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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
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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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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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