Chapter 4: The Bigram Model - Simplest Possible Language Model

1. 1Implement a counting-based bigram model to predict the next token in a sequence.
2. 2Calculate the probability of each token given its preceding token using the bigram model.
3. 3Establish a loss baseline using the bigram model before moving to neural networks.
4. 4Compare the performance of the bigram model with more complex language models.
5. 5Use the bigram model as a foundation for building more advanced language models.

This lesson is useful for machine learning engineers and data scientists who want to understand the fundamentals of language modeling and build a foundation for more complex models.

💡 The bigram model is a simple yet effective way to establish a baseline for language modeling tasks and can be used as a foundation for building more complex models.

📚 Learn to implement a bigram model, the simplest possible language model, to predict the next token in a sequence! #machinelearning #language modeling