BestHub
Sign in
Tagged articles
176 articles
Page 2 of 2
dbaplus Community
dbaplus Community
Aug 28, 2021 · Artificial Intelligence

Is AI Really Intelligent? Exploring Machine Learning, Neural Networks & Deep Learning

The article demystifies AI by explaining that current artificial intelligence is merely automated computation, then walks through fundamental machine‑learning concepts such as exhaustive search, linear regression, neural‑network neurons, activation functions, network structures, training calculations, and concludes with a Python implementation of a three‑layer neural network.

AIDeep LearningNeural Networks
0 likes · 15 min read
Is AI Really Intelligent? Exploring Machine Learning, Neural Networks & Deep Learning
ByteFE
ByteFE
Aug 2, 2021 · Artificial Intelligence

An Overview of Artificial Intelligence, Machine Learning, and Neural Networks

This article provides a beginner‑friendly overview of artificial intelligence, its relationship with machine learning, the four major learning paradigms—supervised, unsupervised, semi‑supervised and reinforcement learning—along with a historical sketch of neural networks, their training workflow, loss functions, back‑propagation, and parameter‑update mechanisms, while also containing a brief recruitment notice.

Deep LearningNeural NetworksUnsupervised Learning
0 likes · 18 min read
An Overview of Artificial Intelligence, Machine Learning, and Neural Networks
DataFunSummit
DataFunSummit
Aug 1, 2021 · Artificial Intelligence

A Comprehensive Overview of Sequence Recommendation Models and Techniques

This article provides an in‑depth review of user behavior sequence recommendation, covering problem definition, data preparation, and a range of neural models—including MLP, CNN, RNN, Temporal CNN, self‑attention, and reinforcement learning—along with practical implementation tips and references.

MLNeural Networksreinforcement learning
0 likes · 35 min read
A Comprehensive Overview of Sequence Recommendation Models and Techniques
Miss Fresh Tech Team
Miss Fresh Tech Team
Jul 8, 2021 · Artificial Intelligence

How AI Powers Smart Vending Cabinets: From RFID to Deep Learning Detection

This article details the evolution of intelligent vending cabinets, comparing RFID, gravity, dynamic and static vision solutions, and explains how deep‑learning models, data pipelines, and system architectures enable high‑accuracy, low‑loss product detection and automated operations in modern unmanned retail.

AIComputer VisionNeural Networks
0 likes · 36 min read
How AI Powers Smart Vending Cabinets: From RFID to Deep Learning Detection
Architects Research Society
Architects Research Society
May 30, 2021 · Artificial Intelligence

Artificial Intelligence vs. Machine Learning: Definitions, History, and Key Differences

This article explains the origins, definitions, and evolving relationship between artificial intelligence and machine learning, highlighting their historical milestones, core concepts, and how modern applications like deep learning, neural networks, and recommendation systems illustrate their intertwined development.

AIDeep LearningDefinitions
0 likes · 8 min read
Artificial Intelligence vs. Machine Learning: Definitions, History, and Key Differences
Tencent Tech
Tencent Tech
May 13, 2021 · Artificial Intelligence

Seeing Inside the Black Box: Visualizing Neural Network Training and Adversarial Threats

This article explains how neural networks work, walks through the step‑by‑step training process of a convolutional model, showcases vivid visualizations of each layer, and demonstrates how tiny adversarial perturbations can dramatically alter predictions, highlighting the importance of AI security.

AI securityCNN visualizationDeep Learning
0 likes · 6 min read
Seeing Inside the Black Box: Visualizing Neural Network Training and Adversarial Threats
DataFunTalk
DataFunTalk
Feb 21, 2021 · Artificial Intelligence

Intra‑Ensemble in Neural Networks

This paper proposes an intra‑ensemble strategy that trains multiple sub‑networks within a single neural network using random training operations, width‑depth variations, and parameter sharing, achieving diverse models and improved performance comparable to traditional ensembles while adding only marginal parameter overhead.

Architecture SearchModel DiversityNeural Networks
0 likes · 9 min read
Intra‑Ensemble in Neural Networks
DataFunTalk
DataFunTalk
Jan 10, 2021 · Artificial Intelligence

Didi's Machine Translation System: Architecture, Techniques, and WMT2020 Competition Experience

This article presents a comprehensive overview of Didi's machine translation platform, covering its evolution from statistical to neural models, the Transformer architecture with relative position and larger FFN, data preparation, training strategies such as back‑translation and knowledge distillation, deployment optimizations with TensorRT, and the team's successful participation in the WMT2020 news translation task.

