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198 articles · Page 2 of 2
Tencent Advertising Technology
Tencent Advertising Technology
May 5, 2020 · Artificial Intelligence

How to Use the TI-ONE SDK to Train Models for the 2020 Tencent Advertising Algorithm Competition

This tutorial walks you through the complete process of using the TI-ONE SDK—including data preparation, dependency installation, session initialization, TensorFlow estimator configuration, job submission, and result monitoring—to train a machine‑learning model for the 2020 Tencent Advertising Algorithm Competition.

TI-ONETencentTensorFlow
0 likes · 7 min read
How to Use the TI-ONE SDK to Train Models for the 2020 Tencent Advertising Algorithm Competition
Programmer DD
Programmer DD
Apr 24, 2020 · Artificial Intelligence

Turn Photos into Studio Ghibli‑Style Anime with AnimeGAN – A Hands‑On Guide

This article introduces AnimeGAN, a lightweight GAN that converts real photos into Japanese anime‑style illustrations, explains its architecture, loss functions, model size advantages, and provides step‑by‑step instructions with code for setting up, training, and testing the TensorFlow implementation.

AnimeGANDeep LearningGaN
0 likes · 8 min read
Turn Photos into Studio Ghibli‑Style Anime with AnimeGAN – A Hands‑On Guide
360 Quality & Efficiency
360 Quality & Efficiency
Apr 17, 2020 · Artificial Intelligence

Extending APEX for Real Distributed Reinforcement Learning with tf2rl

The article examines the limitations of the single‑machine APEX framework in the tf2rl reinforcement‑learning library, proposes a cross‑machine distributed architecture using middleware such as Redis, compares alternative frameworks like EasyRL, and outlines expected performance gains and future development plans.

APEXOff-PolicyTensorFlow
0 likes · 5 min read
Extending APEX for Real Distributed Reinforcement Learning with tf2rl
Didi Tech
Didi Tech
Apr 2, 2020 · Artificial Intelligence

Interview: Didi AI’s DELTA – A Unified Framework for NLP and Speech Model Development

In this interview, Didi AI Labs’ Han Kun explains how the DELTA platform unifies TensorFlow‑based NLP and speech models—supporting tasks from text classification to voice emotion recognition—through a modular, easily deployable architecture, accelerating development, powering Didi products, and now open‑sourced for broader AI collaboration.

AI platformDeltaNLP
0 likes · 14 min read
Interview: Didi AI’s DELTA – A Unified Framework for NLP and Speech Model Development
58 Tech
58 Tech
Mar 27, 2020 · Artificial Intelligence

dl_inference: Open‑Source General Deep Learning Inference Service

dl_inference is an open‑source inference platform that simplifies deployment of TensorFlow and PyTorch models in production, offering unified gRPC access, load‑balanced multi‑node serving, GPU/CPU options, customizable pre‑ and post‑processing, and extensible architecture for future AI workloads.

AI inferenceDeep LearningOpen-source
0 likes · 11 min read
dl_inference: Open‑Source General Deep Learning Inference Service
DataFunTalk
DataFunTalk
Feb 10, 2020 · Artificial Intelligence

Real‑Time Intelligent Anomaly Detection Platform at Ctrip: Integrating Flink and TensorFlow (Prophet)

The article describes Ctrip's Prophet platform, which combines Flink real‑time stream processing with TensorFlow deep‑learning models to provide intelligent, low‑latency anomaly detection, replacing traditional rule‑based alerts and addressing challenges such as holiday traffic and model scalability.

AIAnomaly DetectionDeep Learning
0 likes · 13 min read
Real‑Time Intelligent Anomaly Detection Platform at Ctrip: Integrating Flink and TensorFlow (Prophet)
58 Tech
58 Tech
Jan 15, 2020 · Artificial Intelligence

Mobile AI Vehicle and VIN Recognition: From TensorFlow to TensorFlow Lite Deployment on Android and iOS

This article details how the 58 Used‑Car mobile team built, trained, and optimized TensorFlow‑based object‑detection models for on‑device vehicle and VIN code recognition, covering data preparation, model conversion to TF‑Lite, performance improvements, engineering integration on Android/iOS, and real‑world deployment results.

AndroidTensorFlowTensorFlow Lite
0 likes · 14 min read
Mobile AI Vehicle and VIN Recognition: From TensorFlow to TensorFlow Lite Deployment on Android and iOS
AntTech
AntTech
Jan 7, 2020 · Artificial Intelligence

Interview with AI Expert Tang Yuan: From Art and Mathematics to Open‑Source Leadership and Distributed Machine‑Learning Infrastructure at Ant Financial

This interview chronicles Tang Yuan’s journey from an art‑focused childhood and mathematics studies in the US to becoming a leading AI specialist at Ant Financial, highlighting his open‑source contributions, the creation of TensorFlow Chinese textbooks, and his insights on career growth, community building, and work‑life balance.

AIAnt FinancialTensorFlow
0 likes · 20 min read
Interview with AI Expert Tang Yuan: From Art and Mathematics to Open‑Source Leadership and Distributed Machine‑Learning Infrastructure at Ant Financial
Alibaba Cloud Native
Alibaba Cloud Native
Jan 2, 2020 · Artificial Intelligence

Create an AI-Powered Poem Generator Using Alibaba Cloud Function Compute

This article explains how Alibaba Cloud Function Compute can be used for AI model serving, walks through a three‑step deployment of a TensorFlow‑based Chinese poem generator, compares serverless with traditional ECS setups, and discusses cold‑start mitigation, cost optimization, and monitoring features.

