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移动端部署
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2021-03-02 20:16:46
移动端部署
移动端部署
本模块介绍了飞桨的端侧推理引擎Paddle-Lite:
Paddle Lite
:简要介绍了 Paddle-Lite 特点以及使用说明。
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安装说明
Pip安装
Linux下的PIP安装
MacOS下的PIP安装
Windows下的PIP安装
Conda安装
Linux下的Conda安装
MacOS下的Conda安装
Windows下的Conda安装
Docker安装
Linux下的Docker安装
MacOS下的Docker安装
从源码编译
Linux下从源码编译
MacOS下从源码编译
Windows下从源码编译
飞腾/鲲鹏下从源码编译
申威下从源码编译
兆芯下从源码编译
昆仑XPU芯片安装及运行飞桨
附录
使用教程
整体介绍
基本概念
Tensor概念介绍
广播 (broadcasting)
升级指南
版本迁移工具
模型开发
10分钟快速上手飞桨(PaddlePaddle)
数据集定义与加载
数据预处理
模型组网
训练与预测
资源配置
自定义指标
模型存储与载入
模型导出ONNX协议
VisualDL 工具
VisualDL 工具简介
VisualDL 使用指南
动态图转静态图
基本用法
内部架构原理
支持语法列表
InputSpec 功能介绍
报错信息处理
调试方法
推理部署
服务器端部署
安装与编译 Linux 预测库
安装与编译 Windows 预测库
C++ 预测 API介绍
C 预测 API介绍
Python 预测 API介绍
移动端部署
Paddle-Lite
模型压缩
分布式训练
分布式训练快速开始
使用FleetAPI进行分布式训练
昆仑XPU芯片运行飞桨
飞桨对昆仑XPU芯片的支持
飞桨框架昆仑XPU版安装说明
飞桨框架昆仑XPU版训练示例
自定义OP
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hello paddle: 从普通程序走向机器学习程序
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飞桨高层API使用指南
模型保存及加载
使用线性回归预测波士顿房价
计算机视觉
使用LeNet在MNIST数据集实现图像分类
使用卷积神经网络进行图像分类
基于图片相似度的图片搜索
基于U-Net卷积神经网络实现宠物图像分割
通过OCR实现验证码识别
人脸关键点检测
通过Sub-Pixel实现图像超分辨率
自然语言处理
用N-Gram模型在莎士比亚文集中训练word embedding
IMDB 数据集使用BOW网络的文本分类
使用注意力机制的LSTM的机器翻译
使用序列到序列模型完成数字加法
时序数据
通过AutoEncoder实现时序数据异常检测
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