Tagged articles

deep learning

1283 articles · Page 12 of 13

Sep 14, 2018 · Artificial Intelligence

Top 6 Notable Trends in Deep Learning and Neural Networks

The article surveys six emerging deep‑learning trends—capsule networks that retain spatial hierarchies, data‑efficient deep reinforcement and transfer learning, supervised models, memory‑augmented architectures such as long‑term and progressive networks, and hybrid Bayesian‑GAN approaches—highlighting how these advances expand AI capabilities beyond traditional fully‑connected networks.

AI TrendsCapsule NetworksHybrid Models

0 likes · 11 min read

Top 6 Notable Trends in Deep Learning and Neural Networks

JD Retail Technology

Sep 12, 2018 · Artificial Intelligence

JD.com Delivery Robots: Advanced Localization, Sensor Fusion, and AI‑Driven Navigation

The article details JD.com’s 3.5‑generation delivery robots, explaining their high‑precision multi‑sensor localization, deep‑learning perception, reinforcement‑learning control, extensive patent portfolio, and future challenges, while also inviting readers to vote for the robots in a national patent competition.

JD.comautonomous navigationdeep learning

0 likes · 7 min read

JD.com Delivery Robots: Advanced Localization, Sensor Fusion, and AI‑Driven Navigation

JD Tech

Sep 12, 2018 · Artificial Intelligence

JD Autonomous Delivery Robots: Technologies, Patents, and Future Challenges

The article details JD's third‑generation autonomous delivery robots, covering their multi‑sensor fusion localization, deep‑learning perception, reinforcement‑learning motion control, extensive patent portfolio, and upcoming technical hurdles such as high‑precision mapping and lidar cost, while also inviting public voting for patent awards.

AI navigationJD Logisticsautonomous robots

0 likes · 8 min read

JD Autonomous Delivery Robots: Technologies, Patents, and Future Challenges

Alibaba Cloud Developer

Sep 11, 2018 · Artificial Intelligence

Rocket Launching: Boosting Real-Time CTR Prediction Without Extra Latency

Online click‑through‑rate (CTR) prediction demands millisecond‑level response times, yet deep models are too slow; this paper introduces a “Rocket Launching” framework that jointly trains a lightweight net and a powerful booster net, sharing parameters and using gradient‑blocking and hint loss to improve accuracy without increasing inference latency.

CTR predictionOnline Advertisingco-training

0 likes · 13 min read

Rocket Launching: Boosting Real-Time CTR Prediction Without Extra Latency

Tencent Cloud Developer

Sep 10, 2018 · Artificial Intelligence

Machine Learning vs. Deep Learning: Differences, Applications, and Future Trends

The article explains that machine learning encompasses a range of algorithms such as decision trees and random forests, while deep learning—a specialized subset using multi‑layer neural networks—requires large data, powerful hardware, and longer training, yet offers superior performance in fields like computer vision, NLP, and medical diagnosis, and both are poised for expanding industrial and research adoption.

ApplicationsArtificial Intelligencecomparison

0 likes · 9 min read

Machine Learning vs. Deep Learning: Differences, Applications, and Future Trends

AntTech

Sep 7, 2018 · Artificial Intelligence

How Alipay Leverages LSTM to Strengthen Mobile Payment Fraud Detection

This article explains how Alipay combats the surge of mobile payment fraud by upgrading its risk‑identification system with deep‑learning techniques, modeling victim and fraudster behavior sequences using LSTM, and integrating the resulting scores into existing models to achieve a measurable increase in detection coverage.

LSTMRisk Modelingbehavior sequence

0 likes · 11 min read

How Alipay Leverages LSTM to Strengthen Mobile Payment Fraud Detection

Alibaba Cloud Developer

Aug 28, 2018 · Artificial Intelligence

Boosting 1688’s “Guess You Like” with Wide‑ResNet and Batch Normalization

This article introduces Wide&Deep, PNN, DeepFM, and a novel Wide‑ResNet model for Alibaba’s 1688 “Guess You Like” recommendation, explains the underlying feature services and real‑time scoring pipeline, presents offline experiments showing AUC gains with batch normalization, and shares practical tuning insights.

AlibabaCTR predictionWide&Deep

0 likes · 13 min read

Boosting 1688’s “Guess You Like” with Wide‑ResNet and Batch Normalization

Tencent Cloud Developer

Aug 21, 2018 · Artificial Intelligence

Game AI Exploration – From AlphaGo to MOBA Games

The talk surveyed game‑AI evolution—from rule‑based systems to AlphaGo‑style reinforcement learning—highlighted industry and academic methods, detailed challenges of applying deep‑learning techniques to MOBA titles like Honor Kings, and proposed a hierarchical, multimodal framework with analysis and execution modules supported by robust simulation environments.

