential for modern recommendation systems, describing its advantages such as automatic feature extraction, noise robustness, sequential modeling with RNNs, and improved user‑item representation, and reviews major deep‑learning‑based recommendation models and techniques.

Deep LearningRecommendation SystemsWord2Vec
0 likes · 17 min read
Deep Learning Applications in Recommendation Systems
Hulu Beijing
Hulu Beijing
Feb 1, 2018 · Artificial Intelligence

Understanding GANs: Theory, Minimax Game, and Training Challenges

This article introduces Generative Adversarial Networks (GANs), explains their minimax formulation, value function, Jensen‑Shannon divergence, common variants, and practical training issues such as gradient saturation, while also previewing the next topic on Hidden Markov Models.

Deep LearningGANGenerative Adversarial Networks
0 likes · 11 min read
Understanding GANs: Theory, Minimax Game, and Training Challenges
JD Tech
JD Tech
Feb 1, 2018 · Artificial Intelligence

Telepath: A Vision‑Based Recommender Model Inspired by Human Visual Perception

The Telepath model, presented at AAAI 2018, leverages a biologically‑inspired visual extraction pipeline and dual interest‑understanding networks to improve ranking in large‑scale e‑commerce recommendation and advertising, achieving significant offline and online gains in CTR, GMV, and ROI.

AAAI 2018Deep LearningTelepath
0 likes · 13 min read
Telepath: A Vision‑Based Recommender Model Inspired by Human Visual Perception
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 24, 2018 · Artificial Intelligence

How Alibaba’s Intelligent Writer Boosted Double‑11 Clicks with AI‑Generated Content

This article details Alibaba's Intelligent Writer system, which leverages AI models like PairXNN and deep generation networks to automatically create short copy, benefit points, and rich visual lists for Taobao, achieving significant click‑through improvements during the 2017 Double‑11 shopping festival.

AIDeep Learninge‑commerce
0 likes · 20 min read
How Alibaba’s Intelligent Writer Boosted Double‑11 Clicks with AI‑Generated Content
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 29, 2017 · Artificial Intelligence

How Alibaba Leverages Deep Learning to Revolutionize E‑Commerce Search

Alibaba’s search team outlines how deep learning transforms e‑commerce search and recommendation, detailing system infrastructure, AI‑driven features like intelligent interaction, semantic search, personalized matching, performance optimizations, multi‑agent learning, and future plans for unified user and query representations.

AIDeep Learninge‑commerce
0 likes · 17 min read
How Alibaba Leverages Deep Learning to Revolutionize E‑Commerce Search
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Dec 27, 2017 · Artificial Intelligence

Why Is Math the Biggest Hurdle in Deep Learning? A Step‑by‑Step Guide

This article breaks down the essential mathematics—linear algebra, probability, calculus, and optimization—required for mastering deep learning, explains how each topic maps to core deep‑learning concepts, and outlines six progressive learning stages with concrete examples and recommended textbooks.

AI fundamentalsDeep Learninglinear algebra
0 likes · 50 min read
Why Is Math the Biggest Hurdle in Deep Learning? A Step‑by‑Step Guide
AntTech
AntTech
Dec 22, 2017 · Artificial Intelligence

Transfer Learning: Concepts, Challenges, and Recent Research Highlights from CIKM 2017

This article reviews the key concepts, challenges, and recent research on transfer learning presented at CIKM 2017, covering instance, feature, parameter, and relation‑based methods, supervised and unsupervised deep TL approaches, and transitive transfer learning with associated loss formulations and optimization strategies.

AI researchDeep Learningmachine learning
0 likes · 9 min read
Transfer Learning: Concepts, Challenges, and Recent Research Highlights from CIKM 2017
21CTO
21CTO
Dec 21, 2017 · Artificial Intelligence

How Ordinary Programmers Can Transform Into AI Engineers: Real Success Stories

This article explores whether regular programmers should switch to AI engineering, presents three detailed real‑world transition cases, outlines step‑by‑step learning paths, essential resources, and practical advice for mastering machine learning and deep learning technologies.