BLEUNeural NetworksTensorRT
0 likes · 14 min read
Didi's Machine Translation System: Architecture, Techniques, and WMT2020 Competition Experience
TAL Education Technology
TAL Education Technology
Dec 17, 2020 · Artificial Intelligence

Web Front‑End Intelligent Computing: Concepts, Implementation, and Applications

This article explains how AI technologies are transitioning from labs to the web, covering neural network fundamentals, the distinction between cloud and edge intelligence, implementation pipelines, offline model optimization, online inference backends like WebGL and WASM, and practical web front‑end AI use cases.

Neural NetworksWeb AIfrontend
0 likes · 10 min read
Web Front‑End Intelligent Computing: Concepts, Implementation, and Applications
DeWu Technology
DeWu Technology
Nov 26, 2020 · Artificial Intelligence

Automated Captcha Recognition Using Machine Learning

The article outlines a machine‑learning pipeline for automated captcha recognition, covering dataset generation, image preprocessing, segmentation via clustering or watershed methods, and classification using classic models and CNNs, achieving roughly 94% accuracy while noting the growing complexity of modern captchas and recommending developer collaboration when feasible.

CaptchaNeural NetworksPython
0 likes · 23 min read
Automated Captcha Recognition Using Machine Learning
Didi Tech
Didi Tech
Oct 27, 2020 · Artificial Intelligence

Didi's Machine Translation System: Architecture, Techniques, and WMT2020 Competition Experience

Didi's machine translation system combines a Transformer‑big architecture with relative position representations, enlarged feed‑forward networks, iterative back‑translation, knowledge‑distillation and domain fine‑tuning, optimized via TensorRT for speed, achieving a BLEU 36.6 and third place in the WMT2020 Chinese‑to‑English news task.

BLEUNeural NetworksTensorRT
0 likes · 15 min read
Didi's Machine Translation System: Architecture, Techniques, and WMT2020 Competition Experience
DataFunTalk
DataFunTalk
Oct 20, 2020 · Artificial Intelligence

From Biological Neurons to Artificial Neural Networks: Perceptrons, Multilayer Perceptrons, and Backpropagation

This article traces the evolution of artificial neural networks from their biological inspiration, explains the McCulloch‑Pitts neuron model, details perceptron architecture and learning rule with a Scikit‑Learn example, and introduces multilayer perceptrons and the back‑propagation algorithm together with common activation functions.

AIBackpropagationDeep Learning
0 likes · 19 min read
From Biological Neurons to Artificial Neural Networks: Perceptrons, Multilayer Perceptrons, and Backpropagation
Architects' Tech Alliance
Architects' Tech Alliance
Sep 3, 2020 · Artificial Intelligence

Deep Learning Specialization Infographic Overview

This article presents a comprehensive English summary of the deep learning specialization infographics originally shared by Andrew Ng, covering fundamentals, logistic regression, shallow and deep neural networks, regularization, optimization, hyperparameters, convolutional and recurrent networks, and practical advice for model building and evaluation.

CNNDeep LearningNeural Networks
0 likes · 21 min read
Deep Learning Specialization Infographic Overview
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Aug 13, 2020 · Artificial Intelligence

AI and High‑Performance Computing in Weather Forecasting: From Radar Images to Neural Networks

The article explains how modern weather forecasting in China combines traditional observations with artificial‑intelligence techniques such as U‑Net image‑to‑image models, optical‑flow analysis, and massive high‑performance computing to improve precipitation nowcasting, while also highlighting the scientific challenges and interdisciplinary nature of meteorology.

High‑performance computingNeural Networksartificial intelligence
0 likes · 8 min read
AI and High‑Performance Computing in Weather Forecasting: From Radar Images to Neural Networks
DataFunTalk
DataFunTalk
May 16, 2020 · Artificial Intelligence

Exploring Search Matching Models and Their Applications in DiDi Food

This article introduces the background of search relevance, reviews three common matching model types—representation‑based, interaction‑based, and hybrid—describes their architectures such as DSSM, CDSSM, DRMM and DUET, and presents experimental results of these models on DiDi Food’s search system.

DiDi FoodNeural Networksdeep matching
0 likes · 15 min read
Exploring Search Matching Models and Their Applications in DiDi Food
DataFunTalk
DataFunTalk
May 6, 2020 · Artificial Intelligence

Application of Large-Scale Pretrained Models in Alibaba Machine Translation

This article reviews how large‑scale pretrained language models have reshaped NLP, outlines the challenges of applying them to machine translation, introduces the APT framework and the GRET architecture for better encoder‑decoder integration, and reports experimental gains and future research directions.