AI in GamesAlphaGoMoBA

0 likes · 10 min read

Game AI Exploration – From AlphaGo to MOBA Games

MaGe Linux Operations

Aug 21, 2018 · Artificial Intelligence

How Deep Learning Transformed Face Recognition: From Images to Real‑Time Video

This article surveys the evolution of face recognition from early statistical methods to modern deep‑learning approaches, outlines key researchers, open‑source projects, popular APIs, core processing steps, the DeepFace architecture, datasets, and experimental results, providing a comprehensive guide for practitioners and researchers.

CNNOpenCVcomputer vision

0 likes · 22 min read

How Deep Learning Transformed Face Recognition: From Images to Real‑Time Video

JD Tech

Aug 20, 2018 · Artificial Intelligence

Understanding AI Black‑Box Risks and Security: From Adversarial Samples to JD's Explainable AI Solution

The article explains how the black‑box nature of deep learning creates security risks such as adversarial attacks, describes real‑world examples in autonomous driving and medical imaging, and showcases JD Security's explainable AI system that demystifies model decisions to improve AI safety and industry adoption.

AI securityJD Securityadversarial examples

0 likes · 11 min read

Understanding AI Black‑Box Risks and Security: From Adversarial Samples to JD's Explainable AI Solution

AntTech

Aug 16, 2018 · Artificial Intelligence

Deep Learning Approaches for Text Classification in Alipay Complaint Fraud Detection

This article reviews deep‑learning‑based text classification techniques—including TextCNN, BiGRU, Capsule Networks, Attention mechanisms, and the novel cw2vec embedding—applied to Alipay complaint fraud data, presents experimental comparisons, and discusses their advantages, challenges, and future directions.

AlipayAttentionText classification

0 likes · 18 min read

Deep Learning Approaches for Text Classification in Alipay Complaint Fraud Detection

DataFunTalk

Aug 14, 2018 · Artificial Intelligence

Machine Learning and Deep Learning Engineering Practices at Ping An Life

The article summarizes senior AI expert Wu Jianjun’s presentation on machine‑learning and deep‑learning engineering at Ping An Life, detailing the company’s big‑data platform, data processing pipelines, model training frameworks, distributed computing strategies, and production model‑serving architecture for financial applications.

Distributed Computingdeep learningmodel serving

0 likes · 15 min read

Machine Learning and Deep Learning Engineering Practices at Ping An Life

Tencent Cloud Developer

Aug 10, 2018 · Artificial Intelligence

Overview of OCR Technology and Its Applications on Tencent Cloud

The talk outlines OCR’s evolution from early postal-code readers to modern deep‑learning models, explains Tencent Cloud’s fast, accurate services for printed and handwritten text—including table‑structured and general OCR—and showcases real‑world applications such as ID cards, business cards, license plates, checks, and medical documents while highlighting ongoing challenges and future enhancements.

AIOCROptical Character Recognition

0 likes · 19 min read

Overview of OCR Technology and Its Applications on Tencent Cloud

Alibaba Cloud Developer

Aug 10, 2018 · Artificial Intelligence

How Multi-Level Similarity‑Aware CNN Boosts Person Re‑Identification Accuracy

This article reviews a 2017 ACM MM paper that introduces a multi‑level similarity‑aware CNN (MSP‑CNN) for person re‑identification, detailing its siamese architecture, dual similarity constraints, multi‑task training, experimental results on CUHK03, Market‑1501 and CUHK01, and its advantages for large‑scale deployment.

CNNdeep learningmulti-task learning

0 likes · 16 min read

How Multi-Level Similarity‑Aware CNN Boosts Person Re‑Identification Accuracy

Alibaba Cloud Developer

Aug 8, 2018 · Artificial Intelligence

How Alibaba’s AI Prediction Platform Boosts Smart Customer Service

The article describes Alibaba’s AI‑driven prediction platform for its smart‑customer‑service bots, detailing background, order and issue prediction capabilities, deployed products, underlying algorithms such as DeepFM, DCN, reinforcement learning, streaming computation, and the platform’s modular architecture that enables scalable, automated model management.

AIPlatformdeep learning

0 likes · 13 min read

How Alibaba’s AI Prediction Platform Boosts Smart Customer Service

Tencent Cloud Developer

Aug 6, 2018 · Artificial Intelligence

Tencent's AI Breast Cancer Screening System: Technical Architecture and Implementation

Tencent's AI Breast System combines mammography, pathology, MRI and ultrasound analysis using a multi‑scale, progressive TMuNet model that processes four views, learns from physician feedback, and delivers lesion localization, malignancy scoring and automated reports, achieving up to 92% sensitivity and reducing annotation time.