AI model servingFuncraftFunction Compute
0 likes · 14 min read
Create an AI-Powered Poem Generator Using Alibaba Cloud Function Compute
58 Tech
58 Tech
Dec 20, 2019 · Artificial Intelligence

Deep Learning Platform on Kubernetes: Architecture, Resource Management, Offline Training and Online Inference

The article presents a comprehensive overview of 58.com’s AI platform built on Kubernetes, detailing its layered architecture, resource scheduling, offline training pipelines, debugging environment, distributed TensorFlow/PyTorch training, performance benchmarks, and online inference services, highlighting how the system empowers various business units with scalable AI capabilities.

KubernetesPyTorchTensorFlow
0 likes · 11 min read
Deep Learning Platform on Kubernetes: Architecture, Resource Management, Offline Training and Online Inference
MaGe Linux Operations
MaGe Linux Operations
Nov 20, 2019 · Artificial Intelligence

How North Korea Built a Homegrown AI Facial‑Recognition Smartphone

North Korea’s newly unveiled “Blue Sky” smartphone incorporates a homegrown AI facial‑recognition system built on CNNs, MTCNN, MobileFaceNets and TensorFlow, showcasing how the isolated nation is advancing edge AI despite operating solely on its internal CentOS‑based intranet.

AIDeep LearningNorth Korea
0 likes · 7 min read
How North Korea Built a Homegrown AI Facial‑Recognition Smartphone
360 Tech Engineering
360 Tech Engineering
Nov 13, 2019 · Artificial Intelligence

Text Anti‑Spam Techniques and TextCNN Model for Real‑Time Spam Detection on the Huajiao Platform

This article introduces the Huajiao platform's text anti‑spam architecture, analyzes spam categories and challenges, compares rule‑based and machine‑learning approaches, details traditional NLP methods and the TextCNN deep‑learning model, provides its TensorFlow implementation, and describes the online deployment workflow.

CNNNLPTensorFlow
0 likes · 14 min read
Text Anti‑Spam Techniques and TextCNN Model for Real‑Time Spam Detection on the Huajiao Platform
Huajiao Technology
Huajiao Technology
Nov 12, 2019 · Artificial Intelligence

Text Anti‑Spam Detection with TextCNN: From Traditional Methods to Online Deployment

This article introduces the challenges of text‑based spam on the Huajiao platform, reviews traditional rule‑based and machine‑learning classification methods, explains the TextCNN architecture for robust character‑level detection, and details its TensorFlow Serving deployment for real‑time anti‑spam services.

CNNTensorFlowanti-spam
0 likes · 16 min read
Text Anti‑Spam Detection with TextCNN: From Traditional Methods to Online Deployment
ITPUB
ITPUB
Oct 22, 2019 · Artificial Intelligence

Master Real-Time Image Augmentation with Keras ImageDataGenerator

This guide explains how Keras ImageDataGenerator performs on‑the‑fly image augmentation—covering rotation, shifts, brightness, shear, zoom, channel shifts, flips, and fill‑mode options—with concise Python code examples and visual results to help prevent overfitting in deep‑learning models.

ImageDataGeneratorKerasTensorFlow
0 likes · 7 min read
Master Real-Time Image Augmentation with Keras ImageDataGenerator
21CTO
21CTO
Oct 19, 2019 · Artificial Intelligence

Explore TensorFlow 1.15, Firefox WebSocket Inspector, and Microsoft DAPR & OAM

This article highlights the latest tech releases, including TensorFlow 1.15’s final minor update with compatibility features, Firefox 70’s new WebSocket inspector for developers, the Windows 10 SDK 1909 update, and Microsoft’s open‑source DAPR and OAM projects aimed at simplifying microservice and cloud‑native application development.

DaprFirefoxOAM
0 likes · 7 min read
Explore TensorFlow 1.15, Firefox WebSocket Inspector, and Microsoft DAPR & OAM
Snowball Engineer Team
Snowball Engineer Team
Oct 17, 2019 · Artificial Intelligence

GPU-Accelerated Model Training Optimizations for Snowball Feed Recommendation System

This article describes the challenges of large‑scale model training for Snowball’s feed recommendation, and details a series of engineering optimizations—including GPU acceleration, multi‑threaded data preparation, TFRecord conversion, compression, and batch‑map reordering—that increased training throughput from 6 k to over 20 k samples per second while reducing CPU and I/O bottlenecks.

GPUModel TrainingTFRecord
0 likes · 15 min read
GPU-Accelerated Model Training Optimizations for Snowball Feed Recommendation System
MaGe Linux Operations
MaGe Linux Operations
Sep 27, 2019 · Artificial Intelligence

Top 10 Python Libraries Every AI Developer Should Master

This article introduces ten essential Python libraries—TensorFlow, Scikit‑Learn, NumPy, Keras, PyTorch, LightGBM, Eli5, SciPy, Theano, and Pandas—detailing their features, typical use cases, and adoption in machine‑learning and data‑science projects, while highlighting each library's performance advantages, community support, and integration capabilities to help developers choose the right tool for their AI workflows.

