Master and Build Large Language Models
Master and Build Large Language Models
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 17h 15m | 2.95 GB
Instructors: Sebastian Raschka, Abhinav Kimothi
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 17h 15m | 2.95 GB
Instructors: Sebastian Raschka, Abhinav Kimothi
The best way to understand LLMs is to build one yourself. This course gives you that power.
In this engaging liveVideo, veteran AI researcher Sebastian Raschka leads you step by step through the inner workings of a large language model. You'll see and hear Sebastian talk you through each step of the LLM project you build in his bestselling book Build a Large Language Model (From Scratch).
In this liveVideo, you'll explore how to:
- Plan and code all the parts of an LLM
- Prepare a dataset suitable for LLM training
- Fine-tune LLMs for text classification and with your own data
- Use human feedback to ensure your LLM follows instructions
- Load pretrained weights into an LLM
This liveVideo is the perfect orientation to LLMs for software engineers ready to lead AI initiatives or data scientists and ML researchers who want build or adapt their own LLMs.
This unique course also includes six essential prerequisite videos created by AI expert Abhinav Kimothi, author of A Simple Guide to Retrieval Augmented Generation. Abhinav delivers an insightful review of everything from the Python features you need to work on LLMs to advanced PyTorch operations, ensuring you can succeed regardless of your starting point.
prerequisites
We've added six essential prerequisite videos covering everything from Python fundamentals to advanced PyTorch operations, ensuring you can succeed regardless of your starting point.
- Python Environment Setup - Get your development environment perfect from day one
- Python Fundamentals - Master loops, conditionals, variables, functions, methods, and classes
- Vector Mathematics - Build intuitive understanding of the math behind AI
- PyTorch Essentials - From basic tensors to advanced operations
- Neural Network Foundations - Dot products, matrix multiplication, and activation functions
- Deep Learning Building Blocks - Linear layers, loss functions, backpropagation, and gradient calculation
Master and Build Large Language Models
