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BST236 Computing

Course Information
Course Information
Chapter 0. Introduction
Chapter 0. Introduction
Chapter 1. Basics of Coding
Chapter 1. Basics of Coding
- Principles
- Code in Style
Chapter 2. Coding with AI
Chapter 2. Coding with AI
Chapter 3. Computing Workflow
Chapter 3. Computing Workflow
Chapter 4. Complexity analysis
Chapter 4. Complexity analysis
- Time complexity
- Space complexity
Chapter 5. Data Structures
Chapter 5. Data Structures
- Lists
- Stack
- Queue
- Hash Table
- NumPy Arrays
- Pandas DataFrame
- Polars
- Multiprocessing
Chapter 6. Discrete Algorithms
Chapter 6. Discrete Algorithms
Chapter 7. Numerical Linear Algebra
Chapter 7. Numerical Linear Algebra
Chapter 8. Optimization
Chapter 8. Optimization
Chapter 9. Neural Networks
Chapter 9. Neural Networks
Chapter 10. Transformer
Chapter 10. Transformer
- Word Vectors
- Attention
Appendix
Appendix
References
References

Table of contents

Overview
Lectures

Overview¶

This chapter covers transformer models with a focus on their applications in natural language processing.

Lectures¶

PyTorch Pitfalls

PyTorch Pitfalls

Junwei Lu, Department of Biostatistics, Harvard T.H. Chan School of Public Health.