Tutorials for Beginners

This chapter provides hands-on tutorials to help you explore various use-cases of Kaiwu-PyTorch-Plugin in depth.

Tutorial Overview

Tutorial Name	Task Type	Description
RBM Classification: Handwritten Digit Recognition	Classification	Feature learning and classification on handwritten digits with Restricted Boltzmann Machines (RBMs)
DBN Classification: Deep Belief Network	Classification	Building hierarchical feature representations and performing classification with Deep Belief Networks (DBNs)
BM Generation: Boltzmann Machine Data Generation	Generation	Data generation using fully-connected Boltzmann Machines (BMs)
Q-VAE: Quantum Variational Autoencoder	Generation / Representation	Image generation and representation learning with Quantum Variational Auto-Encoders (Q-VAEs)

Recommended Learning Path

Beginner Track:

1. Start with Quick Start to grasp the basic APIs

2. Proceed to RBM Classification: Handwritten Digit Recognition to understand the RBM workflow

Generative-Model Track:

1. Study BM Generation: Boltzmann Machine Data Generation to see the generative power of Boltzmann Machines

2. Advance to Q-VAE: Quantum Variational Autoencoder for more powerful generative models

Complete Track:

Work through every tutorial in order to master all features of Kaiwu-PyTorch-Plugin.