KerasNumPyPyTorch
0 likes · 15 min read
Top 10 Python Libraries Every AI Developer Should Master
AntTech
AntTech
Sep 11, 2019 · Artificial Intelligence

ElasticDL: An Open‑Source Elastic Deep Learning System Built on TensorFlow 2.0 and Kubernetes

ElasticDL, the first industry‑level open‑source system for elastic deep learning on TensorFlow, leverages Kubernetes‑native scheduling, fault‑tolerance, and TensorFlow 2.0 Eager Execution to dramatically improve cluster utilization, simplify distributed training, and integrate seamlessly with tools like Kubeflow and SQLFlow.

Distributed Deep LearningElasticDLKubernetes
0 likes · 13 min read
ElasticDL: An Open‑Source Elastic Deep Learning System Built on TensorFlow 2.0 and Kubernetes
HomeTech
HomeTech
Sep 4, 2019 · Artificial Intelligence

Accelerating TensorFlow Model Inference with NVIDIA TensorRT: Methods, Experiments, and Results

This article explains how to use NVIDIA TensorRT to accelerate TensorFlow model inference by describing TensorRT architecture, optimization techniques such as layer fusion and precision calibration, detailing the conversion of frozen_graph and saved_model formats, presenting experimental setup and performance comparisons, and summarizing the achieved speed‑up.

Deep LearningModel OptimizationTensorFlow
0 likes · 13 min read
Accelerating TensorFlow Model Inference with NVIDIA TensorRT: Methods, Experiments, and Results
360 Tech Engineering
360 Tech Engineering
Aug 28, 2019 · Artificial Intelligence

Deep Collaborative Filtering Models and Their Implementation in Recommender Systems

This article surveys traditional and deep learning based collaborative filtering techniques—including similarity methods, matrix factorization, explicit and implicit feedback handling, various loss functions, evaluation metrics, and TensorFlow implementations of GMF, MLP, NeuMF, DMF, and ConvMF models—providing practical guidance for building large‑scale recommender systems.

Evaluation MetricsTensorFlowcollaborative filtering
0 likes · 21 min read
Deep Collaborative Filtering Models and Their Implementation in Recommender Systems
360 Tech Engineering
360 Tech Engineering
Aug 28, 2019 · Artificial Intelligence

Understanding TensorFlow Internals with TensorSlow: Computational Graph, Forward/Backward Propagation, and Building an MLP

This article explains how Huajiao Live leverages Spark for data preprocessing and TensorFlow (augmented by the TensorSlow project) for distributed deep‑learning training, detailing computational‑graph concepts, forward and backward propagation, loss construction, gradient‑descent optimization, and a step‑by‑step Python implementation of a multi‑layer perceptron.

Computational GraphDeep LearningMLP
0 likes · 14 min read
Understanding TensorFlow Internals with TensorSlow: Computational Graph, Forward/Backward Propagation, and Building an MLP
Huajiao Technology
Huajiao Technology
Aug 27, 2019 · Artificial Intelligence

Mastering Collaborative Filtering: From Traditional Similarity to Deep Neural Models

This article provides a comprehensive technical overview of collaborative filtering, covering traditional user‑ and item‑based similarity methods, matrix‑factorization approaches for implicit feedback, various loss functions, and a suite of deep neural network models such as GMF, MLP, NeuMF, DMF, and ConvMF, together with implementation details, evaluation metrics, and practical deployment considerations.

Deep LearningRecommendation SystemsSpark
0 likes · 29 min read
Mastering Collaborative Filtering: From Traditional Similarity to Deep Neural Models
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 LearningKerasTensorFlow
0 likes · 29 min read
Deep Learning from Theory to Practice: Neural Networks, Logistic Regression, TensorFlow and Keras for Cat Image Classification
Big Data Technology Architecture
Big Data Technology Architecture
Aug 15, 2019 · Artificial Intelligence

Why Swift May Be the Next Big Thing in Deep Learning

The article explains why Google created Swift for TensorFlow, highlights Swift's strong backing, built‑in automatic differentiation, high performance comparable to C, seamless interoperability with Python, C and C++, low‑level hardware access, and its future role within the MLIR compiler ecosystem for deep learning.

Artificial IntelligenceDeep LearningInteroperability
0 likes · 6 min read
Why Swift May Be the Next Big Thing in Deep Learning
Didi Tech
Didi Tech
Aug 2, 2019 · Artificial Intelligence

How Didi’s Open‑Source DELTA Platform Accelerates NLP and Speech Model Development

At ACL 2019, Didi unveiled DELTA, an open‑source TensorFlow‑based training framework that unifies NLP and speech tasks, offers configurable pipelines, benchmark models, and seamless deployment, enabling AI developers to quickly move from research to production while leveraging Didi’s extensive open‑source ecosystem.

AI platformModel TrainingNLP
0 likes · 6 min read
How Didi’s Open‑Source DELTA Platform Accelerates NLP and Speech Model Development
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 17, 2019 · Artificial Intelligence

How Alibaba Halved BERT Latency for Real‑Time Search

This article details Alibaba's technical challenges with BERT's high resource consumption in online search, analyzes memory and compute bottlenecks using TensorFlow profiling, and presents both TensorFlow‑based tweaks and a custom CUDA implementation that together double throughput and cut latency by about 50%.

AlibabaBERTGPU
0 likes · 9 min read
How Alibaba Halved BERT Latency for Real‑Time Search
360 Tech Engineering
360 Tech Engineering
Jul 2, 2019 · Artificial Intelligence

Understanding TensorFlow Internals with TensorSlow: A Deep Learning Guide

This article explains how TensorFlow powers Huajiao Live's recommendation system, introduces the TensorSlow project for demystifying TensorFlow's core, and walks through deep‑learning fundamentals, computational‑graph concepts, forward and backward propagation, loss construction, gradient‑descent optimization, and building a multi‑layer perceptron with Python code examples.

