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 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
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Quick Start

View code: https://gitee.com/mindspore/docs/blob/r1.2/tutorials/tutorial_code/lenet/lenet.py

View the full tutorial: https://www.mindspore.cn/tutorial/training/en/r1.2/quick_start/quick_start.html

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