AIAPT frameworkGRET
0 likes · 10 min read
Application of Large-Scale Pretrained Models in Alibaba Machine Translation
21CTO
21CTO
Apr 20, 2020 · Artificial Intelligence

Why DeepL’s Neural Translation Beats Google: Inside the AI Engine

This article examines DeepL’s translation system, comparing its neural‑network‑driven output to Google and other services, detailing its Icelandic HPC infrastructure, data collection, architectural choices, language support, strengths, limitations, and expert opinions on why it often delivers more natural translations.

AIComparisonHPC
0 likes · 9 min read
Why DeepL’s Neural Translation Beats Google: Inside the AI Engine
Python Programming Learning Circle
Python Programming Learning Circle
Mar 12, 2020 · Fundamentals

Fundamentals of Derivatives and Partial Derivatives for Neural Networks

This article introduces the mathematical foundations of derivatives and partial derivatives, explains their role in optimizing neural network parameters, covers basic derivative formulas, linear properties, sigmoid derivative, minimum conditions, and constrained optimization using Lagrange multipliers, providing a comprehensive guide for machine‑learning practitioners.

DerivativesNeural Networkscalculus
0 likes · 8 min read
Fundamentals of Derivatives and Partial Derivatives for Neural Networks
Python Programming Learning Circle
Python Programming Learning Circle
Mar 7, 2020 · Artificial Intelligence

Fundamentals of Functions, Sequences, and Their Role in Neural Networks

This article introduces basic mathematical functions—including linear, quadratic, exponential, and step functions—explains sequences and their formulas, and shows how these concepts underpin neural‑network computations such as weighted inputs, activation functions like sigmoid, and error‑backpropagation, providing clear examples and visual illustrations.

AIActivationNeural Networks
0 likes · 11 min read
Fundamentals of Functions, Sequences, and Their Role in Neural Networks
DataFunTalk
DataFunTalk
Dec 19, 2019 · Artificial Intelligence

Model Quantization in Neural Networks: Challenges, Solutions, and Future Directions

This article reviews neural‑network model quantization, explaining why quantization is needed, detailing forward‑ and backward‑propagation issues, presenting three main mitigation strategies, discussing subsequent pruning, performance‑recovery techniques, and outlining future research avenues in efficient machine learning.

Model QuantizationNeural Networksefficient machine learning
0 likes · 27 min read
Model Quantization in Neural Networks: Challenges, Solutions, and Future Directions
DataFunTalk
DataFunTalk
Dec 13, 2019 · Artificial Intelligence

Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU

This article provides a comprehensive overview of deep learning fundamentals, covering neural network basics, forward and backward feedback architectures, key models such as MLP, CNN, RNN, LSTM and GRU, training techniques like gradient descent, learning rate schedules, momentum, weight decay, and batch normalization.

CNNDeep LearningGRU
0 likes · 14 min read
Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU
Architects Research Society
Architects Research Society
Oct 7, 2019 · Artificial Intelligence

Comparison of Deep Learning Software Frameworks and Libraries

The article introduces deep learning as a machine‑learning subfield aimed at achieving artificial intelligence, explains its advantages in pattern recognition, and presents a visual comparison of prominent deep‑learning software frameworks, related tools, and additional resources while also including promotional information for a WeChat community.

AI frameworksNeural Networksmachine learning
0 likes · 3 min read
Comparison of Deep Learning Software Frameworks and Libraries
Qunar Tech Salon
Qunar Tech Salon
Sep 5, 2019 · Artificial Intelligence

Implementing Bilinear Interpolation on FPGA for Neural Network Acceleration

The article explains the principles of bilinear interpolation, why it is needed for smooth image scaling in neural‑network layers such as Interp and Resize, and details FPGA‑specific optimizations—including lookup‑table based coefficient pre‑computation, two‑line BRAM caching, and index‑driven data swapping—to reduce DSP usage and improve throughput.

BRAMBilinear InterpolationDSP
0 likes · 14 min read
Implementing Bilinear Interpolation on FPGA for Neural Network Acceleration
Beike Product & Technology
Beike Product & Technology
Aug 23, 2019 · Artificial Intelligence

Deep Learning from Theory to Practice: Neural Networks, Logistic Regression, TensorFlow and Keras for Cat Image Classification

This tutorial walks readers through the fundamentals of artificial neural networks, perceptrons, logistic regression, activation and loss functions, gradient descent, and provides end‑to‑end Python implementations using NumPy, TensorFlow, and Keras to build and evaluate a cat‑vs‑non‑cat classifier, complete with code snippets, visual explanations, and performance analysis.

