Foundation Models Course

Welcome to the Foundation Models Course! This repository contains all the resources, code, and materials you need to follow along with the course.

Table of Contents

1. Introduction to Foundation Models
2. Recurrent Neural Networks (RNNs)
3. Convolutional Neural Networks (CNNs)
4. Sequence-to-Sequence Models and Attention Mechanisms
5. Transformer Architecture
6. Early Transformer Variants
7. Optimizing Transformers for Efficiency
8. Parameter-Efficient Model Tuning
9. Understanding Large Language Models (LLMs)
10. Scaling Laws in AI
11. Instruction Tuning and Reinforcement Learning from Human Feedback (RLHF)
12. Efficient Training of LLMs
13. Optimizing LLM Inference
14. Compressing and Sparsifying LLMs
15. Effective LLM Prompting Techniques
16. Vision Transformers (ViTs)
17. Diffusion Models and Their Applications
18. Image Generation with AI
19. Multimodal Pretraining Techniques
20. Large Multimodal Models
21. Enhancing Models with Tool Augmentation
22. Retrieval-Augmented Generation
23. State Space Models
24. Ethics and Bias in AI
25. Model Explainability and Interpretability
26. Deploying and Monitoring AI Models
27. Data Augmentation and Preprocessing
28. Federated Learning
29. Adversarial Attacks and Model Robustness
30. Real-World Applications of Foundation Models

Course Overview

This course provides an in-depth look at foundation models, including their architecture, training techniques, and applications. Whether you're a beginner or an experienced practitioner, you'll find valuable insights and practical skills to advance your understanding of modern AI.

1. Introduction to Foundation Models

• Definition and significance
• Examples and applications

2. Recurrent Neural Networks (RNNs)

• Basic concepts
• Types of RNNs
• Applications and limitations

3. Convolutional Neural Networks (CNNs)

• Architecture
• Key operations (convolution, pooling, etc.)
• Applications in image processing

4. Sequence-to-Sequence Models and Attention Mechanisms

• Sequence-to-sequence models
• Attention mechanism

5. Transformer Architecture

• Transformer architecture
• Self-attention mechanism

6. Early Transformer Variants

• Variants and improvements over the original Transformer

7. Optimizing Transformers for Efficiency

• Techniques for improving transformer efficiency

8. Parameter-Efficient Model Tuning

• Methods for tuning models with fewer parameters

9. Understanding Large Language Models (LLMs)

• Overview of LLMs
• Key models and their impact

10. Scaling Laws in AI

• Principles and significance

11. Instruction Tuning and Reinforcement Learning from Human Feedback (RLHF)

• Techniques for tuning models with instructions and reinforcement learning from human feedback

12. Efficient Training of LLMs

• Methods for optimizing training efficiency

13. Optimizing LLM Inference

• Techniques for faster and more efficient inference

14. Compressing and Sparsifying LLMs

• Methods for model compression and sparsification

15. Effective LLM Prompting Techniques

• Strategies for effective prompting

16. Vision Transformers (ViTs)

• Applying transformers to vision tasks

17. Diffusion Models and Their Applications

• Overview and applications

18. Image Generation with AI

• Techniques for generating images with models

19. Multimodal Pretraining Techniques

• Training models on multiple modalities

20. Large Multimodal Models

• Overview of large multimodal models

21. Enhancing Models with Tool Augmentation

• Enhancing models with tool integration

22. Retrieval-Augmented Generation

• Improving models with retrieval mechanisms

23. State Space Models

• Overview and applications

24. Ethics and Bias in AI

• Addressing ethical considerations and biases

25. Model Explainability and Interpretability

• Techniques for model interpretability

26. Deploying and Monitoring AI Models

• Best practices for deploying and monitoring models

27. Data Augmentation and Preprocessing

• Techniques for data augmentation and preprocessing

28. Federated Learning

• Overview and applications

29. Adversarial Attacks and Model Robustness

• Understanding and mitigating adversarial attacks

30. Real-World Applications of Foundation Models

• Case studies and examples

Getting Started

To get started with the course, clone this repository and follow the instructions in the individual module folders.

git clone https://github.com/yourusername/foundation-models-course.git
cd foundation-models-course

Prerequisites

• Basic understanding of machine learning and deep learning concepts
• Python programming skills

Contributing

We welcome contributions! Please read our Contributing Guidelines for more details.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to customize it further according to your needs. If you need any specific content or additional sections, let me know!