This article introduces Grammatical Error Correction (GEC), explains its main error categories, outlines four key challenges, reviews evaluation metrics and the evolution of NLP approaches, and showcases practical solutions and product applications developed by Liulishuo.
At QCon Beijing 2019, four Beike technology experts presented the practical use and challenges of machine learning for user profiling, deep‑learning‑based house‑quality scoring, intelligent customer‑service systems, and AI‑driven floor‑plan generation, summarizing the architecture, data pipelines, model evolution, and future improvement directions.
This article explains the various roles of data scientists, compares data science with machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics, and clarifies the distinctions and overlaps among these fields.
The Youku Video Enhancement and Super‑Resolution Competition challenges teams of up to five to develop 4× upscaling models that also remove noise and compression artifacts, using a 10,000‑pair dataset, with prizes up to ¥100,000 and recruitment opportunities, running from May to September 2019.
This article explains the historical development, definitions, and relationship between artificial intelligence and machine learning, highlighting key milestones, foundational concepts such as deep learning and neural networks, and their modern applications across industries.
This article walks beginners through the fundamentals of artificial intelligence, machine learning, and deep learning, using a real‑world attendance dataset to illustrate how to label data, construct a simple linear model, and expand it into a neural network for workday prediction.
This article explains how MXNet supports data‑parallel training on single‑machine multi‑GPU and multi‑machine multi‑GPU setups, describes KVStore modes, outlines the worker‑server‑scheduler architecture, and shows how to launch large‑scale distributed training using Kubeflow and the mxnet‑operator.
Alibaba’s open‑source MNN is a lightweight, high‑performance deep‑learning inference engine optimized for edge devices, supporting multiple model formats and backends, offering portability across iOS, Android, and IoT, with detailed architecture, performance benchmarks, roadmap, and real‑world application examples.
The article details Meituan's large‑scale O2O search platform, describing its current coverage, challenges such as heterogeneous POI data and user intent diversity, and the deep‑learning‑driven solutions—including intelligent matching, business recognition, component analysis, semantic models, real‑time features, and future personalization directions.
This article reviews why deep neural networks are over‑parameterized, outlines the challenges of deploying them on mobile and embedded devices, and presents six major strategies—pruning, low‑rank approximation, filter selection, quantization, knowledge distillation, and novel architecture design—to accelerate and compress models while preserving performance.
Ren Haibing’s Youku Cognitive Lab talk reviews object segmentation’s motivation, explains semantic and instance concepts, presents UNet‑based and category‑agnostic methods—including fast video segmentation with motion cues—and reports high IoU results while outlining future edge‑aware, label‑free, and non‑online video segmentation research directions.
This article explains the fundamental differences between CPUs and GPUs, outlines key GPU specifications such as CUDA cores, memory capacity, bandwidth, and floating‑point precision, and reviews NVIDIA's major GPU series and architectural evolution for high‑performance and AI workloads.
This article reviews the evolution of autonomous‑driving research, explains how to design end‑to‑end deep‑neural‑network models such as PilotNet, and outlines a reinforcement‑learning based decision system, highlighting key architectures, performance metrics, and future challenges.
This article provides a beginner-friendly overview of artificial intelligence, explaining its core concepts, the relationship between AI, machine learning and deep learning, common real-world applications such as search and recommendation, and practical steps and resources for newcomers to start learning AI with Python and basic statistics.
The article details a practical workflow for Chinese sentiment classification in Tencent’s Goose Man product, covering data preparation, word‑segmentation challenges, a six‑layer multi‑LSTM architecture with word embeddings, training results achieving roughly 96 % accuracy, and its deployment for automatic detection of misleading and high‑impact user reviews.
This article reviews the fundamentals of automatic speech recognition, compares traditional MFCC‑GMM‑HMM pipelines with modern deep neural network approaches such as DNN‑HMM, LSTM‑CTC, and attention‑based models, and illustrates each evolution step with flowchart diagrams and key references.
This article outlines the end‑to‑end text‑mining workflow—from data acquisition and preprocessing to feature extraction, algorithm selection, and model evaluation—while demonstrating a sentiment‑analysis case study that combines LDA topic modeling with deep‑learning classifiers.