Deep LearningKerasNeural Networks
0 likes · 29 min read
Deep Learning from Theory to Practice: Neural Networks, Logistic Regression, TensorFlow and Keras for Cat Image Classification
DataFunTalk
DataFunTalk
May 29, 2019 · Artificial Intelligence

A Comprehensive Overview of Statistical Learning Methods for Machine Learning Interview Preparation

This article provides a detailed, English-language summary of key statistical learning concepts—including perceptron, k‑nearest neighbors, Naive Bayes, decision trees, logistic regression, support vector machines, boosting, EM, HMM, neural networks, K‑Means, bagging, Apriori and dimensionality reduction—complete with formulas, algorithm steps, and illustrative diagrams to aid interview preparation.

Neural NetworksSupport Vector Machineclassification
0 likes · 44 min read
A Comprehensive Overview of Statistical Learning Methods for Machine Learning Interview Preparation
Ctrip Technology
Ctrip Technology
May 21, 2019 · Artificial Intelligence

A Brief Overview of Machine Translation: History, Neural Models, and Practical Insights

This article surveys the evolution of machine translation from early rule‑based systems to modern neural architectures, explains how translation engines are trained, highlights recent advances such as attention and Transformers, and shares practical experience and current challenges in the field.

Attention MechanismNeural NetworksTransformer
0 likes · 11 min read
A Brief Overview of Machine Translation: History, Neural Models, and Practical Insights
DataFunTalk
DataFunTalk
May 21, 2019 · Artificial Intelligence

Deep Learning Foundations: Mathematics, Modern Network Practices, and Research Overview

This article provides a comprehensive overview of deep learning, covering essential mathematics and machine learning fundamentals, modern deep network architectures and regularization techniques, advanced research topics such as structured probabilistic models and generative methods, and a curated reading list for practitioners.

AI fundamentalsNeural Networksmachine learning
0 likes · 4 min read
Deep Learning Foundations: Mathematics, Modern Network Practices, and Research Overview
HomeTech
HomeTech
May 15, 2019 · Artificial Intelligence

How to Build a Deep Learning Model to Predict Workdays from Attendance Data

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.

Deep LearningNeural Networksartificial intelligence
0 likes · 9 min read
How to Build a Deep Learning Model to Predict Workdays from Attendance Data
Tencent Advertising Technology
Tencent Advertising Technology
Apr 26, 2019 · Big Data

Handling Large-Scale Data in the Tencent Advertising Algorithm Competition: Model Choices, Data Splitting, and Feature Engineering

The article shares practical strategies for processing massive advertising data in the Tencent algorithm competition, covering model selection between GBDT and neural networks, efficient data partitioning methods for low‑resource environments, and the importance of feature engineering to achieve top rankings.

GBDTNeural NetworksTencent Ads
0 likes · 7 min read
Handling Large-Scale Data in the Tencent Advertising Algorithm Competition: Model Choices, Data Splitting, and Feature Engineering
ITPUB
ITPUB
Mar 6, 2019 · Artificial Intelligence

Why WeChat’s Translation Glitches Reveal Hidden AI Challenges

A recent WeChat translation bug that turned a name into bizarre Chinese phrases sparked a deep dive into neural machine translation, exposing algorithmic shortcomings, training‑data biases, and the broader uncertainties that affect modern AI‑driven translators.

AINMTNeural Networks
0 likes · 10 min read
Why WeChat’s Translation Glitches Reveal Hidden AI Challenges
Architects Research Society
Architects Research Society
Feb 9, 2019 · Artificial Intelligence

Introduction to TensorFlow and Building a Simple Neural Network for Image Classification

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.

Deep LearningImage ClassificationNeural Networks
0 likes · 14 min read
Introduction to TensorFlow and Building a Simple Neural Network for Image Classification
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 9, 2019 · Artificial Intelligence

Master Deep Learning Foundations and 14 Cutting-Edge Recommendation Models

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.

AIDeep LearningNeural Networks
0 likes · 18 min read
Master Deep Learning Foundations and 14 Cutting-Edge Recommendation Models
iQIYI Technical Product Team
iQIYI Technical Product Team
Dec 28, 2018 · Artificial Intelligence

Short Video Tagging Using Neural Networks

The paper presents a gated‑attention neural network that fuses audio, visual, and title text features to automatically generate high‑quality tags for short videos, achieving state‑of‑the‑art performance on the YouTube‑8M challenge and enabling scalable tagging and recommendation services with future plans for broader tag coverage and temporal segment tagging.

AINeural NetworksYouTube-8M dataset
0 likes · 7 min read
Short Video Tagging Using Neural Networks
Tencent Cloud Developer
Tencent Cloud Developer
Nov 9, 2018 · Artificial Intelligence

Demystifying Neural Networks: A Mathematical Approach

The article explains how basic mathematical principles—starting with simple predictors and linear classifiers, then extending to multi‑classifier systems, activation functions, and weight adjustments—underpin neural network architecture, illustrating each step with concrete examples to show how mathematics drives AI model training and performance.