AI Medical ImagingBreast Cancer DetectionHealthcare AI

0 likes · 13 min read

Tencent's AI Breast Cancer Screening System: Technical Architecture and Implementation

Alibaba Cloud Developer

Aug 2, 2018 · Artificial Intelligence

Style‑Adversarial Autoencoder Enables Precise Content‑Style Image Generation

This paper introduces a style‑adversarial autoencoder that separates content and style latent variables, uses a multi‑class discriminator, and demonstrates superior image generation and data augmentation across MNIST, face, and text datasets tasks.

Generative Adversarial Networksautoencodersdeep learning

0 likes · 15 min read

Style‑Adversarial Autoencoder Enables Precise Content‑Style Image Generation

Alibaba Cloud Developer

Aug 1, 2018 · Artificial Intelligence

How Spatio‑Temporal Autoencoders Detect Anomalies in Real‑World Traffic Video

This paper introduces a spatio‑temporal autoencoder that uses 3D convolutions and a weight‑decaying prediction loss to automatically learn video representations for detecting abnormal events in real‑world traffic surveillance, outperforming previous methods on public benchmarks and a newly collected traffic dataset.

3D convolutiondeep learningspatio-temporal autoencoder

0 likes · 15 min read

How Spatio‑Temporal Autoencoders Detect Anomalies in Real‑World Traffic Video

Alibaba Cloud Developer

Jul 31, 2018 · Artificial Intelligence

How Multi-Level Similarity‑Aware CNN Boosts Person Re‑Identification

This paper introduces a novel multi‑level similarity‑aware CNN (MSP‑CNN) for person re‑identification, applying distinct similarity constraints to low‑ and high‑level feature maps, integrating classification and similarity losses in a multitask framework, and demonstrating superior performance on CUHK03, CUHK01 and Market‑1501 benchmarks.

CNNdeep learningmulti-task learning

0 likes · 15 min read

How Multi-Level Similarity‑Aware CNN Boosts Person Re‑Identification

Tencent Architect

Jul 30, 2018 · Artificial Intelligence

Four‑Minute ImageNet Training: Tencent’s AI Platform Sets a New World Record

Tencent’s intelligent machine‑learning platform achieved a world‑record by training AlexNet in 4 minutes and ResNet‑50 in 6.6 minutes on ImageNet, using large batch sizes, mixed‑precision, LARS optimization, hierarchical synchronization, gradient fusion, and pipeline I/O techniques to overcome accuracy and scalability challenges.

AI accelerationImageNetdeep learning

0 likes · 24 min read

Four‑Minute ImageNet Training: Tencent’s AI Platform Sets a New World Record

Alibaba Cloud Developer

Jul 26, 2018 · Artificial Intelligence

How Alibaba’s DiDa Platform Uses AI to Automate Fashion Outfit Matching

Alibaba’s DiDa platform leverages deep learning models—including CNN, LSTM, and DAN—to automatically generate fashion outfit pairings and descriptive copy, integrating a context graph for business rules, supporting real‑time personalization across e‑commerce, and demonstrating significant efficiency and CTR gains.

AIdeep learninge-commerce

0 likes · 21 min read

How Alibaba’s DiDa Platform Uses AI to Automate Fashion Outfit Matching

JD Retail Technology

Jul 25, 2018 · Artificial Intelligence

JD.com Infrastructure Team Wins SIGIR 2018 E‑commerce Product Categorization Competition

The JD.com Infrastructure team won the SIGIR 2018 e‑commerce competition by developing a multi‑level deep‑learning model that tackled severe class imbalance, vocabulary drift, and hierarchical labeling, boosting F1 from 0.74 to 0.84 and earning paper acceptance at the SIGIR conference.

JD.comSIGIRdeep learning

0 likes · 5 min read

JD.com Infrastructure Team Wins SIGIR 2018 E‑commerce Product Categorization Competition

Qunar Tech Salon

Jul 24, 2018 · Artificial Intelligence

Meituan's AI-Powered Image Intelligent Review System: Watermark Detection, Celebrity Face Recognition, Pornography Detection, and Scene Classification

This article describes Meituan's large‑scale AI‑driven image moderation platform, detailing deep‑learning based watermark detection, celebrity face recognition, pornographic image detection, and scene classification techniques, along with system architecture, data preparation, model evaluation, and deployment considerations.