AIDeep Learningcareer transition
0 likes · 17 min read
How Ordinary Programmers Can Transform Into AI Engineers: Real Success Stories
AntTech
AntTech
Dec 20, 2017 · Artificial Intelligence

Network Embedding Overview and Recent Research Directions from CIKM 2017

An overview of network embedding presented at CIKM 2017, covering its definition, loss functions, algorithm categories such as spectral methods, random walks, deep learning models, emerging research topics like dynamic and attributed embeddings, and various application scenarios illustrated with numerous academic papers.

CIKM2017Deep Learningattribute integration
0 likes · 9 min read
Network Embedding Overview and Recent Research Directions from CIKM 2017
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 20, 2017 · Artificial Intelligence

How Alibaba Leverages Graph Embedding & Deep Learning for Double 11 Home‑Page Recommendations

This article explains how Alibaba's recommendation team built a large‑scale, AI‑driven personalization pipeline for the Double 11 shopping festival, using graph‑embedding recall, deep‑learning ranking models such as DeepResNet, DCN, and a custom XTensorflow platform to improve coverage, diversity, and click‑through rates.

AIDeep Learninge‑commerce
0 likes · 20 min read
How Alibaba Leverages Graph Embedding & Deep Learning for Double 11 Home‑Page Recommendations
21CTO
21CTO
Dec 19, 2017 · Artificial Intelligence

How Deep Neural Networks Decode Images: From CNNs to RNNs

This article explains the fundamental principles behind deep neural networks for image recognition, covering convolutional and recurrent architectures, their training processes, feature extraction mechanisms, and the emerging ability to generate automatic image captions.

Deep LearningRecurrent Neural Networkconvolutional neural network
0 likes · 13 min read
How Deep Neural Networks Decode Images: From CNNs to RNNs
21CTO
21CTO
Dec 16, 2017 · Artificial Intelligence

Unveiling the Mathematics Behind Deep Learning Success

This article reviews recent research that mathematically explains why deep learning, especially convolutional neural networks, achieve remarkable performance by examining core factors such as architecture, regularization, and optimization, and discusses properties like global optimality, geometric stability, and invariant representations.

Deep LearningGeneralizationNeural Networks
0 likes · 16 min read
Unveiling the Mathematics Behind Deep Learning Success
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 9, 2017 · Artificial Intelligence

How to Train Deeper TensorFlow Models by Optimizing GPU Memory

This article summarizes an NIPS 2017 paper that introduces GPU memory‑optimization techniques—swap‑out/in and a memory‑efficient attention layer—integrated into TensorFlow, enabling significantly larger batch sizes and deeper models without sacrificing accuracy.

Deep LearningGPU memory optimizationNIPS 2017
0 likes · 8 min read
How to Train Deeper TensorFlow Models by Optimizing GPU Memory
21CTO
21CTO
Nov 27, 2017 · Artificial Intelligence

What Hardware and Software Do You Really Need for Deep Learning?

This guide answers common beginner questions about deep learning, covering the essential hardware (especially GPUs and why Nvidia dominates), recommended software libraries, the choice between dynamic and static computation graphs, production considerations, required coding background, and how small datasets can still yield powerful models.

@DataDeep LearningGPU
0 likes · 11 min read
What Hardware and Software Do You Really Need for Deep Learning?
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 25, 2017 · Artificial Intelligence

How Alibaba’s NLP Team Dominated Global Entity Extraction and Chinese Grammar Competitions

Alibaba’s iDST NLP team, led by Dr. Si Luo, clinched the top spot in both the KBP2017 English entity discovery challenge and the 2017 Chinese Grammatical Error Diagnosis contest, showcasing cutting‑edge deep‑learning techniques, massive multilingual processing capacity, and innovative transfer‑learning methods.

AI competitionsAlibabaDeep Learning
0 likes · 9 min read
How Alibaba’s NLP Team Dominated Global Entity Extraction and Chinese Grammar Competitions
Meituan Technology Team
Meituan Technology Team
Nov 23, 2017 · Artificial Intelligence

O2O Machine Learning Applications Seminar

The O2O Machine Learning Applications Seminar, featuring experts from Meituan‑Dianping and Alibaba, explores real‑world ML implementations for online‑to‑offline services, including online learning for search, Alibaba’s Ali Xiaomi intelligent assistant, deep‑learning‑driven recommendation systems, and advertising algorithms such as CTR and CVR optimization, sharing practical insights and best practices.