BackpropagationNeural NetworksXOR problem
0 likes · 15 min read
Demystifying Neural Networks: A Mathematical Approach
Tencent Cloud Developer
Tencent Cloud Developer
Oct 18, 2018 · Artificial Intelligence

10 Machine Learning Algorithms You Should Know to Become a Data Scientist

This article outlines the essential role of a data scientist and introduces ten fundamental machine‑learning algorithms—including PCA/SVD, OLS and polynomial regression, regularized linear models, K‑Means, logistic regression, SVM, feed‑forward, convolutional and recurrent neural networks, CRFs, ensemble trees, and reinforcement‑learning methods—while linking to popular Python libraries and tutorials.

AlgorithmsDecision TreesNeural Networks
0 likes · 10 min read
10 Machine Learning Algorithms You Should Know to Become a Data Scientist
Hulu Beijing
Hulu Beijing
Sep 27, 2018 · Artificial Intelligence

From Rules to Neural Networks: The Evolution of Machine Translation

This article traces the history of machine translation—from early rule‑based systems through statistical models that leveraged parallel corpora, to modern neural network approaches—while highlighting current applications, challenges, and future directions in the field.

AI applicationsNeural Networksmachine translation
0 likes · 9 min read
From Rules to Neural Networks: The Evolution of Machine Translation
Tencent Cloud Developer
Tencent Cloud Developer
Sep 26, 2018 · Artificial Intelligence

Breakthroughs in AI: Deep Learning Applications in Speech Recognition

The talk reviews how massive speech data, faster GPUs/CPUs, and deep‑learning models such as DNN, LSTM, CNN, and end‑to‑end CTC have dramatically boosted speech‑recognition accuracy, while outlining remaining challenges like noise, accents, far‑field and multi‑speaker scenarios and describing Tencent Cloud’s related services.

AINeural Networksacoustic modeling
0 likes · 16 min read
Breakthroughs in AI: Deep Learning Applications in Speech Recognition
Tencent Cloud Developer
Tencent Cloud Developer
Sep 20, 2018 · Artificial Intelligence

What Everyone Should Know About Machine Learning

Machine learning lets computers learn patterns from examples instead of explicit code, enabling tasks like image and fraud detection, predictive maintenance, and personalized services, now feasible thanks to big data, cloud compute, and open-source tools, and increasingly discussed by executives for strategic automation.

Big DataNeural NetworksPredictive Maintenance
0 likes · 11 min read
What Everyone Should Know About Machine Learning
Meitu Technology
Meitu Technology
Aug 17, 2018 · Artificial Intelligence

Deep Learning-based Object Detection Algorithm Review (Part 2): Solutions and Network Improvements

The article reviews deep learning object detection solutions: small object detection via FPN and TDM, irregular shapes via deformable convolution, sample imbalance via focal loss and cascade methods, occlusion handling with Soft‑NMS and RRC, large‑batch training using MegDet, relationship modeling with Relation Networks, and network improvements such as DetNet, RefineDet, Pelee, and RFBNet.

FPNFocal LossNeural Networks
0 likes · 38 min read
Deep Learning-based Object Detection Algorithm Review (Part 2): Solutions and Network Improvements
Tencent Cloud Developer
Tencent Cloud Developer
Aug 13, 2018 · Artificial Intelligence

Computer Vision Technology: From Viral Social Media Apps to Enterprise AI Applications

The article surveys computer‑vision fundamentals and evolution—from early filters and feature extractors to modern deep‑learning models—illustrating how techniques like face detection, image matching, and caption generation powered viral social‑media trends and now underpin enterprise AI services on Tencent Cloud, while offering practical implementation and skill‑development guidance.

AI applicationsCNNImage Processing
0 likes · 18 min read
Computer Vision Technology: From Viral Social Media Apps to Enterprise AI Applications
Bitu Technology
Bitu Technology
Jul 19, 2018 · Artificial Intelligence

Introduction to Deep Learning: Concepts, Examples, and Learning Resources

This article provides a comprehensive overview of deep learning, covering its definition, fundamental machine‑learning components, illustrative examples such as hot‑dog classification and house‑price prediction, the mathematics of cost functions and gradient descent, back‑propagation via the chain rule, and practical resources and code snippets using Torch.

BackpropagationCode ExamplesNeural Networks
0 likes · 11 min read
Introduction to Deep Learning: Concepts, Examples, and Learning Resources
Tencent Cloud Developer
Tencent Cloud Developer
Jun 25, 2018 · Artificial Intelligence

Using MLP for Image Classification: Implementation, Results, and Limitations

The article demonstrates how a simple fully‑connected MLP can be trained on a small 64×64×3 cat‑vs‑non‑cat dataset, achieving perfect training accuracy but only 78 % test accuracy, and explains that parameter explosion, vanishing gradients, and lack of spatial invariance limit MLPs, motivating the shift to CNNs.