Image Moderationcomputer visiondeep learning

0 likes · 19 min read

Meituan's AI-Powered Image Intelligent Review System: Watermark Detection, Celebrity Face Recognition, Pornography Detection, and Scene Classification

Alibaba Cloud Developer

Jul 20, 2018 · Artificial Intelligence

How Alibaba’s Brand‑Level Ranking Boosts E‑Commerce Clicks with Attention‑GRU

This article presents Alibaba’s first brand‑level ranking system that personalizes product ordering by modeling user brand preferences with an enhanced Attention‑GRU, detailing feature engineering, model improvements, extensive offline experiments on a massive Tmall dataset, and a successful online A/B test that increased CTR, ATIP, and GMV.

attention GRUbrand rankingdeep learning

0 likes · 27 min read

Alibaba Cloud Developer

Jul 19, 2018 · Artificial Intelligence

Can Generative Models Boost Visual‑Text Retrieval? Introducing GXN

This paper presents GXN, a generative cross‑modal feature learning framework that enhances image‑text retrieval by incorporating both high‑level semantic similarity and fine‑grained local matching through a three‑step Look‑Imagine‑Match process, achieving state‑of‑the‑art results on MSCOCO and Flickr30K.

Artificial IntelligenceGenerative Modelscross-modal retrieval

0 likes · 6 min read

Can Generative Models Boost Visual‑Text Retrieval? Introducing GXN

iQIYI Technical Product Team

Jul 16, 2018 · Artificial Intelligence

AVS Deep Learning Video Coding Loop Filter Challenge Announcement

The AVS Working Group, backed by iQIYI, has launched the Deep Learning Based Video Coding Loop Filter Challenge to spur research on AI‑driven loop filters that can cut bitrate by over 15%, inviting participants to submit Caffe models on the TAVS3 platform with ongoing benchmarking and potential prizes, while encouraging contributions to the upcoming AVS video coding standard.

AIAVSCaffe

0 likes · 5 min read

AVS Deep Learning Video Coding Loop Filter Challenge Announcement

Meituan Technology Team

Jul 12, 2018 · Artificial Intelligence

AI-Powered Image Moderation at Meituan: Watermark Detection, Celebrity Face Recognition, and Content Filtering

Meituan employs AI-driven image moderation across millions of daily uploads, using an SSD‑ResNet detector for watermarks, a multi‑scale Faster R‑CNN ensemble for celebrity faces, a CNN‑based pornographic classifier with incremental learning, and transfer‑learned scene classification, while routing uncertain cases to human reviewers.

Image Moderationcontent filteringdeep learning

0 likes · 19 min read

AI-Powered Image Moderation at Meituan: Watermark Detection, Celebrity Face Recognition, and Content Filtering

Tencent TDS Service

Jul 12, 2018 · Artificial Intelligence

How to Engineer MobileNet for Efficient Image Classification on Mobile Devices

This article details the engineering of MobileNet V1 for image classification on mobile terminals, covering its depthwise separable convolution architecture, data collection and preprocessing, model training with transfer learning, TensorFlow Lite conversion, deployment on iOS/Android, and GPU acceleration techniques for faster inference.

GPU accelerationMobileNetTensorFlow Lite

0 likes · 19 min read

How to Engineer MobileNet for Efficient Image Classification on Mobile Devices

Architects' Tech Alliance

Jul 11, 2018 · Artificial Intelligence

AI Technology Trends and Reference Architecture Overview

The article reviews the evolution of artificial intelligence, presents a comprehensive AI reference framework based on roles, activities and functions, explains the intelligent information chain and IT value chain, and details current AI technology trends such as machine learning, deep learning, transfer learning, active learning and evolutionary learning, while also noting talent shortages and promoting an AI education course.

AI TrendsTalent Shortagedeep learning

0 likes · 13 min read

AI Technology Trends and Reference Architecture Overview

Alibaba Cloud Developer

Jul 6, 2018 · Artificial Intelligence

How Dynamic Scale Selection Boosts Real-Time Action Prediction

This article explains online action prediction, the challenges of early‑stage classification, and introduces a Scale Selection Network that dynamically chooses optimal temporal windows using dilated convolutions, regression and classification sub‑networks, achieving state‑of‑the‑art results on two benchmark datasets.

computer visiondeep learningdilated convolution

0 likes · 7 min read

How Dynamic Scale Selection Boosts Real-Time Action Prediction

Alibaba Cloud Developer

Jul 4, 2018 · Artificial Intelligence

Machine Reading Comprehension Revolutionizes E‑commerce: Alibaba’s XiaoMi and AI Models

This article reviews the background of Alibaba's XiaoMi chatbot, explores how machine reading comprehension can be applied to e‑commerce scenarios such as rule interpretation and product inquiries, surveys key datasets and SQuAD‑based models, and discusses practical challenges and solutions for deploying these technologies in real‑world business environments.

