MindSpore

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
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Quick Start

View code: https://gitee.com/mindspore/docs/blob/r1.0/tutorials/tutorial_code/lenet/lenet.py

View the full tutorial：https://www.mindspore.cn/tutorial/training/en/r1.0/quick_start/quick_start.html

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