Quick Start

Implementing an Image Classification Application
Implementing Simple Linear Function Fitting
Hands-on Installation and Experience

Basic Use

Loading Dataset
Defining the Network
Saving Models
Loading a Model for Inference and Transfer Learning
Publishing Models using MindSpore Hub

Process Data

Converting Dataset to MindRecord
Optimizing Data Processing

Build Networks

Custom Operator
Migrating Training Scripts from Third Party Frameworks

Model Optimization

Debugging in PyNative Mode
Custom Debugging Information
Training Process Visualization
Auto Augmentation
Evaluating the Model during Training

Performance Optimization

Distributed Training
Enabling Mixed Precision
Enabling Graph Kernel Fusion
Applying Gradient Accumulation Algorithm

Model Compression

Applying Quantization Aware Training

Model Security and Privacy

Improving Model Security with NAD Algorithm
Protecting User Privacy with Differential Privacy Mechanism
Testing Model Security Using Fuzz Testing

Application

Computer Vision
Natural Language Processing

MindSpore

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CPU Operator Development

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