AIAlibabaNatural Language Processing

0 likes · 18 min read

Machine Reading Comprehension Revolutionizes E‑commerce: Alibaba’s XiaoMi and AI Models

JD Tech

Jun 29, 2018 · Artificial Intelligence

JD AI's JDAI-Face: Real-Time Multi-Task Facial Attribute Recognition System

The article introduces JD AI's JDAI-Face system, a deep‑learning based real‑time multi‑task facial attribute recognition platform that detects gender, age, ethnicity, expression and attractiveness, outlines its technical pipeline, showcases retail applications, and cites recent academic publications and expert contributors.

deep learningface recognitionmultitask learning

0 likes · 12 min read

JD AI's JDAI-Face: Real-Time Multi-Task Facial Attribute Recognition System

Alibaba Cloud Developer

Jun 29, 2018 · Artificial Intelligence

How AI Powers Heterogeneous Content Ranking in E‑Commerce Search

This paper addresses the challenge of ranking heterogeneous data in e‑commerce by proposing two algorithms—a multi‑armed bandit approach and a personalized Markov deep neural network—to select and order content streams, demonstrating superior performance over baseline models in A/B tests.

Bandit Algorithmscontent rankingdeep learning

0 likes · 7 min read

How AI Powers Heterogeneous Content Ranking in E‑Commerce Search

Qunar Tech Salon

Jun 29, 2018 · Artificial Intelligence

Face Recognition with OpenCV, Python, and Deep Learning

This tutorial explains how to implement high‑accuracy face recognition using OpenCV, Python, and deep learning by leveraging dlib's deep metric learning, creating a custom dataset, encoding facial embeddings, and performing real‑time identification on images and video streams.

OpenCVPythoncomputer vision

0 likes · 30 min read

Face Recognition with OpenCV, Python, and Deep Learning

Alibaba Cloud Developer

Jun 27, 2018 · Artificial Intelligence

How Context-Contrast Features and Gated Multi‑Scale Fusion Boost Scene Segmentation

The paper introduces a context‑contrast local feature and a gated multi‑scale fusion mechanism that together enhance pixel‑level scene segmentation, especially for inconspicuous objects, and validates the approach with state‑of‑the‑art results on Pascal Context, SUN‑RGBD, and COCO Stuff datasets.

computer visioncontext contrastdeep learning

0 likes · 6 min read

How Context-Contrast Features and Gated Multi‑Scale Fusion Boost Scene Segmentation

Alibaba Cloud Developer

Jun 22, 2018 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

This beginner-friendly guide walks through core machine‑learning concepts—from data organization and feature design to supervised and unsupervised algorithms such as perceptron, logistic regression, decision trees, LDA, and ensemble techniques—while explaining model evaluation, overfitting, and practical tuning strategies.

Ensemble MethodsUnsupervised Learningdeep learning

0 likes · 8 min read

Essential Machine Learning Algorithms Every Beginner Must Know

Meituan Technology Team

Jun 21, 2018 · Artificial Intelligence

Deep Learning for Text Matching and Ranking at Meituan

Meituan leverages deep‑learning models such as Word2Vec, DSSM, and LSTM‑based encoders within its ClickNet framework to compute text similarity and rank results, integrating rich business features like user location and merchant rating, thereby surpassing traditional TF‑IDF, BM25, and XGBoost approaches and boosting click‑through rates and revenue.

AIRanking Modelsdeep learning

0 likes · 27 min read

Deep Learning for Text Matching and Ranking at Meituan

JD Tech

Jun 15, 2018 · Artificial Intelligence

JD AI Research Institute Wins CVPR 2018 LIP Human Pose Estimation Competition

In June 2018, JD AI Research Institute's Computer Vision and Multimedia Lab won both the single‑person and multi‑person tracks of the CVPR 2018 Look Into Person (LIP) competition, achieving 90.9% and 72.2% accuracy respectively through enhanced multi‑scale CNN models, data augmentation, and focal loss techniques.

CVPR 2018JD AIdeep learning

0 likes · 5 min read

JD AI Research Institute Wins CVPR 2018 LIP Human Pose Estimation Competition

Alibaba Cloud Developer

Jun 15, 2018 · Mobile Development

How Alipay’s xNN Engine Brings Deep Learning to Mobile Apps

This article explains how Alipay’s xNN deep‑learning engine tackles the challenges of deploying AI on billions of mobile devices by using aggressive model compression, a lightweight SDK, and joint algorithm‑ and instruction‑level optimizations to achieve high accuracy, tiny package size, and real‑time performance.