This article reviews the evolution of click‑through‑rate (CTR) estimation models for recommendation ranking, covering logistic regression, feature‑engineering tricks, factorization machines, deep neural networks, wide‑and‑deep architectures, and the Deep & Cross Network, while discussing their strengths, limitations, and future research directions.
This article summarizes a computer‑vision salon where Dr. Ji Yongnan explains imaging pipelines, traditional feature‑based methods, deep‑learning breakthroughs, Tencent Cloud AI services, real‑world case studies, and answers audience questions about machine‑vision versus computer‑vision and data‑scarcity challenges.
This article explores how deep learning techniques such as RNN, LSTM, 3D‑CNN, and CNN‑RNN hybrids can be applied to predict network bandwidth and traffic, comparing traditional time‑series methods with modern AI approaches and highlighting the potential of graph neural networks for future improvements.
This article walks through creating a Python‑based neural network to predict breast cancer using the Wisconsin dataset, covering network architecture, weight and bias initialization, back‑propagation, gradient descent, and the role of activation functions such as sigmoid, tanh, ReLU and Leaky ReLU.
This article reviews Jia Yangqing’s insights on why deep learning finally succeeded—highlighting the roles of big data and high‑performance computing—while examining its current limitations, emerging challenges, and future opportunities across AI engineering, AutoML, and hardware‑software co‑design.
This article reviews the challenges of media‑domain named entity recognition, outlines the evolution from rule‑based methods through traditional machine‑learning and deep‑learning models to attention‑based Transformers, and details Sohu’s practical Bi‑LSTM‑CRF system with data‑annotation strategies and performance results.
This article explains how real‑time bidding works in computational advertising, defines the budget‑constrained bidding problem, models it with reinforcement learning, and presents a deep‑network implementation together with visual analysis and key references.
This article explains how to build both unsupervised and supervised deep‑learning models that compute item similarity from user behavior, covering prod2vec embeddings, skip‑gram architectures, loss function design, and practical training steps for modern recommender systems.
The AI Scientific Frontier Conference 2019, co‑hosted by the Chinese Academy of Sciences AI Alliance and Beijing Institute of Technology, gathers leading researchers to present cutting‑edge talks on AI theory, deep learning, computer vision, robotics, NLP, big data, and related applications, with detailed schedules, speaker bios, venue information, and registration details provided.
Natural language processing, a cornerstone of AI, relies on language models to capture linguistic features; this article reviews classic pre‑training models—ELMo, GPT, and BERT—explaining their architectures, training objectives, and how they boost downstream NLP tasks despite data‑scarcity challenges.
This article surveys the evolution of object detection models—comparing one‑stage and two‑stage approaches, their performance trade‑offs, and recent state‑of‑the‑art methods—while also outlining key concepts and breakthroughs in natural language processing, highlighting the impact of deep‑learning models such as BERT.
This article explains how the lightweight xNN-OCR engine achieves high accuracy and real‑time performance on mobile devices through deep‑learning model compression, novel detection and recognition techniques, and showcases its practical applications such as bank‑card, gas‑meter, license‑plate, and ID recognition.
This article interprets Alibaba’s “Image Matters” paper, explaining how the Deep Image CTR Model (DICM) introduces user‑side visual preference modeling with image embeddings, why traditional Parameter Servers struggle with large image vectors, and how the Advanced Model Server (AMS) compresses embeddings to enable efficient distributed training.
This article explores the evolution of Generative Adversarial Networks' objective functions, detailing the shift from Jensen‑Shannon divergence to f‑divergence, IPM‑based approaches, and auxiliary losses, while highlighting their impact on stability and performance across image, audio, and text generation tasks.
This article reviews the evolution of Taobao’s search and recommendation technology, tracing its journey from simple statistical models and rule‑based systems through large‑scale machine learning and real‑time online learning to modern deep‑learning and cognitive intelligence approaches that drive e‑commerce innovation.
This article introduces Variational Autoencoders (VAEs), compares them with GANs, explains the underlying variational inference principle, and details how VAEs are trained using the evidence lower bound, complemented by visual diagrams and key equations.
