Module 1 - Recap
A quick recap of some of the things we learnt in this module on fundamentals of AI Engineering
In this module, we traced the quiet evolution behind today's LLMs: from simple predictors like linear regression and Naive Bayes, to n-grams and a keyboard powered by counts; from Bag-of-Words/TF-IDF to embeddings that place meaning in vector space; then into neural networks that learn patterns over sequences.
We saw why we predict tokens instead of characters, how a tokenizer turns text into IDs, and how attention led to Transformers — enabling parallelism, long-range context, and scale.
Finally, we met Large Language Models, which combine these pieces with big data, parameters, and compute to generate, reason, and follow instructions.
Recap Quiz
Answer the questions below and hit Submit.
1. What core limitation do n-grams have that embeddings + neural nets address?
2. Why do embeddings help over Bag-of-Words / TF-IDF?
3. What problem did attention first solve in seq2seq models?
4. Why are Transformers easier to scale than RNNs/LSTMs?
5. What's the role of a tokenizer?
6. 'Large' in Large Language Models primarily refers to…