Alipaydeep learningmobile AI

0 likes · 10 min read

How Alipay’s xNN Engine Brings Deep Learning to Mobile Apps

21CTO

Jun 14, 2018 · Artificial Intelligence

What Data Scientists Chose in 2018: Top AI, ML, and Big Data Tools Revealed

The 2018 KDnuggets survey of over 2,000 data‑science professionals shows Python dominating with 66% usage, R dropping below 50%, TensorFlow leading deep‑learning frameworks, RapidMiner gaining traction, SQL remaining stable, Hadoop declining, and regional participation shifting toward Europe.

Data SciencePythondeep learning

0 likes · 9 min read

What Data Scientists Chose in 2018: Top AI, ML, and Big Data Tools Revealed

Alibaba Cloud Developer

Jun 8, 2018 · Artificial Intelligence

How Alibaba’s DFSMN Model Pushes Speech Recognition Accuracy to 96.04%

Alibaba’s DAMO Academy unveiled the DFSMN speech‑recognition model, open‑sourced on GitHub, which sets a new 96.04% accuracy record on LibriSpeech, trains three times faster than LSTM, and powers real‑world demos like AI cashiers and metro ticket machines.

AIAlibabaDFSMN

0 likes · 3 min read

How Alibaba’s DFSMN Model Pushes Speech Recognition Accuracy to 96.04%

Alibaba Cloud Developer

Jun 8, 2018 · Artificial Intelligence

How DFSMN Sets a New Record in Speech Recognition Accuracy and Speed

Alibaba's DAMO Academy has open‑sourced the Deep‑Feedforward Sequential Memory Network (DFSMN), a next‑generation speech‑recognition model that achieves a world‑record 96.04% accuracy on LibriSpeech, trains three times faster than LSTM, halves model size, and dramatically speeds up real‑time decoding.

DFSMNacoustic modelingdeep learning

0 likes · 17 min read

How DFSMN Sets a New Record in Speech Recognition Accuracy and Speed

Tencent TDS Service

Jun 7, 2018 · Artificial Intelligence

Upgrading HED Edge Detection to TensorFlow 1.7: Refactored Code and New Layer Techniques

This tutorial walks through rewriting the HED edge‑detection network for TensorFlow 1.7, covering deprecated API fixes, migration from TF‑Slim to tf.layers, matrix initialization, batch normalization nuances, and a comprehensive review of convolution variants such as 1×1, depthwise, separable, and dilated convolutions, plus guidance on transposed convolutions and modern architectures like ResNet and Inception.

CNNConvolutionEdge Detection

0 likes · 24 min read

Upgrading HED Edge Detection to TensorFlow 1.7: Refactored Code and New Layer Techniques

Baidu Intelligent Testing

Jun 5, 2018 · Artificial Intelligence

Applying Deep Learning for Automated UI Bug Detection in Mobile Apps

To address the rising cost of manual UI testing on diverse mobile devices, the article presents a deep‑learning‑based solution using PaddlePaddle that automatically detects UI style bugs such as misaligned controls, text overlap, and blank spaces through data‑driven model training, image preprocessing, and classification.

PaddlePaddleUI testingautomation

0 likes · 10 min read

Applying Deep Learning for Automated UI Bug Detection in Mobile Apps

Meitu Technology

May 29, 2018 · Artificial Intelligence

Boost MXNet Video Training Speed by Up to 18× with Rec‑Format I/O

This article analyzes MXNet's lack of native video I/O, compares existing image iterators, introduces a Rec‑format based video iterator, and demonstrates through single‑GPU and multi‑GPU experiments that the new approach can accelerate training by up to eighteen times.

ImageRecordIterMXNetPerformance Optimization

0 likes · 9 min read

Boost MXNet Video Training Speed by Up to 18× with Rec‑Format I/O

Tencent Advertising Technology

May 28, 2018 · Artificial Intelligence

Winning Approach of the Tencent Advertising Algorithm Competition: Feature Engineering, Model Selection, and Future Work

The team from Jilin University, Harbin Institute of Technology, and Beijing University of Posts and Telecommunications shares their winning strategy for the Tencent Advertising Algorithm Competition, detailing their feature engineering, model selection, and future work to handle large‑scale data challenges.