Deep LearningO2OOnline Learning
0 likes · 5 min read
O2O Machine Learning Applications Seminar
Architects' Tech Alliance
Architects' Tech Alliance
Nov 20, 2017 · Artificial Intelligence

Understanding the Evolution and Differences of AI, Machine Learning, and Deep Learning

This article explains the origins and development of artificial intelligence, clarifies the relationships and distinctions among AI, machine learning, and deep learning, and uses several illustrative diagrams to help readers quickly grasp how these three hot AI technologies are connected and differ from each other.

AIDeep Learningmachine learning
0 likes · 4 min read
Understanding the Evolution and Differences of AI, Machine Learning, and Deep Learning
Tencent Cloud Developer
Tencent Cloud Developer
Nov 17, 2017 · Artificial Intelligence

Heterogeneous Acceleration for Deep Learning: From CPU Limitations to AI Processors

The article explains why general‑purpose CPUs can no longer meet deep‑learning demands due to intrinsic scaling limits and memory‑bandwidth bottlenecks, and surveys how heterogeneous accelerators—GPUs, FPGAs, ASICs and emerging AI processors with high‑bandwidth memory—provide specialized, high‑parallelism, power‑efficient solutions for both cloud and edge workloads.

AI ProcessorsASICCPU
0 likes · 11 min read
Heterogeneous Acceleration for Deep Learning: From CPU Limitations to AI Processors
ITPUB
ITPUB
Nov 17, 2017 · Artificial Intelligence

How RNNs Power Risk Control in O2O Food Delivery: A TensorFlow Case Study

This article explains how Baidu Waimai's risk‑control team uses recurrent neural networks, especially LSTM, within TensorFlow to detect fraudulent merchants and users, compares static and dynamic RNN implementations, demonstrates a MNIST digit‑recognition example, and discusses optimization algorithms and model trade‑offs for real‑time fraud detection.

Deep LearningLSTMMNIST
0 likes · 27 min read
How RNNs Power Risk Control in O2O Food Delivery: A TensorFlow Case Study
MaGe Linux Operations
MaGe Linux Operations
Nov 5, 2017 · Artificial Intelligence

How Deep Learning Transforms Modern Face Recognition: From Basics to DeepFace

This article surveys the evolution of face recognition from traditional image‑based methods to real‑time video processing, highlights key researchers and open‑source projects, explains the four‑stage pipeline, details DeepFace's deep‑learning architecture, and provides practical installation and usage instructions for Python developers.

CNNComputer VisionDatasets
0 likes · 21 min read
How Deep Learning Transforms Modern Face Recognition: From Basics to DeepFace
Ctrip Technology
Ctrip Technology
Nov 3, 2017 · Artificial Intelligence

Intelligent Assistants: Definition, Deep‑Learning NLP Framework, and Applications in Intent Recognition, Knowledge Mining, and QA

This article explains what intelligent assistants are, distinguishes them from simple chatbots, outlines a four‑step deep‑learning NLP framework (Embed‑Encode‑Attend‑Predict), and demonstrates its use in intent recognition, knowledge mining, automatic question answering, and industry deployments.

AIDeep LearningIntelligent Assistant
0 likes · 17 min read
Intelligent Assistants: Definition, Deep‑Learning NLP Framework, and Applications in Intent Recognition, Knowledge Mining, and QA
21CTO
21CTO
Oct 31, 2017 · Artificial Intelligence

Machine Learning vs Deep Learning: Key Differences, Examples, and Future Trends

This article explains the fundamental concepts of machine learning and deep learning, compares their data and hardware dependencies, feature processing, problem‑solving approaches, execution time, and interpretability, and outlines real‑world applications and future development trends.

Data ScienceDeep LearningNeural Networks
0 likes · 13 min read
Machine Learning vs Deep Learning: Key Differences, Examples, and Future Trends
21CTO
21CTO
Oct 19, 2017 · Artificial Intelligence

Why AI Won’t Take Over: Insights from Salesforce’s Chief Scientist Richard Socher

The article profiles Salesforce chief scientist Richard Socher, detailing his journey from founding MetaMind to advancing deep‑learning‑based natural language processing in Einstein AI, while highlighting his views on AI ethics, data bias, and the ongoing debate between humanist and AI‑extinction perspectives.