Computational GraphDeep LearningMLP
0 likes · 13 min read
Understanding TensorFlow Internals with TensorSlow: A Deep Learning Guide
Huajiao Technology
Huajiao Technology
Jul 2, 2019 · Artificial Intelligence

Understanding Deep Learning with TensorFlow: Applications, Computational Graphs, and MLP Implementation

This article introduces deep learning applications at Huajiao Live, explains TensorFlow's computational graph architecture, details core concepts such as placeholders, variables, operations, forward and backward propagation, and provides complete Python-like code examples for building and training a multi-layer perceptron.

Computational GraphDeep LearningMLP
0 likes · 14 min read
Understanding Deep Learning with TensorFlow: Applications, Computational Graphs, and MLP Implementation
Tencent Cloud Developer
Tencent Cloud Developer
Apr 24, 2019 · Artificial Intelligence

Chinese Text Sentiment Classification Using Multi‑layer LSTM: Data Preparation, Model Architecture, and Business Applications

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.

Chinese NLPDeep LearningKeras
0 likes · 23 min read
Chinese Text Sentiment Classification Using Multi‑layer LSTM: Data Preparation, Model Architecture, and Business Applications
Tencent Cloud Developer
Tencent Cloud Developer
Apr 15, 2019 · Artificial Intelligence

Serverless AI Inference with TensorFlow Serving on Tencent Cloud SCF

This tutorial shows how to package a TensorFlow SavedModel for MNIST, upload it to Tencent Cloud Object Storage, create a Python 2.7 SCF function that loads the model with TensorFlow Serving, and expose it via API Gateway as a scalable, server‑less AI inference endpoint.

AI servingAPI GatewayCloud Functions
0 likes · 14 min read
Serverless AI Inference with TensorFlow Serving on Tencent Cloud SCF
iQIYI Technical Product Team
iQIYI Technical Product Team
Apr 4, 2019 · Artificial Intelligence

Principles, Methodology, and Tools for Machine Learning Performance Optimization

The article presents a systematic, top‑down methodology for machine‑learning performance optimization—covering principles, benchmark‑driven loops, foundational hardware and software checks, profiling tools, throughput and latency metrics, and practical techniques for IO, compute, mixed‑precision, and distributed training to maximize resource utilization.

ComputeIOPerformance Optimization
0 likes · 22 min read
Principles, Methodology, and Tools for Machine Learning Performance Optimization
Qunar Tech Salon
Qunar Tech Salon
Mar 27, 2019 · Artificial Intelligence

Profiling TensorFlow Performance with TensorBoard and Timeline

This article explains how to use TensorBoard and the Timeline tool to monitor TensorFlow GPU utilization, identify operation bottlenecks, and visualize execution times, including code examples and steps for exporting and merging profiling data for repeated runs.

GPU monitoringTensorBoardTensorFlow
0 likes · 7 min read
Profiling TensorFlow Performance with TensorBoard and Timeline
58 Tech
58 Tech
Feb 20, 2019 · Artificial Intelligence

Building and Deploying Language Models for Text Quality Evaluation and Generation

This article explains the concepts, training pipeline, deployment formats, and practical applications of language models—particularly LSTM‑based models—for evaluating and generating text quality in a real‑world rental listing platform, highlighting data preparation, model training, and online serving techniques.

DeploymentLSTMLanguage Model
0 likes · 16 min read
Building and Deploying Language Models for Text Quality Evaluation and Generation
Ctrip Technology
Ctrip Technology
Feb 13, 2019 · Artificial Intelligence

Understanding TensorFlow Extended (TFX): Concepts, Data Preparation, and Model Deployment

This article introduces TensorFlow Extended (TFX), illustrating practical TensorFlow examples such as ship trajectory classification, insurance premium adjustments, and car auction pricing, then explains TFX’s data validation, schema generation, model analysis, and deployment options to streamline machine‑learning pipelines.

AITFXTensorFlow
0 likes · 12 min read
Understanding TensorFlow Extended (TFX): Concepts, Data Preparation, and Model Deployment
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 LearningPythonTensorFlow
0 likes · 14 min read
Introduction to TensorFlow and Building a Simple Neural Network for Image Classification
Xianyu Technology
Xianyu Technology
Jan 2, 2019 · Artificial Intelligence

Xianyu's Flutter and AI-Powered UI-to-Code Innovation: Technical Deep Dive

Xianyu’s 2018 technical breakthroughs combine Flutter/Dart for tri‑platform development, a TensorFlow‑driven UI2Code system that converts screenshots into runnable Flutter code, and the SWAK framework that isolates business logic from middleware, boosting productivity, simplifying legacy code, and showcasing playful engineering anecdotes.

Cross‑Platform DevelopmentFlutterSWAK
0 likes · 6 min read
Xianyu's Flutter and AI-Powered UI-to-Code Innovation: Technical Deep Dive
360 Quality & Efficiency
360 Quality & Efficiency
Dec 28, 2018 · Artificial Intelligence

SRGAN-Based Image Super-Resolution and MNIST Training Tutorial

This tutorial outlines a curriculum covering open‑source examples for enhancing image resolution using SRGAN, explains GAN‑based super‑resolution concepts, details network architectures and perceptual loss, and provides a simple MNIST training walkthrough with code links and resources.

