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