BestHub
Sign in
Tagged articles

Deep Learning

1276 articles · Page 12 of 13
Tencent Cloud Developer
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 GamesAlphaGoDeep Learning
0 likes · 10 min read
Game AI Exploration – From AlphaGo to MOBA Games
MaGe Linux Operations
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.

CNNDeep LearningOpenCV
0 likes · 22 min read
How Deep Learning Transformed Face Recognition: From Images to Real‑Time Video
JD Tech
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 SecurityDeep LearningJD Security
0 likes · 11 min read
Understanding AI Black‑Box Risks and Security: From Adversarial Samples to JD's Explainable AI Solution
AntTech
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.

AlipayDeep LearningText classification
0 likes · 18 min read
Deep Learning Approaches for Text Classification in Alipay Complaint Fraud Detection
DataFunTalk
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.

Deep LearningDistributed Computingmodel serving
0 likes · 15 min read
Machine Learning and Deep Learning Engineering Practices at Ping An Life
Tencent Cloud Developer
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.

AIDeep LearningOCR
0 likes · 19 min read
Overview of OCR Technology and Its Applications on Tencent Cloud
Alibaba Cloud Developer
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
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.

AIDeep LearningMachine Learning
0 likes · 13 min read
How Alibaba’s AI Prediction Platform Boosts Smart Customer Service
Tencent Cloud Developer
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 DetectionDeep Learning
0 likes · 13 min read
Tencent's AI Breast Cancer Screening System: Technical Architecture and Implementation
Alibaba Cloud Developer
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
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
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 accelerationDeep LearningImageNet
0 likes · 24 min read
Four‑Minute ImageNet Training: Tencent’s AI Platform Sets a New World Record
Alibaba Cloud Developer
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
Qunar Tech Salon
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.

Deep LearningImage Moderationcomputer vision
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
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.

Deep Learningattention GRUbrand ranking
0 likes · 27 min read
How Alibaba’s Brand‑Level Ranking Boosts E‑Commerce Clicks with Attention‑GRU
Alibaba Cloud Developer
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 IntelligenceDeep LearningGenerative Models
0 likes · 6 min read
Can Generative Models Boost Visual‑Text Retrieval? Introducing GXN
iQIYI Technical Product Team
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
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.

Deep LearningImage ModerationTransfer Learning
0 likes · 19 min read
AI-Powered Image Moderation at Meituan: Watermark Detection, Celebrity Face Recognition, and Content Filtering
Tencent TDS Service
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.

Deep LearningGPU AccelerationMobileNet
0 likes · 19 min read
How to Engineer MobileNet for Efficient Image Classification on Mobile Devices
Architects' Tech Alliance
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 trendsDeep LearningMachine Learning
0 likes · 13 min read
AI Technology Trends and Reference Architecture Overview
Alibaba Cloud Developer
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.

Deep Learningcomputer visiondilated convolution
0 likes · 7 min read
How Dynamic Scale Selection Boosts Real-Time Action Prediction
Alibaba Cloud Developer
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.

AIAlibabaDeep Learning
0 likes · 18 min read
Machine Reading Comprehension Revolutionizes E‑commerce: Alibaba’s XiaoMi and AI Models
JD Tech
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
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 AlgorithmsDeep LearningE‑commerce
0 likes · 7 min read
How AI Powers Heterogeneous Content Ranking in E‑Commerce Search
Qunar Tech Salon
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.

Deep LearningOpenCVPython
0 likes · 30 min read
Face Recognition with OpenCV, Python, and Deep Learning
Alibaba Cloud Developer
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.

Deep Learningcomputer visioncontext contrast
0 likes · 6 min read
How Context-Contrast Features and Gated Multi‑Scale Fusion Boost Scene Segmentation
Alibaba Cloud Developer
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.

Deep LearningEnsemble MethodsMachine Learning
0 likes · 8 min read
Essential Machine Learning Algorithms Every Beginner Must Know
Meituan Technology Team
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.

AIDeep LearningRanking Models
0 likes · 27 min read
Deep Learning for Text Matching and Ranking at Meituan
JD Tech
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 2018Deep LearningJD AI
0 likes · 5 min read
JD AI Research Institute Wins CVPR 2018 LIP Human Pose Estimation Competition
Alibaba Cloud Developer
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
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.

Deep LearningMachine LearningPython
0 likes · 9 min read
What Data Scientists Chose in 2018: Top AI, ML, and Big Data Tools Revealed
Alibaba Cloud Developer
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.

DFSMNDeep Learningacoustic modeling
0 likes · 17 min read
How DFSMN Sets a New Record in Speech Recognition Accuracy and Speed
Tencent TDS Service
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.

CNNConvolutionDeep Learning
0 likes · 24 min read
Upgrading HED Edge Detection to TensorFlow 1.7: Refactored Code and New Layer Techniques
Baidu Intelligent Testing
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.

Deep LearningPaddlePaddleUI testing
0 likes · 10 min read
Applying Deep Learning for Automated UI Bug Detection in Mobile Apps
Meitu Technology
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.

Deep LearningImageRecordIterMXNet
0 likes · 9 min read
Boost MXNet Video Training Speed by Up to 18× with Rec‑Format I/O
Tencent Advertising Technology
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.

AdvertisingCompetitionDeep Learning
0 likes · 4 min read
Winning Approach of the Tencent Advertising Algorithm Competition: Feature Engineering, Model Selection, and Future Work
High Availability Architecture
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
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.

AIDeep LearningJudea Pearl
0 likes · 11 min read
Why Causal Reasoning Is the Missing Piece for Truly Intelligent AI
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.

Deep LearningFlowNetLucas-Kanade
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.

AICompetitionCustomer Service
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.

Data AugmentationDeep Learningface 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.

CPUCloud ComputingDeep Learning
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.

AIDeep Learningbenchmark
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 Learningdeep forest
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.

AIDeep LearningE‑commerce
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 LearningMachine Learninglarge-scale recommendation
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 LearningLibraries
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 NetworksMachine Learning
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 LearningE‑commerce
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 LearningNatural Language Generation
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 LearningMathematics
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 LearningMachine Learning
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 Learninggeneralizationinformation theory
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.

Deep LearningGPUProduction
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.

Artificial IntelligenceDeep LearningMachine 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.

CNNDeep LearningDeepFace
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.

Deep LearningMachine Learningdata science
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 ethicsArtificial IntelligenceDeep Learning
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 LearningE‑commerceKnowledge Graph
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