H5pyImage ClassificationMLP
0 likes · 15 min read
Using MLP for Image Classification: Implementation, Results, and Limitations
Meitu Technology
Meitu Technology
Jun 13, 2018 · Artificial Intelligence

Meipai AI Tech Talk: Deep Ranking Models, Video Clustering, and Optimization

The talk covered Meipai’s personalized deep ranking model that balances depth and low latency, a behavior‑driven video clustering method that enriches recommendation beyond visual cues, and the use of advanced data structures to accelerate solving large‑scale optimization problems in business contexts.

Neural Networksmachine learningoptimization
0 likes · 5 min read
Meipai AI Tech Talk: Deep Ranking Models, Video Clustering, and Optimization
Didi Tech
Didi Tech
Jun 1, 2018 · Artificial Intelligence

Didi's Attention-Based End-to-End Mandarin Speech Recognition: A Detailed Review

Didi’s attention‑based end‑to‑end Mandarin speech recognizer, built on the Listen‑Attend‑Spell architecture and modeling roughly 5,000 common characters, delivers 15‑25% relative accuracy gains over its prior LSTM‑CTC system while cutting model size, latency and server requirements and simplifying training by eliminating separate acoustic, pronunciation and language components.

End-to-EndLASMandarin
0 likes · 14 min read
Didi's Attention-Based End-to-End Mandarin Speech Recognition: A Detailed Review
Xianyu Technology
Xianyu Technology
Apr 20, 2018 · Artificial Intelligence

Client‑Side Voice Recognition with TensorFlow Lite and MFCC Optimization

The paper presents a client‑side speech recognizer that uses a compact TensorFlow Lite Inception‑v3 CNN model combined with an optimized MFCC feature pipeline and ARM‑NEON‑accelerated, multi‑threaded processing, achieving low‑latency, high‑accuracy voice recognition on mobile and embedded devices.

Audio ProcessingMFCCNeural Networks
0 likes · 14 min read
Client‑Side Voice Recognition with TensorFlow Lite and MFCC Optimization
JD Tech
JD Tech
Apr 19, 2018 · Artificial Intelligence

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

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

AI educationDeep LearningNeural Networks
0 likes · 23 min read
Key Insights from Prof. Zhou Zhihua’s Talk on Deep Learning, Model Complexity, and the Deep Forest Method
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 13, 2018 · Artificial Intelligence

Overview of Common Machine Learning Models: Characteristics, Advantages, and Disadvantages

This article provides a concise overview of fifteen widely used machine learning models—including decision trees, random forests, k‑means, KNN, EM, linear and logistic regression, Naive Bayes, Apriori, Boosting, GBDT, SVM, neural networks, HMM, and CRF—detailing their features, strengths, weaknesses, and typical application scenarios.

Neural Networksclassificationclustering
0 likes · 12 min read
Overview of Common Machine Learning Models: Characteristics, Advantages, and Disadvantages
Hulu Beijing
Hulu Beijing
Feb 8, 2018 · Artificial Intelligence

How Self‑Organizing Maps Work: Key Features, Design Tips & K‑Means Comparison

This article explains the principles, biological inspiration, network structure, training process, design parameters, and practical differences of Self‑Organizing Maps (SOM), an unsupervised neural network used for clustering, visualization, and feature extraction, and compares it with methods like K‑means.

Neural NetworksSelf-Organizing MapUnsupervised Learning
0 likes · 10 min read
How Self‑Organizing Maps Work: Key Features, Design Tips & K‑Means Comparison
21CTO
21CTO
Jan 6, 2018 · Artificial Intelligence

How Image Recognition Transforms Our World: Principles, Processes, and Future

This article explains the fundamentals of image recognition technology, its underlying principles, processing steps, neural‑network and nonlinear‑dimensionality‑reduction approaches, and highlights its wide‑range applications and future potential across many industries.

AIComputer VisionNeural Networks
0 likes · 11 min read
How Image Recognition Transforms Our World: Principles, Processes, and Future
21CTO
21CTO
Dec 16, 2017 · Artificial Intelligence

Unveiling the Mathematics Behind Deep Learning Success

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

Deep LearningGeneralizationNeural Networks
0 likes · 16 min read
Unveiling the Mathematics Behind Deep Learning Success
21CTO
21CTO
Oct 31, 2017 · Artificial Intelligence

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

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

Data ScienceDeep LearningNeural Networks
0 likes · 13 min read
Machine Learning vs Deep Learning: Key Differences, Examples, and Future Trends
Hujiang Technology
Hujiang Technology
Oct 12, 2017 · Artificial Intelligence

An Overview of Machine Learning and Deep Learning: Definitions, Concepts, and Core Techniques

This article provides a comprehensive introduction to machine learning and deep learning, covering their definitions, classifications, key algorithms, neural network structures, core concepts such as generalization and regularization, and typical architectures like CNN and RNN, illustrated with numerous diagrams.

