NLP Basics Explained: Tokenization & Embeddings | Beginner Friendly

Key Takeaways

[music] Today, we're going to pull back the curtain on how AI actually understands us. We're talking about the absolute bedrock of natural language processing. Basically, how machines learn to read and understand our language. You know that feeling, right? You type some jumbled up thought into a search bar and somehow it just knows what you're looking for. or you ask a chatbot a really complicated question and it fires back a perfect answer. It honestly feels like magic, but it's not. It's a really, really clever process. And we're about to break it all down. So, the tech behind all of this magic has a name, natural language processing, or NLP for short. And that's exactly what it is. It's this whole area of computer science focused on one huge goal. Teaching machines how to understand, interpret, and even generate human language. All the stuff we do naturally. And here's the best part. You don't need to be a machine learning genius to get it. The whole thing, I mean, pretty much all of modern language AI is built on top of two fundamental foundational building blocks. If you get these two ideas, you'll get the whole game. So, let's get into our first building block. Before a machine can even think about understanding a sentence, it has to do what any of us would do with a big complicated task. You have to break it down into smaller bite-sized pieces. And this process has a special name. It's called tokenization. It's just a fancy word for splitting up raw text into smaller chunks called tokens. That's it. Think of it like taking a sentence and breaking it down into its individual ingredients. Okay, let's use a really simple example to see this in action. Let's take the sentence, I love machine learning. How does a computer actually start to process that? Well, there are a few ways you could do it. You could split by the word, right? I love machine learning. Super simple. But what happens when it sees a word it's never seen before? It kind of breaks. On the other extreme, you could split by character. M, A, C, H, I, N, E. That way, you'll never have an unknown word, but you end up with these crazy long sequences. So the sweet spot, the one that modern models like GPT actually use, is subordenization. It's smart. It breaks words into meaningful parts like learn and ing. This gives it the perfect balance. It can handle new words while still being really efficient. And I really need to stress this point. This first step is absolutely crucial. If the computer messes up the tokenization, it doesn't matter how smart the rest of the AI is. The whole process is starting off on the wrong foot. It's simple. Bad tokenization means bad understanding. Okay, so we've chopped up our sentence into little pieces. But a pile of tokens is just well a pile of tokens. It doesn't mean anything yet. For the machine to actually get it, it needs to understand the meaning of each piece. And that brings us to our second block, which is where things get really, really cool. This next step is called creating an embedding. And at its core, an embedding is just a way to turn our text tokens into a list of numbers, a numerical vector. But it's not just any list of numbers. This vector is specially designed to capture the meaning, the whole vibe of that token. Because remember, computers don't speak English. They speak math. So to make this a little more real, let me ask you a question. When you and I see the word king, a whole bunch of concepts pop into our head, right? Royalty, power, castles. But how does a computer which only gets ones and zeros represent a rich idea like that? It sees it like this. just a string of numbers. This vector, and it can be hundreds of numbers long, is the machine's entire definition of king. It's like a unique numerical fingerprint for that specific concept. And this gets us to the most brilliant idea in all of this. With embeddings, meaning becomes a position in a giant highdimensional space. That vector isn't just a list of numbers. It's a set of coordinates that points to a very specific spot in this enormous meaning space. And once you have this map of meaning, you can do some unbelievable things. Words that mean similar things like king and queen end up right next to each other in this space. And words that are totally different, like car and banana, are super far apart. But here's the craziest part. You can do math with words. If you take the coordinates for king, subtract the coordinates for man, and add the coordinates for woman, the point you land on is incredibly close to the coordinates for queen. It's literally doing algebra with concepts. It's amazing. Okay, so that's cool. But what about a word that has more than one meaning? Take the word bank. Are we talking about a river bank or are we talking about a place you put your money? How can one set of coordinates, one vector, possibly mean both things? This is exactly where the technology got a major upgrade. The older way of doing things used static word embeddings. The word bank had one vector and that was it. It couldn't handle any nuance. But modern NLP uses contextual embeddings. These systems are way smarter. They look at the entire sentence first. So the bank in I sat on the riverbank gets a totally different numerical fingerprint than the bank in I deposited money in the bank. And that is how modern AI finally cracked the code on context. So we have our two building blocks. We have tokenization to break things into pieces and we have embeddings to give those pieces meaning. Now let's put it all together and see the entire assembly line from a sentence we'd write to something a machine can actually understand. So here's a super simplified look at how it works. We start with our raw text that gets fed into the tokenizer and broken into pieces. Each of those unique tokens has a number, an ID. Then those IDs are used to look up the correct numerical vector, the embedding, from a giant table. And finally, these vectors, which are now just packed with meaning, are fed into the big AI model like a transformer, which then does its thing. If you walk away with just one thought, let it be this. It's this simple. Tokenization decides what the pieces of language are, and embeddings decide what those pieces actually mean. One is about the structure, the other is all about the meaning. Okay, so why have we gone through all this? Why is this foundation so incredibly important? Well, it's because basically everything that we think of as language AI today is built right on top of this exact two-step process. The ability to search for an idea, not just a keyword. That's semantic search, and it's powered by embeddings. All the chat bots and large language models that are changing the world, they run on this exact pipeline. Everything from classifying text and screening resumes to recommending your next favorite movie. It all starts with tokenization and embeddings. So, let's bring it all home. The oneline summary is this. Tokenization breaks language into pieces. Embeddings turn those pieces into meaning. That's it. That is the fundamental dance that allows a machine to truly understand what we're saying. >> [music]