This comprehensive guide explores anomaly (outlier) detection across diverse methods—including time‑series analysis, statistical tests, distance metrics, matrix factorization, graph approaches, behavior‑sequence modeling, and supervised machine‑learning models—highlighting their principles, formulas, and practical use cases such as fraud prevention and system monitoring.
This article traces the evolution of convolutional neural networks from the pioneering AlexNet through VGG, Inception, ResNet, Inception‑v4, Inception‑ResNet and ResNeXt, highlighting architectural innovations, performance gains, and the underlying biological inspirations that shaped modern deep learning models.
This article explores how deep‑learning‑based image dewarping techniques, from traditional hardware methods to modern U‑Net, Stacked U‑Net and Dilated U‑Net architectures, can correct warped document photos, improve OCR accuracy, and support intelligent verification in high‑throughput business scenarios.
This article introduces Alibaba’s intelligent matting editor and its Semantic Human Matting (SHM) algorithm, detailing the integration of semantic segmentation and deep matting networks, the fusion module, training strategy, experimental results, and the deployment of an online fully‑automatic cutout tool for designers.
The UI2CODE system tackles UI layout analysis by first extracting backgrounds with Sobel, Laplacian and Canny edge detection plus a flood‑fill algorithm, then isolating foreground components through connected‑component analysis and a Faster R‑CNN classifier, and finally fusing both pipelines to achieve superior precision, recall and IoU on Xianyu app screenshots.
The article details the design and iterative improvement of 58.com’s intelligent QABot, covering knowledge‑base construction, feature engineering, three generations of classification models—including FastText, Bi‑LSTM, and deep semantic matching—and evaluation metrics that achieve high accuracy and automation rates.
This article explains how Alibaba’s algorithm team tackles the massive challenge of extracting high‑quality buyer‑generated images from Taobao by combining statistical features, GBDT classification, deep CNN visual assessment, aesthetic scoring, and dirty‑data filters to improve content moderation efficiency.
This article introduces TensorFlow, explains when neural networks are appropriate, outlines the general workflow for solving image‑based problems, and provides a step‑by‑step Python implementation of a multilayer perceptron that classifies handwritten digits, while also discussing TensorFlow's strengths, limitations, and alternatives.
This article explains the background of GPU communication, introduces DMA and RDMA fundamentals, describes how GPUDirect RDMA enables direct GPU-to-GPU memory access across machines, and presents performance results showing reduced latency and increased bandwidth for distributed deep‑learning training.
JD AI Research Institute introduced the Improved Selective Refinement Network (ISRN), a face‑detection model that outperforms all competitors on the WIDER FACE benchmark across Easy, Medium, and Hard subsets with AP scores of 96.3%, 95.4% and 90.3%, thanks to novel training strategies, COCO pre‑training, redesigned input modules, and multi‑stage classification‑regression enhancements.
This article explains the background of GPU communication, details NVIDIA's GPUDirect and its Peer‑to‑Peer features, discusses virtualization challenges, and presents performance measurements on an Alibaba Cloud GN5 instance showing latency reduction and near‑linear scaling for deep‑learning workloads.
This article explains how Alibaba's automatic task‑pipeline system transforms deep‑learning inference for retail video streams by decoupling model execution from scheduling, using Python‑based pipelines, high‑performance shared memory, and robust fault‑tolerance, achieving up to 13% faster processing and double the camera capacity.
The article traces AI’s origins from the 1956 Dartmouth conference, explains how deep learning has become the driving force behind modern AI applications, discusses the need for a mindset shift, lifelong learning, and strategic adaptation to stay relevant in the rapidly evolving intelligent era.
Cheng Huige’s winning AI Challenger 2018 solution treated fine‑grained Chinese review sentiment as a 20‑aspect multi‑class task, combining a high‑capacity LSTM encoder with self‑attention, word‑and‑character embeddings, simplified ELMo pre‑training, diverse tokenizations and a weighted seven‑model ensemble (including BERT), which together delivered the competition’s top F1 performance.