AI ethicsDeep LearningRichard Socher
0 likes · 4 min read
Why AI Won’t Take Over: Insights from Salesforce’s Chief Scientist Richard Socher
Ctrip Technology
Ctrip Technology
Oct 19, 2017 · Artificial Intelligence

Intelligent Human‑Computer Interaction: Technical Practices of Alibaba’s “Ali Xiaomi” Chatbot

This article presents a comprehensive overview of Alibaba’s intelligent chatbot “Ali Xiaomi”, covering industry context, e‑commerce deployment, NLU architecture, intent‑matching layers, deep‑learning‑based intent classification, reinforcement‑learning‑driven recommendation, knowledge‑graph‑enhanced services, and hybrid retrieval‑generation dialogue models, with future outlooks for AI‑driven interaction.

Deep LearningKnowledge Graphe‑commerce
0 likes · 18 min read
Intelligent Human‑Computer Interaction: Technical Practices of Alibaba’s “Ali Xiaomi” Chatbot
Ctrip Technology
Ctrip Technology
Oct 19, 2017 · Artificial Intelligence

Future Intent Prediction for Chatbots: Architecture, Techniques, and Evaluation

This article presents a comprehensive overview of JD.com’s JIMI chatbot system and introduces a data‑driven future‑intent prediction framework that leverages NLP, deep learning, and clustering to anticipate user questions both before and during a conversation, improving efficiency and user experience.

AIDeep LearningIntent Prediction
0 likes · 9 min read
Future Intent Prediction for Chatbots: Architecture, Techniques, and Evaluation
Architecture Digest
Architecture Digest
Oct 17, 2017 · Artificial Intelligence

Design and Architecture of the Weibo Deep Learning Platform

This article presents the design, architecture, and operational experience of Weibo's deep learning platform, covering its machine‑learning workflow, control center, distributed training cluster, and online prediction service, and explains how the platform accelerates development and improves business outcomes.

AIDeep LearningDistributed Training
0 likes · 17 min read
Design and Architecture of the Weibo Deep Learning Platform
Architects' Tech Alliance
Architects' Tech Alliance
Oct 17, 2017 · Artificial Intelligence

AI Learning Resources and Architecture Overview – A Curated Collection

This article presents a comprehensive collection of AI, machine learning, and deep learning learning materials, including historical overviews, technology architectures, application domains, and numerous downloadable resources such as tutorials, datasets, and code links, aimed at guiding enthusiasts and professionals in intelligent operations.

@DataDeep LearningResources
0 likes · 16 min read
AI Learning Resources and Architecture Overview – A Curated Collection
Meituan Technology Team
Meituan Technology Team
Oct 12, 2017 · Artificial Intelligence

Machine Learning Q&A: Data Imputation, Feature Selection, Recommendation Systems and More

The article answers ten machine‑learning questions, explaining how to impute missing behavior data, extract and select features, describe Meituan‑Dianping’s recommendation pipeline, suggest a beginner learning path, clarify L1 sparsity, recommend TextCNN for text, discuss search‑ranking sample bias, label generation for wide‑deep models, the shift to deep‑learning video detection, and the use of factorization machines for CTR with open‑source examples.

Deep LearningL1 RegularizationRecommendation Systems
0 likes · 7 min read
Machine Learning Q&A: Data Imputation, Feature Selection, Recommendation Systems and More
Architects' Tech Alliance
Architects' Tech Alliance
Oct 10, 2017 · Artificial Intelligence

An Overview of a Three-Day Introductory TensorFlow Tutorial

This article introduces TensorFlow, its origins and capabilities, and summarizes a three‑day hands‑on tutorial covering installation, basic models, convolutional and recurrent neural networks, and practical code examples for deep learning practitioners.

AIDeep LearningTensorFlow
0 likes · 5 min read
An Overview of a Three-Day Introductory TensorFlow Tutorial
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Oct 9, 2017 · Artificial Intelligence

A Pragmatic Roadmap to Master Machine Learning: Courses, Resources, and Tips

The author shares a step‑by‑step self‑learning plan for machine learning, covering essential linear‑algebra refreshers, foundational algorithm courses, hands‑on coding tutorials in MATLAB and Python, advanced deep‑learning studies with CS231n, and a curated list of reference links and GitHub notes.