GaNMNISTSRGAN
0 likes · 7 min read
SRGAN-Based Image Super-Resolution and MNIST Training Tutorial
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 28, 2018 · Artificial Intelligence

Elastic Feature Scaling: Boosting Alibaba’s Online Recommendation CTR by 4%

This article describes how Ant Financial’s AI team redesigned TensorFlow to enable elastic feature scaling, introduced a Group‑Lasso optimizer and streaming frequency filtering, compressed models by 90%, and achieved significant CTR and efficiency gains in Alipay’s online recommendation system.

Recommendation SystemsTensorFlowfeature scaling
0 likes · 20 min read
Elastic Feature Scaling: Boosting Alibaba’s Online Recommendation CTR by 4%
21CTO
21CTO
Dec 14, 2018 · Artificial Intelligence

Inside Jeff Dean and Sanjay Ghemawat’s Epic Journey: From Index Crashes to AI Powerhouses

The article chronicles Jeff Dean and Sanjay Ghemawat’s partnership at Google, from the 2000 index failure that threatened the company, through their pioneering work on MapReduce and large‑scale infrastructure, to the creation of TensorFlow and the rise of Google AI, highlighting their unique collaborative style and lasting impact on modern computing.

Artificial IntelligenceGoogleJeff Dean
0 likes · 29 min read
Inside Jeff Dean and Sanjay Ghemawat’s Epic Journey: From Index Crashes to AI Powerhouses
DataFunTalk
DataFunTalk
Nov 29, 2018 · Artificial Intelligence

TensorFlow Technology Development and Practical Applications: Deep Learning Overview, TensorFlow Introduction, and Fashion Design Use Cases

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.

Deep LearningFaster R-CNNTensorFlow
0 likes · 10 min read
TensorFlow Technology Development and Practical Applications: Deep Learning Overview, TensorFlow Introduction, and Fashion Design Use Cases
DataFunTalk
DataFunTalk
Nov 24, 2018 · Artificial Intelligence

Comprehensive Guide to Fine‑Tuning BERT on Chinese Datasets

This article provides a step‑by‑step guide for fine‑tuning Google’s open‑source BERT on Chinese datasets, covering model download, processor customization, code examples, training commands, and insights into the underlying TensorFlow estimator architecture and deployment considerations.

BERTChinese NLPTensorFlow
0 likes · 11 min read
Comprehensive Guide to Fine‑Tuning BERT on Chinese Datasets
MaGe Linux Operations
MaGe Linux Operations
Nov 22, 2018 · Artificial Intelligence

Accelerating TensorFlow Deep Learning: GPU & Distributed Training Techniques

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.

Deep LearningGPU AccelerationTensorFlow
0 likes · 10 min read
Accelerating TensorFlow Deep Learning: GPU & Distributed Training Techniques
21CTO
21CTO
Nov 20, 2018 · Big Data

What Languages and Tools Do Big Data Experts Use? Insights from 31 IT Leaders

Based on interviews with 31 IT leaders from 28 organizations, this article reveals the most popular programming languages, frameworks, and platforms—such as Python, Scala, Spark, Kafka, TensorFlow, and Tableau—currently driving big‑data extraction, analysis, and reporting, and highlights emerging trends and tool preferences.

Big DataKafkaPython
0 likes · 12 min read
What Languages and Tools Do Big Data Experts Use? Insights from 31 IT Leaders
Xianyu Technology
Xianyu Technology
Nov 20, 2018 · Artificial Intelligence

How to Separate Complex Image Foreground from Background Using AI and Classic CV Techniques

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.

Deep LearningTensorFlowcomputer vision
0 likes · 15 min read
How to Separate Complex Image Foreground from Background Using AI and Classic CV Techniques
Meituan Technology Team
Meituan Technology Team
Nov 15, 2018 · Artificial Intelligence

Reinforcement Learning for Meituan's "Guess You Like" Recommendation Ranking

Meituan enhanced its homepage “Guess You Like” recommendation slot by modeling user‑item interactions as a Markov Decision Process and applying an improved DDPG reinforcement‑learning agent that adjusts the ranking trade‑off parameter, uses advantage‑based Q decomposition, shares actor‑critic weights, and runs in a real‑time TensorFlow pipeline, delivering consistent lifts in click‑through, dwell time, and depth.

DDPGMDP ModelingTensorFlow
0 likes · 21 min read
Reinforcement Learning for Meituan's "Guess You Like" Recommendation Ranking
Xianyu Technology
Xianyu Technology
Oct 30, 2018 · Artificial Intelligence

AI‑Driven Image Bug Detection in the Xianyu Mobile App

The Xianyu quality team uses AI—combining a simple CNN, OCR‑LSTM text analysis, and hierarchical image clustering—to automatically spot blank pages, garbled Chinese text, and duplicate screenshots, with an automated retraining pipeline that updates models as the app evolves and plans future widget‑level defect detection.

AITensorFlowbug detection
0 likes · 7 min read
AI‑Driven Image Bug Detection in the Xianyu Mobile App
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 27, 2018 · Artificial Intelligence

How DeepInsight Transforms Deep Learning Model Debugging with Real-Time Visualization

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.