CNNNeural NetworksRNN
0 likes · 22 min read
An Overview of Machine Learning and Deep Learning: Definitions, Concepts, and Core Techniques
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Oct 12, 2017 · Artificial Intelligence

Which Machine Learning Skills Will Be Most In‑Demand in the Next 3‑5 Years?

The article explains that industrial AI needs specialists who can apply machine‑learning models to specific domains, outlines essential fundamentals such as regression, classification, neural networks, data visualization, and unsupervised learning, and offers practical career advice for students and early‑career professionals seeking to transition into machine‑learning roles.

Data visualizationIndustrial AINeural Networks
0 likes · 11 min read
Which Machine Learning Skills Will Be Most In‑Demand in the Next 3‑5 Years?
Hujiang Technology
Hujiang Technology
Oct 11, 2017 · Artificial Intelligence

An Overview of Machine Learning and Deep Learning: Definitions, Core Concepts, and Typical Architectures

This article provides a comprehensive introduction to machine learning and deep learning, covering their definitions, differences, key concepts such as generalization, regularization, and overfitting, as well as typical algorithms and network architectures like CNN and RNN, illustrated with numerous diagrams.

AlgorithmsNeural Networksmachine learning
0 likes · 22 min read
An Overview of Machine Learning and Deep Learning: Definitions, Core Concepts, and Typical Architectures
21CTO
21CTO
Sep 30, 2017 · Artificial Intelligence

Top 10 Cutting-Edge Deep Learning Architectures for Computer Vision

This article surveys recent breakthroughs in deep learning for computer vision, explains what constitutes an advanced architecture, outlines common vision tasks, and provides concise overviews plus paper and Keras implementation links for ten influential models such as AlexNet, VGG, ResNet, and GAN.

CNNImage ClassificationKeras
0 likes · 15 min read
Top 10 Cutting-Edge Deep Learning Architectures for Computer Vision
Architecture Digest
Architecture Digest
Sep 30, 2017 · Artificial Intelligence

Overview of Prominent Deep Learning Architectures for Computer Vision

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

Computer VisionDeep LearningKeras
0 likes · 16 min read
Overview of Prominent Deep Learning Architectures for Computer Vision
21CTO
21CTO
Aug 29, 2017 · Artificial Intelligence

Why You Don't Need Advanced Math to Start Learning Deep Learning

Despite the hype that deep learning demands heavy calculus and linear algebra, this article shows beginners how basic concepts like derivatives and partial derivatives can be grasped with simple analogies, explains activation functions, learning rates, and the role of training and testing data in neural networks.

DerivativesNeural Networksactivation function
0 likes · 12 min read
Why You Don't Need Advanced Math to Start Learning Deep Learning
Liulishuo Tech Team
Liulishuo Tech Team
Aug 11, 2017 · Artificial Intelligence

DeepGrammar: A Neural Network Approach for Grammatical Error Detection and Correction

DeepGrammar is a bidirectional GRU‑based neural model that detects subject‑verb agreement errors by encoding surrounding context into fixed‑length vectors, outperforming rule‑based, classifier, and NMT approaches on the CoNLL‑2014 benchmark and achieving state‑of‑the‑art results across multiple error types.

GRUNeural Networksgrammar correction
0 likes · 8 min read
DeepGrammar: A Neural Network Approach for Grammatical Error Detection and Correction
21CTO
21CTO
Aug 9, 2017 · Artificial Intelligence

How Jeff Dean Builds Intelligent Systems with Large‑Scale Deep Learning

Jeff Dean, Google Senior Fellow and head of Google Brain, presents a comprehensive overview of constructing intelligent systems using large‑scale deep learning, covering architectural strategies, scaling techniques, key challenges, and real‑world applications, with insights drawn from his seminal research and industry experience.

Google BrainJeff DeanNeural Networks
0 likes · 2 min read
How Jeff Dean Builds Intelligent Systems with Large‑Scale Deep Learning
MaGe Linux Operations
MaGe Linux Operations
May 7, 2017 · Artificial Intelligence

Big Data & Machine Learning: Core Definitions and Essential Algorithms

This article explains what big data and machine learning are, their interrelationship, various big‑data analysis approaches, core machine‑learning concepts, and details ten fundamental algorithms—including regression, neural networks, SVM, clustering, dimensionality reduction, and recommendation—while highlighting their roles in modern data‑driven applications.