AdvertisingCompetitionTencent

0 likes · 4 min read

Winning Approach of the Tencent Advertising Algorithm Competition: Feature Engineering, Model Selection, and Future Work

High Availability Architecture

May 28, 2018 · Artificial Intelligence

Interview with GIAC AI Forum Lecturer Long Mingkang on Building AI Platforms, Speech Recognition Challenges, and Future AI Trends

In this interview, Long Mingkang, Vice President of iFlytek's Cloud Computing Institute, shares his experience building large‑scale speech cloud services, discusses the technical hurdles of speech recognition and AI platform development, compares TensorFlow and MXNet, and offers insights on AutoML, industry trends, and how engineers can master AI.

AIAI platformsAutoML

0 likes · 13 min read

Interview with GIAC AI Forum Lecturer Long Mingkang on Building AI Platforms, Speech Recognition Challenges, and Future AI Trends

21CTO

May 20, 2018 · Artificial Intelligence

Why Causal Reasoning Is the Missing Piece for Truly Intelligent AI

Judea Pearl, the 2011 Turing Award laureate, argues that modern AI is stuck in curve‑fitting and that true intelligence requires machines to understand cause and effect, a perspective he expands on through a series of insightful interview questions and answers.

AIJudea Pearlcausality

0 likes · 11 min read

Why Causal Reasoning Is the Missing Piece for Truly Intelligent AI

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.

FlowNetLucas-Kanadecomputer vision

0 likes · 14 min read

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

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.

AICompetitionCustomer Service

0 likes · 14 min read

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

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

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

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.

Data Augmentationdeep learningface recognition

0 likes · 18 min read

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

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 NLPUnsupervised Learningcw2vec

0 likes · 14 min read

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

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.

CPUCloud ComputingFPGA

0 likes · 9 min read

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

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

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 forestdeep learning

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

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.

AIautomationbanner design

0 likes · 17 min read

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

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 predictionLarge‑Scale Training

0 likes · 16 min read

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

Huawei Cloud Developer Alliance

Apr 4, 2018 · Artificial Intelligence

12 Must‑Read Deep Learning Books to Jumpstart Your AI Journey

This article curates a ranked list of twelve deep‑learning books, from beginner‑friendly guides to comprehensive textbooks, describing each title’s focus, strengths, and ideal audience, helping readers choose the right resource to start or deepen their AI knowledge.

Artificial IntelligenceBooksPython

0 likes · 14 min read

12 Must‑Read Deep Learning Books to Jumpstart Your AI Journey

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.

Hotel Industrydeep learning

0 likes · 18 min read

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

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 learningfeature engineeringmulti-task learning

0 likes · 16 min read

Deep Learning Model Applications and Optimizations for Recommendation Ranking at Meituan

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.

CTRCVRclick-through rate

0 likes · 8 min read

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

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

AntTech

Mar 23, 2018 · Artificial Intelligence

Technical Overview of Alipay's 2018 Spring Festival “Scan Fu” Image Recognition System

The article details Alipay's 2018 Spring Festival "Scan Fu" initiative, describing the challenges of high‑volume Chinese character detection, the client‑server architecture, the lightweight xFuNet deep‑learning model, training strategies, performance results, and future AR extensions.

Alipayclient‑serverdeep learning

0 likes · 9 min read

Technical Overview of Alipay's 2018 Spring Festival “Scan Fu” Image Recognition System

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 DetectionNLPText classification

0 likes · 10 min read

Abusive Comment Detection Using TextCNN: A Strategy + Algorithm Approach

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.

AILibrariesPython

0 likes · 5 min read

Essential Cheat Sheets for Machine Learning and Deep Learning Researchers

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.

AnacondaCNNMNIST

0 likes · 21 min read

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

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 MechanismPCNNdeep learning

0 likes · 14 min read

How Deep Learning Transforms Knowledge Graph Relation Extraction

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.

Formal VerificationHoare logicblockchain

0 likes · 12 min read

Why Fully Automated Formal Verification of Smart Contracts Is Impossible

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.

Generative Adversarial NetworksWGANWasserstein distance

0 likes · 11 min read

Understanding WGANs: From GAN Pitfalls to Wasserstein Solutions

Baobao Algorithm Notes

Feb 28, 2018 · Artificial Intelligence

Mastering Text Classification: From TF‑IDF to Word Embeddings and Deep Learning

This article provides a comprehensive guide to text classification, covering traditional pipelines, bag‑of‑words and TF‑IDF features, dimensionality‑reduction techniques, word‑embedding models such as GloVe, word2vec and fastText, and modern deep‑learning architectures like CNN, RCNN and HAN.

CNNNLPRNN

0 likes · 9 min read

Mastering Text Classification: From TF‑IDF to Word Embeddings and Deep Learning

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.