Deep Learninglinear algebramachine learning
0 likes · 8 min read
A Pragmatic Roadmap to Master Machine Learning: Courses, Resources, and Tips
Architecture Digest
Architecture Digest
Sep 30, 2017 · Artificial Intelligence

Overview of Prominent Deep Learning Architectures for Computer Vision

This article surveys recent progress in deep learning by presenting key computer‑vision architectures such as AlexNet, VGG, GoogleNet, ResNet, ResNeXt, RCNN, YOLO, SqueezeNet, SegNet and GANs, providing brief descriptions, their advantages, and links to original papers and Keras implementations.

Computer VisionDeep LearningKeras
0 likes · 16 min read
Overview of Prominent Deep Learning Architectures for Computer Vision
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 28, 2017 · Artificial Intelligence

How Alipay’s xNN Brings Deep Learning to Millions of Mobile Devices

This article explains how Alipay’s xNN engine overcomes mobile deep‑learning challenges through aggressive model compression, lightweight SDK design, algorithm‑ and instruction‑level optimizations, enabling high‑accuracy AI inference on a wide range of Android and iOS devices with minimal app‑size impact.

AlipayDeep LearningInference Optimization
0 likes · 13 min read
How Alipay’s xNN Brings Deep Learning to Millions of Mobile Devices
Tongcheng Travel Technology Center
Tongcheng Travel Technology Center
Sep 8, 2017 · Artificial Intelligence

Challenges and Techniques in Image Search: Facenet Model and Triplet Loss

The article discusses the evolution of image search engines, outlines key challenges such as image quality, watermarks, speed, and feature extraction, and explains how the Facenet deep‑learning model with Triplet loss can be used to generate compact image embeddings for efficient similarity search.

Computer VisionDeep Learningfacenet
0 likes · 7 min read
Challenges and Techniques in Image Search: Facenet Model and Triplet Loss
Ctrip Technology
Ctrip Technology
Aug 28, 2017 · Artificial Intelligence

Building and Applying Large‑Scale Knowledge Graphs: Construction, Reasoning, and Use Cases

This article examines the construction, reasoning, and large‑scale applications of knowledge graphs, discussing graph building techniques, storage solutions, deep‑learning‑based entity extraction, inference models such as TransR and RESCAL, and how these graphs enhance search, recommendation, and other AI systems.

Deep LearningKnowledge Graphentity recognition
0 likes · 13 min read
Building and Applying Large‑Scale Knowledge Graphs: Construction, Reasoning, and Use Cases
Architecture Digest
Architecture Digest
Aug 21, 2017 · Artificial Intelligence

A Practical Guide for Ordinary Programmers to Enter the AI Field

This article offers a step‑by‑step learning roadmap, resources, and practical advice to help programmers with a bachelor's degree and limited time smoothly transition into artificial intelligence by building foundational knowledge, hands‑on projects, and continued self‑directed study.

AIDeep LearningLearning Path
0 likes · 14 min read
A Practical Guide for Ordinary Programmers to Enter the AI Field
Qunar Tech Salon
Qunar Tech Salon
Aug 16, 2017 · Artificial Intelligence

Applying Wide & Deep Learning to Meituan‑Dianping Recommendation System

This article describes how Meituan‑Dianping leverages deep learning, especially the Wide & Deep model, to improve its recommendation system by addressing business diversity, user context, feature engineering challenges, optimizer and loss function choices, and presents offline and online experimental results showing significant CTR gains.

CTRDeep LearningWide&Deep
0 likes · 22 min read
Applying Wide & Deep Learning to Meituan‑Dianping Recommendation System
MaGe Linux Operations
MaGe Linux Operations
Aug 11, 2017 · Artificial Intelligence

Master Python Machine Learning in 14 Free Steps from Zero to Advanced

This comprehensive guide walks beginners through fourteen free steps to learn Python machine learning, covering installation, core scientific libraries, fundamental and advanced algorithms, ensemble methods, gradient boosting, dimensionality reduction, and deep learning frameworks with curated resources and practical examples.

