Summary

We’ve covered a lot in just this first chapter! Let’s quickly recap some of the top themes before moving on. First, we looked at the art of pretraining and fine-tuning, including a few key pretraining objects such as masked language and causal language modeling. We learned about the Transformer model architecture, including the core self-attention mechanism with its variant. We looked at state-of-the-art vision and language models, including spotlights on contrastive pretraining from natural language supervision, and scaling laws for neural language models. We learned about encoders, decoders, and their combination, which are useful throughout the vision and language domains today.

Now that you have a great conceptual and applied basis to understand pretraining foundation models, let’s look at preparing your dataset: part one.