In this module, we’ll cover the basic concepts of neural networks and transformers, which provide the backbone for ChatGPT-style language models. In this module we will discuss:

• The basics of how neural networks can fit any shape to any dataset.
• The basics of how neural networks are trained with backpropagation.
• The basics of how transformers work, including:
• Word embedding
• Position Encoding
• Attention

In this module, we’ll cover the essential matrix algebra that is required when coding neural networks in PyTorch. Specifically, we will discuss:

• Matrix addition and multiplication.
• Why matrix multiplication is so funky.
• Matrix concepts that help us read PyTorch documentation and error messages.
• A walkthrough of all the matrix math required to code a transformer.

In this module, we will code a ChatGPT-like language model from scratch. Specifically, we will:

• Code Position Encoding.
• Code Attention.
• Code a Decoder-Only Transformer from scratch.
• Train our model.
• Use our model.

Training a large-scale language model from scratch is crazy expensive and takes a long time and pretty much nobody does it. Instead, what they take is a pre-trained model and fine-tune it to perform specific tasks. In this module, we’ll learn how to fine-tune a production grade large language model and do it ourselves with GPUs in the cloud. Specifically, we will:

• Load and use a large language model in the cloud and run it on a GPU.
• Fine-tune a large language model on a custom dataset.
• Use the fine-tuned model.