Tencent’s industrial practice shows how a large‑scale offline‑nearline‑online “Shield” recommendation architecture, powered by the DeepR framework built on RCaffe, uses deep semantic embeddings, massive neural networks and reinforcement‑learning decisions to handle billions of daily requests, demonstrating that data richness and engineering capability, not model depth alone, drive performance in big‑data recommendation systems.
The Meituan‑Dianping search team progressed its core ranking from linear, FM and GBDT models to a knowledge‑graph‑enhanced deep‑learning architecture, culminating in the listwise LambdaDNN network that directly optimizes NDCG, supported by extensive feature engineering, distributed TensorFlow training, and the Athena diagnostic system.
This article presents a detailed case study of building a purchase‑user prediction model by integrating Convolutional Neural Networks for feature extraction with Long Short‑Term Memory networks for time‑series forecasting, covering background, model structure, data augmentation, experimental results, and business impact.
The talk explains how Meituan transformed its O2O hotel search by layering a multi‑stage retrieval pipeline with intent‑aware NLP, then progressively upgrading ranking—from XGBoost to MLPs, feature‑embedding networks, and finally a Wide‑Deep multi‑task model—while tackling data sparsity, diverse scenarios, and deploying the system via TensorFlow‑Serving and the in‑house MLX platform.
This article provides a comprehensive, step‑by‑step explanation of the Transformer architecture, covering its encoder‑decoder structure, self‑attention, multi‑head attention, positional encoding, residual connections, and training processes, illustrated with diagrams and code snippets to aid readers new to neural machine translation.
This article introduces core deep‑learning architectures—including MLP, RNN, CNN, auto‑encoders, and RBM—explains common activation and loss functions, and then surveys fourteen influential deep‑learning‑based recommendation algorithms such as FM, wide&deep, deepFM, NCF, GBDT+LR, seq2seq and YouTube DNN, complete with model diagrams and reference links.
iQIYI built a cloud‑based deep‑learning training platform called Jarvis, replacing the initial Runonce service, by containerizing GPU tasks, adopting Ceph S3 storage with FUSE, optimizing data pipelines, and addressing compute, storage, and networking challenges to improve scalability and reduce GPU idle time.
iQIYI’s NLP pipeline—combining CRF‑based segmentation, bidirectional LSTM/GRU models with attention and a CNN classifier—automatically extracts opinion targets, sentiment words and polarity from unstructured video comments, aggregates them across users to reveal collective attitudes toward actors, plot, and visual effects, and guides future work on implicit opinions and broader sentiment domains.
Alibaba's X‑DeepLearning (XDL) is an open‑source deep‑learning framework optimized for high‑dimensional sparse data, offering industrial‑grade distributed training, built‑in CTR/recommendation algorithms, structured compression, and online learning capabilities, with benchmark results demonstrating superior scalability and performance.
This article describes how a large‑scale operations monitoring system at Ctrip evolved from rule‑based alerts to a deep‑learning‑driven anomaly detection pipeline using LSTM models and Flink streaming, achieving a ten‑fold reduction in alert volume while improving fault detection.
This article explains the design of a Cross‑Domain Embedding (CSDE) method that transfers Alipay user features to Taobao representations, details its learning and adaptive prediction stages, and shows experimental and online results demonstrating significant conversion‑rate improvements for new and inactive users.
The article analyzes how iQIYI leverages deep learning, dynamic frame interpolation, personalized content features, and AI-driven heat metrics to reshape video streaming, improve traffic prediction, and gain a competitive edge in the rapidly evolving entertainment industry.
At Intel Architecture Day 2018 the company announced its next‑generation Sunny Cove micro‑architecture built on 10 nm, detailed its roadmap through 2021, introduced the 3D‑stacking Foveros technology, previewed new integrated and discrete GPUs, and launched a deep‑learning reference stack for AI developers.
The paper introduces an improved two‑channel CNN, built on a shared VGG16 backbone and a hinge‑loss metric, to automatically generate numeric tags for game advertising assets by learning content and style similarity, achieving over 97% test accuracy and enabling efficient ad placement and asset management.
