Explore the fascinating world of large language models in this comprehensive guide. We'll begin by laying a foundation with key concepts such as softmax, layer normalization, and feed forward layers. Next, we'll delve into the first step of these models, tokenization, explaining the difference between word-wise, character-wise, and sub-word tokenization, as well as how they impact the models' understanding and flexibility.

After setting a strong base, we'll dive deeper into the fascinating process of embedding and positional encoding. Learn how these techniques translate human language into a language that models can understand, ultimately creating a space where tokens can relate to one another.

Then, prepare for the main event as we pull back the curtain on attention — the magic that enables our models to understand context when completing sentences. We'll decode complex concepts like the query, key, and value matrices, guiding you through their function in the model's computation process.

Finally, we'll dive into the concept of multi-attention heads and their role in improving a model's ability to handle complex inputs. This video provides an accessible understanding of large language models without drowning you in complex math, offering a perfect balance between clarity and depth. Whether you're a seasoned pro or just getting started in the field of AI, this video promises to enhance your understanding of these incredible computational tools.

0:00 Intro
0:32 Key Concept
3:15 Tokenization
5:25 Embeddings
7:38 Attention
11:57 Multihead Attention
14:55 The Entire Process
21:36 Outro

#LargeLanguageModels #MachineLearning #Attention #MultiAttentionHead

AemonAlgiz Transcript on Youtube

Large Language Models Process Explained. What Makes Them Tick and How They Work Under the Hood!