11
Fine-Tuning and Adaptation of LLMs
Research Scientist Guide
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Preface
1
Statistics & Mathematics
2
Data Structures with Python
3
Algorithms Coding Patterns
4
ML Data Pipelines
5
Machine Learning Models
6
Deep Learning Models
7
Time-Series Analysis and Models
8
Deep Reinforcement Learning
9
Model Evaluation and Analysis
10
Large Language Models
11
Fine-Tuning and Adaptation of LLMs
12
Model Compression, Deployment, and Efficiency
13
Computer Vision and Visual Intelligence
14
Multimodal
15
Causality
16
Trustworthy & Secure AI
17
Scalability & Optimization
18
Statistical Analysis & Testing
References
Appendices
A
Weighted least squares
B
Generalized least squares
C
Synchrony of parametric trends
D
Analysis of precipitation extremes and climate projections
E
Practice exercises
11
Fine-Tuning and Adaptation of LLMs
10
Large Language Models
12
Model Compression, Deployment, and Efficiency