AutoML and Neural Architecture Search automate deep‑learning model design by using controller networks to explore and evaluate candidate architectures, with efficient variants like PNAS and ENAS reducing cost, while platforms such as Google Cloud AutoML and open‑source AutoKeras make these techniques accessible, promising broader, democratized AI breakthroughs.
AutoML and Neural Architecture Search automate deep‑learning model design by sampling and training network blocks, using reinforcement‑learning or efficient weight‑sharing strategies such as PNAS and ENAS, enabling high‑accuracy architectures in days on a single GPU, with services like Google Cloud AutoML and open‑source tools like AutoKeras, while future research aims to expand search spaces beyond hand‑crafted blocks.
An in‑depth overview of multi‑task learning for natural language processing is presented, covering deep learning foundations, challenges, various multi‑task learning paradigms (hard, soft, shared‑private, function‑level, hierarchical, and search‑based sharing), benchmark platforms, and future research directions, illustrated with numerous diagrams.
The article examines YouTube’s large‑scale recommendation system, detailing its deep‑learning architecture, the challenges of scale, freshness and noise, and the design choices in candidate generation, ranking, data collection, and evaluation that together deliver over 70% of user watch time.
The article surveys the nineteen leading AI technologies—from natural language generation and speech recognition to digital twins and marketing automation—detailing their core functions, common use cases such as customer service, security, content creation, and the key vendors delivering each solution.
This article presents a multi‑source pointer network approach for generating concise, brand‑preserving product short titles in e‑commerce, detailing problem formalization, model architecture, extensive offline and online experiments, and demonstrating significant improvements over traditional truncation and seq2seq baselines.
The article summarizes Zheng Zeyu's presentation on deep learning fundamentals, the evolution and features of TensorFlow, and how AI techniques such as neural networks and Faster R-CNN are applied to address data challenges and enable intelligent fashion design and recommendation.
Alibaba's X-Deep Learning (XDL) framework, the first open‑source deep learning system designed for high‑dimensional sparse data, powers advertising, recommendation, and search workloads, delivering industrial‑scale performance and boosting revenue while offering an open, easy‑to‑use solution for the broader AI community.
This comprehensive guide walks beginners through fourteen practical steps to learn Python machine learning, covering essential Python skills, core scientific libraries, fundamental algorithms, advanced techniques like SVM and ensemble methods, dimensionality reduction, and deep learning with TensorFlow, all using free online resources.
This article explains how to speed up TensorFlow deep‑learning model training using single‑GPU acceleration, multi‑GPU parallelism, and distributed TensorFlow on Kubernetes, covering device placement, session parameters, synchronous vs asynchronous training modes, and practical code examples to improve performance and scalability.
This article discusses the limitations of algorithm-based classification models, including the need for large labeled datasets, limited sample coverage, frequent category changes requiring retraining, and complex optimization issues, while exploring knowledge graph-based approaches and generative adversarial networks for more flexible and accurate classification.
This comprehensive overview examines the fundamentals, historical milestones, key algorithms, major datasets, policy support, industry applications, and future trends of face recognition technology, highlighting its rapid growth within computer vision and artificial intelligence.
This guide lists and briefly describes 18 open‑source and cloud‑based machine learning platforms—from H2O and TensorFlow to Azure ML and AWS services—highlighting their key features, supported languages, and typical use cases for developers at any skill level.
This article presents a step‑by‑step solution that combines computer‑vision preprocessing, OCR, CNN classification, shape matching, and inpainting to isolate meaningful foreground elements from images with intricate backgrounds, discussing practical results, limitations, and code implementations.
This article explains how reinforcement learning can be modeled and deployed to enable intelligent, interactive product search on e‑commerce platforms, detailing problem definition, system architecture, training methodology, online results, and future research directions.
The article surveys ten leading open-source AI tools and frameworks—including TensorFlow, SystemML, Caffe, Apache Mahout, OpenNN, Torch, Neuroph, Deeplearning4j, Mycroft, and OpenCog—detailing their primary features, supported languages, hardware compatibility, and typical use cases for research and development.
The article explains how to replace keyword matching with deep‑learning document embeddings in Elasticsearch by applying PCA dimensionality reduction, indexing vectors using Lucene’s KD‑tree structures via a custom plugin, and leveraging FAISS‑style nearest‑neighbour techniques to achieve fast, semantically aware similarity search.
