A new series by DataPeCharcha
Your 16-week journey to finally understand Large Language Models. No jargon, just clarity on how models like GPT-4 *really* work.
WEEK 1
We start at the very beginning, exploring how early models predicted text by simply counting which words appeared together most often. It's the simple, statistical foundation for everything that followed.
WEEK 2
This week, we see the first spark of true 'understanding.' Learn how we taught computers that 'dog' and 'puppy' are related, and how RNNs tried to give models a basic memory.
WEEK 3
Early neural nets were forgetful. Discover the clever 'gate' system in LSTMs that gave models a reliable long-term memory, a breakthrough that dominated NLP for years.
WEEK 4
Computers don't read words, they read numbers. We'll break down the essential process of 'tokenization'—turning text into a language machines can finally understand.
WEEK 5
Before the magic happens, we have to prepare the data. Learn how a Transformer gives each word its initial meaning and, crucially, a 'timestamp' so it knows the order of the sentence.
WEEK 6
This is the revolutionary concept that changed everything. We'll use simple analogies to explain how words 'talk' to each other to figure out the true context of a sentence.
WEEK 7
One 'conversation' between words isn't enough. See how Multi-Head Attention allows words to look at the sentence from many different perspectives at once for a richer understanding.
WEEK 8
Assemble the full building block by combining multi-head attention with the position-wise feed-forward network and the stabilization tricks that make deep networks possible.
WEEK 9
How does an LLM learn from the entire internet? We'll cover the simple goal of 'predicting the next word' and the core training loop that makes it all possible.
WEEK 10
A raw model is just a text predictor. Learn about Supervised Fine-Tuning (SFT), the process that turns it into a helpful instruction-follower.
WEEK 11
How do we align models with human values? We'll explore Reinforcement Learning from Human Feedback, the advanced technique that makes models helpful and harmless.
WEEK 12
Once trained, how does a model choose its words? We'll look at the straightforward, deterministic methods that form the basis of text generation.
WEEK 13
To sound less robotic, models need controlled randomness. Learn about temperature and top-k sampling for more diverse, human-like writing.
WEEK 14
Making models bigger is expensive. Discover the Mixture of Experts architecture—a clever 'committee of specialists' that scales to trillions of parameters efficiently.
WEEK 15
We'll explore how LLMs are breaking the text barrier to understand images, audio, and video, moving toward human-like world understanding.
WEEK 16
Learn how to communicate with LLMs effectively. We'll cover techniques for getting better responses and common pitfalls to avoid in real-world usage.