🧩 Part 1/8

✔ What is Pruning and Why Do We Need It?
✔ Types of Pruning Techniques
✔ Weight Pruning
✔ Unit Pruning
✔ Structured Pruning
✔ How to Apply Pruning in Machine Learning Models
✔ Pruning Criteria and Algorithms
✔ Pruning Strategies and Schedules
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🧩 Part 2/8

✔ What is Pruning and Why is it Important?
✔ Pre-Pruning: Definition, Advantages, and Disadvantages
✔ Post-Pruning: Definition, Advantages, and Disadvantages
✔ Pruning Criteria: How to Choose the Best Split Point
✔ Comparison of Pre-Pruning and Post-Pruning on a Real Dataset
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🧩 Part 3/8

✔ Decision Trees and Overfitting
✔ What are Decision Trees?
✔ How to Measure Overfitting?
✔ Cost-Complexity Pruning
✔ What is Cost-Complexity Pruning?
✔ How to Find the Optimal Subtree?
✔ How to Choose the Best Alpha Value?
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🧩 Part 4/8

✔ Decision Trees and Error Rate
✔ How to Build a Decision Tree
✔ How to Measure the Error Rate of a Decision Tree
✔ Minimum Error Pruning
✔ What is Minimum Error Pruning
✔ How to Apply Minimum Error Pruning to a Decision Tree
✔ Example of Minimum Error Pruning
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🧩 Part 5/8

✔ Decision Trees and Overfitting
✔ What are Decision Trees?
✔ How to Measure Overfitting?
✔ Reduced Error Pruning
✔ How does Reduced Error Pruning Work?
✔ Advantages and Disadvantages of Reduced Error Pruning
✔ Example of Reduced Error Pruning
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🧩 Part 6/8

✔ What is Pruning and Why is it Important?
✔ Challenges of Pruning Neural Networks
✔ Pruning Methods for Neural Networks
✔ Weight Pruning
✔ Unit Pruning
✔ Structured Pruning
✔ Lottery Ticket Hypothesis
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🧩 Part 7/8

✔ What is Pruning and Why is it Important?
✔ Pruning Strategies for Convolutional Neural Networks
✔ Filter Pruning
✔ Channel Pruning
✔ Layer Pruning
✔ Pruning Algorithms and Tools
✔ Magnitude-based Pruning
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🧩 Part 8/8

✔ What is Pruning and Why is it Important?
✔ Pruning Methods
✔ Pruning Criteria
✔ Pruning Recurrent Neural Networks
✔ Challenges and Benefits
✔ Pruning Techniques for RNNs
✔ Experimental Results and Analysis
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