The article presents a comprehensive overview of Meituan's intelligent customer service system, detailing its evolution, the roles of QABot, TaskBot and ChatBot, the underlying NLP and knowledge‑graph technologies, model implementations such as DSSM and Seq2seq, and the impressive operational results achieved in food‑delivery and ride‑hailing services.
Researchers propose a Deep Feedforward Sequential Memory Network (DFSMN) for speech synthesis that matches BLSTM quality while using only a quarter of the model size and achieving four times faster inference, making it ideal for memory‑constrained, real‑time edge devices.
This article introduces Advanced LSTM (A‑LSTM), which linearly combines multiple past hidden states to overcome traditional LSTM's one‑step dependency, and demonstrates its application in utterance‑level speech emotion recognition, achieving a 5.5% accuracy improvement through attention‑based weighted‑pooling RNNs and auxiliary speaker and gender tasks.
During the World Cup, Youku used a frame‑rate‑up‑conversion algorithm that synthesizes intermediate frames by combining block‑based motion estimation, optical‑flow refinement, and a rule‑based plus deep‑learning fusion, enabling smooth 25→50 fps video and preserving fast‑moving objects such as a football.
The article clarifies that artificial intelligence encompasses machine learning, which in turn includes deep learning, and uses real‑world examples—from fraud detection and customer clustering to image recognition and language translation—to illustrate how these data‑driven models learn patterns, make predictions, and transform many industries.
This paper introduces a Deep Feedforward Sequential Memory Network (DFSMN) for statistical parametric speech synthesis that matches BLSTM quality with only a quarter of the model size and four times faster inference, making it ideal for memory‑constrained, real‑time IoT devices.
The article summarizes a QCon 2018 presentation by Luo Jingjia of Manbang Group, detailing how AI and deep‑learning techniques are applied to model, optimize, and recommend vehicle‑cargo matching in China’s massive highway logistics network, including data handling, neural‑network design, and practical challenges.
The article introduces convolutional neural networks, explains core concepts such as convolution, padding, stride, and pooling, demonstrates how to calculate output dimensions, and provides a step‑by‑step Keras example that builds, compiles, and trains a multi‑layer CNN for image classification.
This article describes how deep learning techniques, including convolutional neural networks and CAM visualizations, are applied to automatically evaluate and select high‑quality images for online travel agency ticket listings, improving user experience while reducing manual effort.
This tutorial explains how to extend a deep‑learning object detection project with OpenCV and Python to process live video streams, covering setup, command‑line arguments, model loading, frame‑by‑frame detection, drawing bounding boxes, FPS measurement, and performance tips for achieving real‑time results.
This article reviews the evolution of Alibaba’s e‑commerce search and recommendation technologies, detailing Taobao’s unique challenges, the shift from rule‑based retrieval to large‑scale machine learning, real‑time online learning, deep learning and intelligent decision‑making, and outlines future directions toward cognitive intelligence.
DeepInsight is a distributed, micro‑service‑based platform that provides end‑to‑end data exposure, multi‑dimensional visual analysis, and interactive debugging for TensorFlow models, turning opaque neural networks into transparent, controllable systems through real‑time visualizations, dynamic data sets, and integrated lifecycle management.
This article explores the challenges of OCR on warped document images, reviews traditional and deep‑learning‑based correction methods, describes a synthetic dataset generation pipeline, proposes enhanced U‑Net architectures including stacked and dilated variants, evaluates them with MS‑SSIM, and outlines future research directions.
JD.com's technical architecture team partnered with product teams to leverage the Euler knowledge computing platform and product knowledge graph to enhance the e-commerce review ecosystem, improving user experience in UGC communities through intelligent content management.
In an interview at the 2018 AI Pioneer Conference, Dr. Lv Zhengdong discusses his background, the development of neural‑symbolic AI at DeepCuriosity, its applications in law, public security and finance, challenges in NLP, and his perspective on the future trajectory of artificial intelligence.
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.
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.
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.
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.
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.
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.
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.