ARaugmented realitycloud recognition

0 likes · 8 min read

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

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.

AIautoencodersdeep learning

0 likes · 17 min read

Why Deep Learning Is Revolutionizing Recommendation Systems

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 architecturesCNNRNN

0 likes · 23 min read

Eight Neural Network Architectures Every Machine Learning Researcher Should Know

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.

Word2Vecautoencodersdeep learning

0 likes · 17 min read

Deep Learning Applications in Recommendation Systems

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.

GaNGenerative Adversarial NetworksMinimax Game

0 likes · 11 min read

Understanding GANs: Theory, Minimax Game, and Training Challenges

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 2018Telepathdeep learning

0 likes · 13 min read

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

Hulu Beijing

Jan 25, 2018 · Artificial Intelligence

How Batch Normalization Accelerates Neural Network Training and Improves Generalization

This article explains the motivation, core principles, and implementation details of Batch Normalization, including how it normalizes each mini‑batch, restores learned feature distributions, and is applied in convolutional neural networks to speed up training and boost model generalization.

CNNbatch normalizationdeep learning

0 likes · 6 min read

How Batch Normalization Accelerates Neural Network Training and Improves Generalization

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.

AINatural Language Generationdeep learning

0 likes · 20 min read

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

Architecture Digest

Jan 18, 2018 · Artificial Intelligence

Personalized Recommendation System Practices at Ctrip: Data, Recall, Ranking, and Feature Engineering

The article details Ctrip's large‑scale personalized recommendation system, covering data sources, recall strategies, ranking models, feature engineering techniques, and practical insights from industry research to improve user experience in travel services.

Ctripdeep learningfeature engineering

0 likes · 16 min read

Personalized Recommendation System Practices at Ctrip: Data, Recall, Ranking, and Feature Engineering

Ctrip Technology

Jan 11, 2018 · Artificial Intelligence

Ctrip's Personalized Recommendation System: Data, Recall, and Ranking Practices

This article details Ctrip's large‑scale personalized recommendation system, covering the data sources, recall strategies, ranking models, feature engineering techniques, and future directions for improving recommendation quality in the travel domain.

Recalldeep learningpersonalization

0 likes · 19 min read

Ctrip's Personalized Recommendation System: Data, Recall, and Ranking Practices

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.

AISearch Optimizationdeep learning

0 likes · 17 min read

How Alibaba Leverages Deep Learning to Revolutionize E‑Commerce Search

AI Large-Model Wave and Transformation Guide

Dec 27, 2017 · Fundamentals

Essential Math Topics Every Deep Learning Engineer Should Master

The article outlines key mathematical areas—probability & statistics, linear algebra, calculus, information theory, and convex optimization—essential for deep learning, and recommends specific lecture notes and textbooks for each topic to guide learners toward effective study.

calculusconvex optimizationdeep learning

0 likes · 4 min read

Essential Math Topics Every Deep Learning Engineer Should Master

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 FundamentalsOptimizationdeep learning

0 likes · 50 min read

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

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

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.

AICareer Transitiondeep learning

0 likes · 17 min read

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

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.

CIKM2017attribute integrationdeep learning

0 likes · 9 min read

Network Embedding Overview and Recent Research Directions from CIKM 2017

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

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.

Recurrent Neural Networkconvolutional neural networkdeep learning

0 likes · 13 min read

How Deep Neural Networks Decode Images: From CNNs to RNNs

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 learninggeneralizationinformation theory

0 likes · 16 min read

Unveiling the Mathematics Behind Deep Learning Success

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.

GPU memory optimizationNIPS 2017TensorFlow

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How to Train Deeper TensorFlow Models by Optimizing GPU Memory

Hulu Beijing

Dec 7, 2017 · Artificial Intelligence

How Recurrent Neural Networks Generate Text Representations and Tackle Gradient Problems

This article explains what Recurrent Neural Networks are, how they create text representations, why they suffer from gradient vanishing or explosion, and what architectural improvements such as LSTM, GRU, residual connections and gradient clipping can do to overcome these issues.

Gradient ExplosionLSTMRNN

0 likes · 9 min read

How Recurrent Neural Networks Generate Text Representations and Tackle Gradient Problems

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

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 competitionsAlibabaNLP

0 likes · 9 min read

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

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.

Artificial IntelligenceO2Oadvertising algorithms

0 likes · 5 min read

O2O Machine Learning Applications Seminar

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.

AITechnologydeep learning

0 likes · 4 min read

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

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

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.

LSTMMNISTOptimization

0 likes · 27 min read

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

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.

CNNDeepFacePython

0 likes · 21 min read

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