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Machine Learning

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Overview
Introduction
- 1.1 Basics New
Data Basics
- 1.1 Fundamentals
Supervised Learning
- 1. Linear Regression
- 2. Logistic Regression
  - 2.1 Fundamentals
  - 2.2 Model Mechanics
- 3. Decision Trees
Deep Learning
- 1. Neural Networks
  - 1.1 Fundamentals
  - 1.2 Activation Functions
  - 1.3 MLP
  - 1.4 Debugging
    
    1. Debugging
    2. WandB
- 2. CNN
  - 2.1 CNN Basics
- 3. Transformers
  - 3.1 Fundamentals
Probabilistic Models
Unsupervised Learning
- 1. Introduction
- 1. Fundamentals
  - 1.1 Fundamentals
- 2. Clustering
- 3. Dimenstionality Reduction
  - 3.1 DR Overview
  - 3.2 Kernel PCA
- 4. Anomaly Detection
  - 4.1 AD Overview
- 5. Association Rule Learning
  - 5.1 ARM Overview
Self Supervised Learning
Further Reading

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2.7 Deep Type Clustering

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2.6 Spectral Clustering Next
3.1 DR Overview