AI platformDeep LearningTensorFlow
0 likes · 17 min read
How DeepInsight Transforms Deep Learning Model Debugging with Real-Time Visualization
Xianyu Technology
Xianyu Technology
Sep 25, 2018 · Artificial Intelligence

TensorFlow Lite Applications and UI2Code at Xianyu (Idle Fish)

Xianyu leverages a custom TensorFlow Lite framework to power AI‑driven features such as dynamic video‑cover selection, video fingerprinting, and furniture recognition for smart rentals, while its UI2Code tool transforms screenshots into pixel‑perfect production UI code, emphasizing extensibility, security, and online model updates.

TensorFlowTensorFlow LiteXianyu
0 likes · 7 min read
TensorFlow Lite Applications and UI2Code at Xianyu (Idle Fish)
Tencent Cloud Developer
Tencent Cloud Developer
Aug 3, 2018 · Artificial Intelligence

Analysis of Google Quickdraw CNN‑RNN Model for Sketch Recognition

The article dissects Google’s Quickdraw sketch‑recognition model, detailing its 1‑D convolutional front‑end, Bi‑LSTM encoder, and softmax classifier, explaining the TFRecord‑based normalization and interpolation steps, why pooling harms accuracy, and how the massive dataset can fuel diverse sequential‑learning applications and product concepts.

CNNRNNSketch Recognition
0 likes · 7 min read
Analysis of Google Quickdraw CNN‑RNN Model for Sketch Recognition
Qizhuo Club
Qizhuo Club
Jul 30, 2018 · Artificial Intelligence

Mastering Inception v3: From Codebase to Rose Recognition with TensorFlow

This article walks through the Inception v3 TensorFlow codebase, explains its design principles, details the training script flags and loss calculations, shows how to fine‑tune the model on a flower dataset, and provides practical tips for building custom datasets and optimizing hyper‑parameters for image classification.

CNNInceptionPython
0 likes · 25 min read
Mastering Inception v3: From Codebase to Rose Recognition with TensorFlow
Didi Tech
Didi Tech
Jun 8, 2018 · Artificial Intelligence

DiDi PS: High-Performance RDMA-Based Parameter Server for Distributed Deep Learning

DiDi PS is a custom RDMA‑based parameter server that uses a ring topology and optimized ibverbs communication to dramatically accelerate distributed deep‑learning training, consistently outperforming OpenMPI, NCCL2, TensorFlow’s built‑in RDMA, and Horovod while providing more stable and scalable synchronization for massive data workloads.

AllreduceParameter ServerPerformance
0 likes · 10 min read
DiDi PS: High-Performance RDMA-Based Parameter Server for Distributed Deep Learning
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.

Batch NormalizationCNNConvolution
0 likes · 24 min read
Upgrading HED Edge Detection to TensorFlow 1.7: Refactored Code and New Layer Techniques
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 26, 2018 · Artificial Intelligence

How TensorFlowRS Supercharges Large‑Scale Search & Recommendation with 10×‑100× Speedups

This article describes TensorFlowRS, an Alibaba‑built extension of TensorFlow that tackles the massive compute and sparse‑feature challenges of search, advertising and recommendation by redesigning the parameter server, adding fail‑over, gradient‑compensation, online‑learning support, advanced training modes and visualisation, achieving up to 100× training speedup and improved model quality.

Parameter ServerRecommendation SystemsTensorFlow
0 likes · 16 min read
How TensorFlowRS Supercharges Large‑Scale Search & Recommendation with 10×‑100× Speedups
Meituan Technology Team
Meituan Technology Team
Apr 4, 2018 · Artificial Intelligence

Performance Optimization of Distributed TensorFlow for WDL Models at Meituan

Meituan‑Dianping identified data‑pipeline, network, and memory‑arena bottlenecks in distributed TensorFlow training of Wide & Deep recommendation models and resolved them by switching to tf.data pipelines, batching TFRecord reads, increasing MALLOC_ARENA_MAX, and moving embedding lookups to parameter servers, achieving 2–3× speedup and near‑linear scaling on up to 32 GPUs.

AFOPerformance OptimizationTensorFlow
0 likes · 12 min read
Performance Optimization of Distributed TensorFlow for WDL Models at Meituan
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
Tencent Cloud Developer
Tencent Cloud Developer
Mar 13, 2018 · Artificial Intelligence

TensorFlow MNIST Tutorial: Environment Setup, Softmax Regression, and CNN Implementation

This beginner‑friendly TensorFlow tutorial by Chen Yidong walks readers through Windows environment setup, explains TensorFlow’s graph‑execution model, and demonstrates both softmax linear regression and a deep convolutional neural network for MNIST, while also covering utility scripts, TensorBoard visualization, and CPU/GPU or multi‑GPU deployment.

CNNGPUMNIST
0 likes · 13 min read
TensorFlow MNIST Tutorial: Environment Setup, Softmax Regression, and CNN Implementation
MaGe Linux Operations
MaGe Linux Operations
Jan 21, 2018 · Artificial Intelligence

Can You Break a WordPress CAPTCHA in 15 Minutes with Machine Learning?

This tutorial shows how to generate a labeled dataset from the open‑source WordPress "Really Simple CAPTCHA" plugin, train a lightweight convolutional neural network using Python, OpenCV, Keras and TensorFlow, and decode real captchas within fifteen minutes, demonstrating the power of modern computer‑vision techniques.

TensorFlowcomputer vision
0 likes · 11 min read
Can You Break a WordPress CAPTCHA in 15 Minutes with Machine Learning?
MaGe Linux Operations
MaGe Linux Operations
Dec 29, 2017 · Backend Development

7 Emerging Python Libraries You Should Explore in 2017

Discover seven lesser‑known Python libraries—from Arrow’s datetime handling and TensorFlow’s machine‑learning power to Zappa’s serverless deployment, Peewee’s lightweight ORM, Sanic’s high‑performance web framework, Bokeh’s interactive visualizations, and Blaze’s data‑analysis capabilities—each poised to gain traction in 2017.