Deep LearningPythonscikit-learn
0 likes · 24 min read
Master Python Machine Learning in 14 Free Steps from Zero to Advanced
21CTO
21CTO
Aug 9, 2017 · Artificial Intelligence

Andrew Ng’s Journey: From Stanford to Baidu and the Rise of AI Education

The article chronicles Andrew Ng’s life—from his early education and family background to his pioneering roles at Google, Stanford, and Baidu—highlighting his AI breakthroughs, philosophy on learning, and the launch of deeplearning.ai courses that aim to build an AI‑driven society.

AI educationAndrew NgBaidu
0 likes · 12 min read
Andrew Ng’s Journey: From Stanford to Baidu and the Rise of AI Education
21CTO
21CTO
Aug 8, 2017 · Artificial Intelligence

How to Transition from Programmer to Data Scientist: A Practical AI Roadmap

This guide outlines a step‑by‑step learning roadmap for ordinary programmers aiming to become data scientists, covering essential math, statistics, machine learning fundamentals, feature engineering, deep learning resources, open‑source tools, and practical project advice to navigate the AI field effectively.

AIData ScienceDeep Learning
0 likes · 18 min read
How to Transition from Programmer to Data Scientist: A Practical AI Roadmap
21CTO
21CTO
Aug 6, 2017 · Artificial Intelligence

How YouTube’s Recommendation Engine Evolved: From Graph Walks to Deep Neural Networks

This article reviews YouTube’s recommendation system research from 2008 to 2016, detailing four development stages—user‑video graph walks, video‑video graph walks, search‑based methods with collaborative filtering, and deep neural networks—highlighting key algorithms, system architectures, and experimental results.

Deep LearningSearchYouTube
0 likes · 17 min read
How YouTube’s Recommendation Engine Evolved: From Graph Walks to Deep Neural Networks
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 24, 2017 · Artificial Intelligence

How Alibaba’s AI Beats the KITTI Benchmark and Revolutionizes Visual Shopping

Alibaba’s AI breakthroughs—from a foot‑scanning shopping demo that lets a Google engineer instantly find matching shoes, to a record‑setting vehicle detection model on KITTI and world‑leading OCR for real‑time image review—showcase the power and commercial potential of modern computer‑vision research.

AIComputer VisionDeep Learning
0 likes · 5 min read
How Alibaba’s AI Beats the KITTI Benchmark and Revolutionizes Visual Shopping
Tencent Advertising Technology
Tencent Advertising Technology
Jul 13, 2017 · Artificial Intelligence

Insights from the First Tencent Social Advertising University Algorithm Competition: Teams’ Strategies and Experiences

The article summarizes the inaugural Tencent Social Advertising university algorithm contest, highlighting the winning team’s approach, detailed interviews with three top teams, their feature engineering, model choices, challenges faced, and advice for future participants in mobile app conversion rate prediction.

Deep LearningTencentadvertising conversion
0 likes · 17 min read
Insights from the First Tencent Social Advertising University Algorithm Competition: Teams’ Strategies and Experiences
Qunar Tech Salon
Qunar Tech Salon
Jul 10, 2017 · Artificial Intelligence

Qunar Intelligent Service Robot: Architecture, Cognitive System, and Iterative Development

The article details Qunar's development of an AI-powered customer service robot, describing its motivation, data analysis, multi‑phase cognitive system architecture, knowledge‑base management, evaluation mechanisms, and future integration into a group‑wide intelligent service platform to improve service efficiency and reduce costs.

AIChatbotDeep Learning
0 likes · 17 min read
Qunar Intelligent Service Robot: Architecture, Cognitive System, and Iterative Development
21CTO
21CTO
Jul 8, 2017 · Artificial Intelligence

Mastering Recommendation Systems: From Collaborative Filtering to Deep Learning

This article surveys major recommendation system techniques—from collaborative filtering and matrix factorization to clustering and deep‑learning approaches like YouTube’s two‑stage neural network—explaining their principles, strengths, and practical considerations for building effective personalized recommenders.

Deep LearningRecommendation SystemsYouTube
0 likes · 10 min read
Mastering Recommendation Systems: From Collaborative Filtering to Deep Learning
21CTO
21CTO
Jul 5, 2017 · Artificial Intelligence

Can AI Learn to Write Like a Chinese Novelist? Exploring Deep Learning in Literature

This article examines how deep‑learning‑based AI models, from symbolic and statistical NLP methods to Karpathy's recurrent network, progressively learn to generate Chinese wuxia novels, poetry, and web fiction, revealing both their surprising advances and inherent limitations.

