💐Linear Neural Networks for Regression💐

[TOC]

Linear Regression

Basics

• 3.1.2. Vectorization for Speed
• 3.1.3. The Normal Distribution and Squared Loss
• 3.1.4. Linear Regression as a Neural Network
• 3.1.5. Summary
• 3.1.6. Exercises

3.2. Object-Oriented Design for Implementation

• 3.2.1. Utilities
• 3.2.2. Models
• 3.2.3. Data
• 3.2.4. Training
• 3.2.5. Summary
• 3.2.6. Exercises

3.3. Synthetic Regression Data

• 3.3.1. Generating the Dataset
• 3.3.2. Reading the Dataset
• 3.3.3. Concise Implementation of the Data Loader
• 3.3.4. Summary
• 3.3.5. Exercises

3.4. Linear Regression Implementation from Scratch

• 3.4.1. Defining the Model
• 3.4.2. Defining the Loss Function
• 3.4.3. Defining the Optimization Algorithm
• 3.4.4. Training
• 3.4.5. Summary
• 3.4.6. Exercises

3.5. Concise Implementation of Linear Regression

• 3.5.1. Defining the Model
• 3.5.2. Defining the Loss Function
• 3.5.3. Defining the Optimization Algorithm
• 3.5.4. Training
• 3.5.5. Summary
• 3.5.6. Exercises

3.6. Generalization

• 3.6.1. Training Error and Generalization Error
• 3.6.2. Underfitting or Overfitting?
• 3.6.3. Model Selection
• 3.6.4. Summary
• 3.6.5. Exercises

3.7. Weight Decay

• 3.7.1. Norms and Weight Decay
• 3.7.2. High-Dimensional Linear Regression
• 3.7.3. Implementation from Scratch
• 3.7.4. Concise Implementation
• 3.7.5. Summary
• 3.7.6. Exercises