BokehPythonTensorFlow
0 likes · 8 min read
7 Emerging Python Libraries You Should Explore in 2017
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
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
vivo Internet Technology
vivo Internet Technology
Nov 3, 2017 · Artificial Intelligence

Integrating Distributed TensorFlow with Kubernetes: Architecture and Deployment

The article explains how to combine Distributed TensorFlow with Kubernetes—using GlusterFS storage, Deployments for parameter servers, Jobs for workers, service discovery, monitoring, and a Jinja2‑generated YAML template—to create isolated, scalable training clusters with Jupyter and TensorBoard access.

Distributed ComputingGlusterFSKubernetes
0 likes · 12 min read
Integrating Distributed TensorFlow with Kubernetes: Architecture and Deployment
Architects' Tech Alliance
Architects' Tech Alliance
Oct 10, 2017 · Artificial Intelligence

An Overview of a Three-Day Introductory TensorFlow Tutorial

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

AIDeep LearningTensorFlow
0 likes · 5 min read
An Overview of a Three-Day Introductory TensorFlow Tutorial
Architecture Digest
Architecture Digest
Aug 15, 2017 · Artificial Intelligence

Why AI Engineers Must Understand Basic Infrastructure: From Big Data to Deep Learning

The article explains why AI engineers need foundational infrastructure knowledge—covering big‑data processing, cloud services, containerization, MapReduce, and deep‑learning platforms—to effectively solve real‑world problems, collaborate with teams, and build scalable, maintainable AI solutions.

AI InfrastructureBig DataCloud Computing
0 likes · 14 min read
Why AI Engineers Must Understand Basic Infrastructure: From Big Data to Deep Learning
21CTO
21CTO
Aug 13, 2017 · Artificial Intelligence

How Distributed Machine Learning Platforms Compare: Spark, PMLS, TensorFlow

This article surveys distributed machine‑learning platforms, classifies them into basic data‑flow, parameter‑server, and advanced data‑flow models, examines Spark, PMLS (Petuum), TensorFlow and MXNet, presents performance comparisons on EC2 instances, and discusses bottlenecks, fault tolerance, and future research directions.

Parameter ServerSparkTensorFlow
0 likes · 12 min read
How Distributed Machine Learning Platforms Compare: Spark, PMLS, TensorFlow
High Availability Architecture
High Availability Architecture
Aug 2, 2017 · Artificial Intelligence

A Comparative Study of Distributed Machine Learning Platforms: Design Methods and Evaluation

This article surveys design approaches for distributed machine learning platforms, classifies them into basic dataflow, parameter‑server, and advanced dataflow models, examines examples such as Spark, PMLS, TensorFlow and MXNet, and presents performance evaluations and future research directions.

Parameter ServerSparkTensorFlow
0 likes · 10 min read
A Comparative Study of Distributed Machine Learning Platforms: Design Methods and Evaluation
UCloud Tech
UCloud Tech
Jul 20, 2017 · Artificial Intelligence

Build a Real-Time Facial Expression Recognition Service with UCloud AI-as-a-Service

This guide walks you through training an Inception‑V3 model on the FER2013 dataset with TensorFlow 1.1, packaging the model, and deploying a scalable facial expression recognition API using UCloud's AI‑as‑a‑Service platform, including performance testing against GPU benchmarks.

AIFacial Expression RecognitionModel Deployment
0 likes · 11 min read
Build a Real-Time Facial Expression Recognition Service with UCloud AI-as-a-Service
21CTO
21CTO
Jul 16, 2017 · Artificial Intelligence

Why Every AI Engineer Must Master Infrastructure Basics

In the AI era, engineers need more than cutting‑edge algorithms—they must understand infrastructure, deployment, scalability, and team collaboration, as illustrated by four practical reasons and Google’s architectural breakthroughs that bridge big data, machine learning, and deep learning.

AI InfrastructureCloud ComputingGoogle
0 likes · 17 min read
Why Every AI Engineer Must Master Infrastructure Basics
Hujiang Technology
Hujiang Technology
Jul 6, 2017 · Artificial Intelligence

Comparing Core ML and TensorFlow Performance and API Usage on iOS

The article compares Apple’s Core ML and Google’s TensorFlow on iOS, explaining their architectures, showing performance measurements, and detailing API usage with code examples, highlighting Core ML’s ease of integration versus TensorFlow’s greater flexibility but higher complexity.

Core MLTensorFlowiOS
0 likes · 10 min read
Comparing Core ML and TensorFlow Performance and API Usage on iOS
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 5, 2017 · Artificial Intelligence

Alibaba’s Distributed Training Boosts Neural Machine Translation Speed

Since its 2013 debut, Neural Machine Translation (NMT) has approached human quality, but training costs are high; Alibaba’s team developed a distributed NMT system in 2017, employing data‑parallel, model‑average, BMUF, Downpour SGD, and Ring‑allReduce techniques to cut training time from over 20 days to a few days while maintaining translation quality.

