Become an AI Researcher - LLM, Math, PyTorch, Neural Networks, Transformers

Overview

Master foundational math, PyTorch, neural networks, and Transformers to build expertise in AI research and understand the technology behind modern LLMs.

Syllabus

• Introduction to AI Research
Overview of AI Research Fields
Current Trends and Applications
• Foundational Mathematics for AI
Linear Algebra
Calculus
Probability and Statistics
• Introduction to PyTorch
Setting Up PyTorch
Tensors and Operations
Building Your First Model
• Neural Networks
Perceptron and Multilayer Perceptrons
Activation Functions
Backpropagation and Training
• Advanced Neural Network Concepts
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Regularization Techniques
• Understanding Transformers
Attention Mechanism
Transformer Architecture
BERT, GPT, and Other Transformative Models
• Large Language Models (LLMs)
Language Model Pre-training and Fine-tuning
Practical Applications of LLMs
• Research Methods in AI
Formulating Research Questions
Conducting Literature Reviews
Experiment Design and Evaluation
• Ethical and Societal Implications of AI
Bias and Fairness
Privacy and Security
• Capstone Project
Proposal Development
Implementation and Evaluation
Presentation and Feedback
• Course Conclusion
Recap and Next Steps
Opportunities in AI Research

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Subjects

Computer Science