AIDeep LearningText Generation
0 likes · 15 min read
Can AI Learn to Write Like a Chinese Novelist? Exploring Deep Learning in Literature
Suning Technology
Suning Technology
Jun 29, 2017 · Artificial Intelligence

How Keyword-Based Scoring Boosts Sentence Similarity for Chatbots

Suning’s Silicon Valley research team presented a novel keyword‑based sentence similarity method at the 9th Web Science conference, highlighting how incorporating keywords, part‑of‑speech, and word position improves chatbot accuracy and efficiency, achieving up to 30% better relevance judgments.

AIChatbotDeep Learning
0 likes · 5 min read
How Keyword-Based Scoring Boosts Sentence Similarity for Chatbots
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 21, 2017 · Artificial Intelligence

How Alibaba’s AI Powers Machine Reading Comprehension in E‑Commerce

Alibaba’s AI assistant “Ali Xiaomì” is exploring machine reading comprehension to automatically understand e‑commerce rules and product information, leveraging deep learning models and datasets such as SQuAD, bAbI, and MCTest, while addressing challenges of long texts, answer granularity, and real‑world deployment.

Deep LearningE-commerce AImachine reading comprehension
0 likes · 18 min read
How Alibaba’s AI Powers Machine Reading Comprehension in E‑Commerce
Qunar Tech Salon
Qunar Tech Salon
May 22, 2017 · Artificial Intelligence

Which Deep Learning Framework Is Best for You?

This article compares the most popular open‑source deep‑learning frameworks—including TensorFlow, Caffe, Caffe2, CNTK, MXNet, Torch, PyTorch, Deeplearning4J and Theano—detailing their origins, key features, strengths, weaknesses, ecosystem support, and the trade‑offs between open‑source and proprietary AI solutions.

AICaffeDeep Learning
0 likes · 13 min read
Which Deep Learning Framework Is Best for You?
Qunar Tech Salon
Qunar Tech Salon
Apr 24, 2017 · Artificial Intelligence

Advances in Image Super-Resolution Using Deep Learning: CNN, GAN, and PixelCNN

Recent advances in image super-resolution leverage deep learning techniques such as convolutional neural networks, residual learning, perceptual loss, generative adversarial networks, and PixelCNN to reconstruct high-resolution details from low-resolution inputs, addressing challenges of scalability, training efficiency, and multi-scale upscaling.

CNNDeep LearningGAN
0 likes · 13 min read
Advances in Image Super-Resolution Using Deep Learning: CNN, GAN, and PixelCNN
MaGe Linux Operations
MaGe Linux Operations
Apr 19, 2017 · Artificial Intelligence

Accelerate TensorFlow Deep Learning with GPU, Multi‑GPU, and Distributed Training

This article explains how to speed up TensorFlow deep‑learning model training by using a single GPU, configuring session parameters, assigning operations to specific devices, employing multi‑GPU parallelism, and leveraging distributed TensorFlow on Kubernetes, while also discussing synchronous versus asynchronous training modes and practical best practices.

Deep LearningDistributed TrainingGPU Acceleration
0 likes · 11 min read
Accelerate TensorFlow Deep Learning with GPU, Multi‑GPU, and Distributed Training
Suning Technology
Suning Technology
Apr 18, 2017 · Artificial Intelligence

How Deep Learning Is Revolutionizing E‑Commerce Search and Chatbots

At the 2017 QCon Beijing conference, Suning’s Silicon Valley Research Institute director Jim demonstrated how deep‑learning techniques can transform e‑commerce by vectorizing product data for smarter search relevance and by combining AI models with limited labeled data to build conversational chat‑bot platforms that understand user intent.

AIChatbotDeep Learning
0 likes · 5 min read
How Deep Learning Is Revolutionizing E‑Commerce Search and Chatbots
21CTO
21CTO
Apr 17, 2017 · Artificial Intelligence

Can Neural Networks Write Other Neural Networks? Inside the Neural Complete Project

Neural Complete, an open‑source project by Pascal van Kooten, trains a neural network to auto‑complete the code of another neural network using LSTM and Keras, demonstrating AI‑driven metaprogramming that could accelerate software development, research, and numerous future applications.