BMUFDownpour SGDModel Averaging
0 likes · 18 min read
Alibaba’s Distributed Training Boosts Neural Machine Translation Speed
Tencent TDS Service
Tencent TDS Service
May 25, 2017 · Artificial Intelligence

Running a CNN on Mobile: TensorFlow & OpenCV Document Detection Guide

This article walks through a real‑world mobile implementation of a convolutional neural network for document detection, covering problem definition, limitations of traditional OpenCV pipelines, the adoption of a HED edge‑detection network, data preparation, model training, TensorFlow library trimming, and deployment tricks for iOS and Android.

CNNDocument DetectionEdge Detection
0 likes · 23 min read
Running a CNN on Mobile: TensorFlow & OpenCV Document Detection Guide
Qunar Tech Salon
Qunar Tech Salon
May 22, 2017 · Artificial Intelligence

Which Deep Learning Framework Is Best for You?

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

AICaffeDeep Learning
0 likes · 13 min read
Which Deep Learning Framework Is Best for You?
MaGe Linux Operations
MaGe Linux Operations
Apr 19, 2017 · Artificial Intelligence

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

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

Deep LearningGPU AccelerationTensorFlow
0 likes · 11 min read
Accelerate TensorFlow Deep Learning with GPU, Multi‑GPU, and Distributed Training
Liulishuo Tech Team
Liulishuo Tech Team
Mar 25, 2017 · Artificial Intelligence

Building a Student Model with TensorFlow: Deep Knowledge Tracing for Adaptive Learning

This article reviews how Liulishuo applied TensorFlow to implement a Deep Knowledge Tracing (DKT) student model for an adaptive learning system, covering the problem background, model architecture, TensorFlow implementation details, multi‑GPU training, and practical deployment considerations.

Deep Knowledge TracingRNNStudent Modeling
0 likes · 12 min read
Building a Student Model with TensorFlow: Deep Knowledge Tracing for Adaptive Learning
Architecture Digest
Architecture Digest
Jan 24, 2017 · Artificial Intelligence

TensorFlow: Large‑Scale Machine Learning on Heterogeneous Distributed Systems – Overview and Implementation

TensorFlow is a dataflow‑based programming model for large‑scale machine learning that uses directed acyclic graphs to represent computations, supports single‑device, multi‑device, and distributed execution with sophisticated node placement, communication, fault‑tolerance, and optimization techniques, and provides tools such as TensorBoard for visualization.

Dataflow GraphDistributed ComputingTensorFlow
0 likes · 13 min read
TensorFlow: Large‑Scale Machine Learning on Heterogeneous Distributed Systems – Overview and Implementation
Qunar Tech Salon
Qunar Tech Salon
Dec 5, 2016 · Artificial Intelligence

Understanding Convolutional Neural Networks for OCR and CAPTCHA Recognition

This article introduces the fundamentals of neural networks for image recognition, explains regression vs classification, describes convolution, pooling and fully connected layers, illustrates the classic LeNet‑5 model on the MNIST dataset, and shows how a TensorFlow‑based CNN can be trained to recognize CAPTCHA images, achieving high accuracy.

CNNLeNet-5OCR
0 likes · 10 min read
Understanding Convolutional Neural Networks for OCR and CAPTCHA Recognition
21CTO
21CTO
Nov 6, 2016 · Artificial Intelligence

How to Build a Scalable AI-Powered Recommendation System with SOA

This article outlines a service‑oriented architecture for a high‑availability personalized recommendation platform, detailing the front‑end, back‑end, crawler, user‑profile modeling, data collection from logs and client events, and processing pipelines using technologies such as Node.js, Python, RabbitMQ/Kafka, MongoDB and TensorFlow.

Full-StackSOATensorFlow
0 likes · 5 min read
How to Build a Scalable AI-Powered Recommendation System with SOA
ITPUB
ITPUB
Sep 21, 2016 · Artificial Intelligence

Deep Learning Platforms Unveiled: From DistBelief to TensorFlow and Real‑World Uses

The article reviews the evolution and challenges of deep learning, outlines major commercial platforms such as DistBelief, COTS, and Adam, compares open‑source frameworks like MXNet, TensorFlow and Petuum, and highlights their architectures, performance metrics, and diverse applications ranging from image recognition to recommendation systems.

AIDeep LearningMXNet
0 likes · 11 min read
Deep Learning Platforms Unveiled: From DistBelief to TensorFlow and Real‑World Uses
GF Securities FinTech
GF Securities FinTech
Sep 7, 2016 · Artificial Intelligence

How Google’s Open‑Source TensorFlow Model Generates Accurate Summaries for Long Texts

Google Brain’s open‑source TensorFlow model tackles long‑text summarization by extracting key information and generating concise headlines, demonstrating state‑of‑the‑art extractive and abstractive techniques, with released code, hyper‑parameter details, and examples that illustrate its performance on news articles.

TensorFlowabstractive summarizationextractive summarization
0 likes · 6 min read
How Google’s Open‑Source TensorFlow Model Generates Accurate Summaries for Long Texts
ITPUB
ITPUB
Sep 6, 2016 · Artificial Intelligence

Deep Learning Platforms: From Google’s DistBelief to Open‑Source MXNet and TensorFlow

The article reviews the evolution, challenges, and commercial and open‑source deep learning platforms—including DistBelief, COTS, Adam, MXNet, TensorFlow, and Petuum—while highlighting real‑world applications such as image recognition, recommendation, sentiment analysis, and crowd monitoring.

AI ApplicationsGPU AccelerationMXNet
0 likes · 10 min read
Deep Learning Platforms: From Google’s DistBelief to Open‑Source MXNet and TensorFlow