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Large Language Models
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Real sessions from our LLM curriculum — PyTorch, transformers, tokenization, prompt engineering and deployment.
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Watch Our Demo Lessons
Get a feel for our teaching style and curriculum depth before you enroll
1
PyTorch Fundamentals
Transformer Architecture for LLMs
2
Understanding LLMs
Tokenization, Embeddings & Hugging Face
3
Prompt Engineering
Structured LLM Outputs
4
Understanding LLMs
Quantization & Local Deployment
Full Curriculum
What You'll Master
Deep, hands-on coverage of how modern LLMs actually work and how to build with them
PyTorch & Transformers
- Tensors & Autograd Basics
- Transformer Architecture Deep-Dive
- Attention Mechanisms
- Training Loops from Scratch
Tokenization & Embeddings
- Byte-Pair Encoding & Tokenizers
- Embedding Spaces
- Hugging Face Ecosystem
- Working with Pretrained Models
Prompt Engineering
- Structured Output Design
- Few-Shot & Chain-of-Thought Prompting
- Function Calling
- Evaluating LLM Responses
Deployment & Optimization
- Model Quantization
- Local Deployment (Ollama & Friends)
- Inference Cost Optimization
- Building Real LLM Apps
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