Welcome to Kaiwu-Pytorch-Plugin

• Getting Started
  • Overview
  • Kaiwu-PyTorch-Plugin (KPP)
  • Prerequisites
  • 1. Neural Network Basics
  • 2. Boltzmann Machine Structure
  • 3. Energy Function and Probability Distribution
  • Installation Guide
  • Quick Start
  • Beginner Tutorial

• Theoretical Foundations
  • 1.1 The Spin-Glass Analogy
  • 1.2 The Boltzmann Distribution and Equilibrium
  • 1.3 The Need for Noise: Escaping Spurious Minima
  • 1.4 The Renormalization Group: From Microscopic Spins to Macroscopic Features
  • 2.1 A Brief Recap: Linear Neurons and Limitations
  • 2.2 Recurrent Networks and Content-Addressable Memory
  • 2.3 Hebbian Learning as Sculpting Energy
  • 3.1 Defining the Objective: Low Energy for Real Data
  • 3.2 The Intractable Partition Function Problem
  • 3.3 Contrastive Divergence
  • 4.1 The Classic Boltzmann Machine: Visible and Hidden Symmetry
  • 4.2 Restricted Boltzmann Machine (RBM)
  • 4.3 Beyond Single Layers: Stacking for Deep Learning
  • Part I Summary: The Core Bottleneck and the Path Forward

• Module contents
  • Module contents

• FAQ
  • FAQ

• About
  • Release Notes