AI programmingDeep LearningNeural Networks
0 likes · 6 min read
Can Neural Networks Write Other Neural Networks? Inside the Neural Complete Project
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 7, 2017 · Artificial Intelligence

How BiCNet Enables Multi‑Agent Cooperation in StarCraft Battles

This article reviews the BiCNet framework, a bidirectional coordination network that lets multiple AI agents learn cooperative strategies in StarCraft micro‑battles, achieving state‑of‑the‑art performance across various combat scenarios and demonstrating broad applicability to real‑world multi‑agent tasks.

BiCNetDeep LearningStarCraft
0 likes · 14 min read
How BiCNet Enables Multi‑Agent Cooperation in StarCraft Battles
Baidu Waimai Technology Team
Baidu Waimai Technology Team
Apr 6, 2017 · Artificial Intelligence

Intelligent Logistics Scheduling System for Food Delivery Using Cloud Computing, Big Data, and Deep Learning

This article describes a cloud‑based intelligent logistics scheduling platform for food‑delivery services that leverages big‑data analytics, deep‑learning prediction models, and visualisation tools to achieve multi‑objective dynamic optimization, improve dispatch efficiency, and enhance user experience across thousands of cities.

AIDeep LearningLogistics
0 likes · 14 min read
Intelligent Logistics Scheduling System for Food Delivery Using Cloud Computing, Big Data, and Deep Learning
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 4, 2017 · Artificial Intelligence

BiCNet: Mastering Multi-Agent Cooperation in StarCraft Battles

The paper introduces BiCNet, a bidirectional coordination network that learns optimal multi‑agent strategies in StarCraft micro‑battles—ranging from collision‑free movement to complex cover attacks and focused fire—outperforming prior state‑of‑the‑art methods and demonstrating scalable potential for real‑world cooperative AI tasks.

BiCNetDeep LearningStarCraft
0 likes · 14 min read
BiCNet: Mastering Multi-Agent Cooperation in StarCraft Battles
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 17, 2017 · Artificial Intelligence

How Improved Latency‑Controlled BLSTM Models Boost Online Speech Recognition Efficiency

This article explains how latency‑controlled BLSTM acoustic models were refined to accelerate online speech recognition while preserving accuracy, detailing the training strategy, computational trade‑offs, and two model enhancements that achieve up to 60% faster decoding with modest resource savings.

Deep LearningLC-BLSTMacoustic modeling
0 likes · 6 min read
How Improved Latency‑Controlled BLSTM Models Boost Online Speech Recognition Efficiency
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 16, 2017 · Artificial Intelligence

How Alibaba Harnesses Deep Reinforcement Learning for E‑Commerce Innovation

This interview with Alibaba researcher Xu Yinghui reveals how the company built large‑scale deep reinforcement learning systems for search, recommendation, logistics and online advertising, detailing team structures, technical breakthroughs, training challenges, and future directions such as multi‑agent learning and GAN integration.

AIAlibabaDeep Learning
0 likes · 20 min read
How Alibaba Harnesses Deep Reinforcement Learning for E‑Commerce Innovation
dbaplus Community
dbaplus Community
Mar 13, 2017 · Artificial Intelligence

Unlocking Tree‑Structured Data: A Deep Dive into Recursive Neural Networks and BPTS

Recursive Neural Networks (RNN) extend deep learning to tree and graph structures, using Back‑Propagation Through Structure (BPTS) for training; this article explains their theory, forward and backward computations, implementation details, code snippets, and applications in natural language and scene parsing, while noting practical challenges.

BPTSDeep LearningRecursive Neural Network
0 likes · 15 min read
Unlocking Tree‑Structured Data: A Deep Dive into Recursive Neural Networks and BPTS
Qunar Tech Salon
Qunar Tech Salon
Mar 12, 2017 · Big Data

Essential Skills and Career Paths for Data Professionals: From Big Data Platforms to AI

The article outlines the key competencies, responsibilities, and career development advice for data professionals across the entire data stack—from building big‑data platforms and data warehouses to visualization, analysis, algorithm engineering, and deep‑learning applications—emphasizing the importance of creating business value with data.

Big DataData AnalystData Warehouse
0 likes · 15 min read
Essential Skills and Career Paths for Data Professionals: From Big Data Platforms to AI