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 the Data Processing

Build Networks

Customizing and Using Loss Function
Custom Operator
Migrating Training Scripts from Third Party Frameworks
Deep Probabilistic Programming
Implementing High-order Automatic Differentiation
Quantum Neural Network

Model Optimization

Debugging in PyNative Mode
Using Dump in the Graph Mode
Custom Debugging Information
Training Process Visualization
Auto Augmentation
Evaluating the Model during Training
Incremental Operator Build

Performance Optimization

Distributed Training
Enabling Mixed Precision
Enabling Graph Kernel Fusion
Applying a Gradient Accumulation Algorithm
Application of Single-Node Tensor Cache

Model Compression

Applying Quantization Aware Training
Applying Post Training Quantization

Model Security and Privacy

Improving Model Security with NAD Algorithm
Protecting User Privacy with Differential Privacy Mechanism
Protecting User Privacy with Suppress Privacy
Testing Model Security Using Fuzz Testing
Using Membership Inference to Test Model Security

Application

Computer Vision
Natural Language Processing
High Performance Computing
Using MindSpore on the Cloud

MindSpore

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Applying Post Training Quantization
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Applying Post Training Quantization

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