Big DataNeural Networksclustering
0 likes · 24 min read
Big Data & Machine Learning: Core Definitions and Essential Algorithms
21CTO
21CTO
Apr 17, 2017 · Artificial Intelligence

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

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

AI programmingDeep LearningNeural Networks
0 likes · 6 min read
Can Neural Networks Write Other Neural Networks? Inside the Neural Complete Project
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Mar 20, 2017 · Operations

How 360’s DoctorStarange Boosts Ops with AI‑Driven Prediction, Correlation, and Resource Optimization

This article explains how 360’s DoctorStarange system combines time‑series forecasting, neural‑network predictions, alarm correlation, and a machine‑health scoring model to reduce false alerts, automate remediation, and maximize resource utilization across thousands of production servers.

ARIMANeural NetworksOperations
0 likes · 14 min read
How 360’s DoctorStarange Boosts Ops with AI‑Driven Prediction, Correlation, and Resource Optimization
Qunar Tech Salon
Qunar Tech Salon
Feb 10, 2017 · Artificial Intelligence

Introduction to TensorFlow: Graphs, Sessions, Variables, Placeholders, and MNIST Handwritten Digit Recognition

This tutorial provides a concise, Python‑based introduction to TensorFlow, covering its core concepts such as computation graphs, sessions, data structures, variables, placeholders, feed_dict, and demonstrates a complete MNIST handwritten digit classification example with code snippets.

MNISTNeural NetworksPython
0 likes · 14 min read
Introduction to TensorFlow: Graphs, Sessions, Variables, Placeholders, and MNIST Handwritten Digit Recognition
Architecture Digest
Architecture Digest
Jan 6, 2017 · Artificial Intelligence

Deep Learning Approaches to Automatic Programming: Black‑Box and Code‑Generation Paradigms

Recent advances in deep learning have enabled machines to automatically generate code, with research divided into black‑box methods that learn input‑output transformations and code‑generation approaches that produce explicit program fragments, exemplified by systems such as Neural Program Interpreters and hierarchical generative CNN models.

Code GenerationNeural Networksautomatic programming
0 likes · 19 min read
Deep Learning Approaches to Automatic Programming: Black‑Box and Code‑Generation Paradigms
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 28, 2016 · Artificial Intelligence

How Deep Learning is Revolutionizing Automatic Question Answering

This article reviews the evolution of automatic question answering systems, outlines their core processing framework, and details how deep neural networks—especially CNNs, RNNs, and DCNNs—enable semantic representation, matching, and answer generation, while also discussing current challenges and future directions.

Deep LearningNeural Networksnatural language processing
0 likes · 27 min read
How Deep Learning is Revolutionizing Automatic Question Answering
Ctrip Technology
Ctrip Technology
Sep 10, 2016 · Artificial Intelligence

Deep Learning Anti‑Scam Guide: An Informal Introduction to Neural Networks, Training, and Practical Applications

This article provides a light‑hearted yet thorough overview of deep learning, covering neural network fundamentals, layer construction, back‑propagation, ResNet shortcuts, encoder‑decoder structures, PU‑learning for unlabeled data, GPU acceleration, and practical advice on data size, frameworks, and deployment in financial scenarios.

BackpropagationBig DataGPU
0 likes · 27 min read
Deep Learning Anti‑Scam Guide: An Informal Introduction to Neural Networks, Training, and Practical Applications
Ctrip Technology
Ctrip Technology
Jul 29, 2016 · Artificial Intelligence

Deep Learning for Multi‑field Categorical Data: Click‑Through Rate Prediction and Model Comparisons

This article presents a deep‑learning‑based approach to multi‑field categorical data, explains FM and FNN embeddings, compares several click‑through‑rate prediction models on Criteo and iPinYou datasets, and demonstrates that factorisation‑machine‑supported neural networks significantly outperform logistic regression and other baselines.

AdvertisingNeural Networksclick-through rate prediction
0 likes · 15 min read
Deep Learning for Multi‑field Categorical Data: Click‑Through Rate Prediction and Model Comparisons
dbaplus Community
dbaplus Community
Mar 9, 2016 · Artificial Intelligence

How AlphaGo’s Deep Neural Networks Achieve Human‑Level Go Mastery

This article breaks down AlphaGo’s breakthrough architecture—four specialized neural‑network modules, Monte‑Carlo Tree Search, and deep reinforcement learning—to explain how the system moved from imitation learning to self‑improvement and ultimately defeated top human Go players.

AlphaGoDeep LearningGo AI
0 likes · 15 min read
How AlphaGo’s Deep Neural Networks Achieve Human‑Level Go Mastery