What is a Layer in AI

In this video, you can learn what a layer is in the context of artificial intelligence (AI) and how it processes information using vectors (or arrows). Layers play a crucial role in transforming input data into meaningful outputs within neural networks, especially in large language models (LLMs) and transformer architectures.

Key Topics:

• What is a layer in AI and neural networks?
• The process of vector normalization
• Attention mechanisms in neural networks (Q, K, V)
• The role of Rotary Positional Encoding (RoPE) in AI
• How matrix multiplication works in AI layers

Video overview

A layer takes a list of vectors—representing words in a sentence—and processes them step by step. At the end of this process, the output vectors represent the meaning of the next word or token in a sequence. But how does this happen? A lot of it comes down to normalization, attention, and rotary positional encoding (RoPE).

In this video, we break down the following concepts:

• Normalization: Ensuring all vectors have the same length for effective processing.
• Attention Mechanism: The role of query (Q), key (K), and value (V) vectors in helping the layer focus on relevant parts of the input sequence.
• Rotary Positional Encoding (RoPE): How this technique is used to remind the model of the position of each word in a sentence.
• Linear Unit and Matrix Multiplication: How the layer processes vectors through multiplications to refine the meaning and understanding of the sequence.

By the end of this video, you'll have a clear understanding of how layers work in neural networks, how vectors are transformed, and how attention mechanisms allow models to make more accurate predictions.

More information

• Large Language Models (LLM) - What is AI
• Large Language Models (LLM) - What are LLMs
• Large Language Models (LLM) - Tokenization in AI
• Large Language Models (LLM) - Embedding in AI
• Large Language Models (LLM) - RoPE (Positional Encoding) in AI
• Large Language Models (LLM) - Layers in AI
• Large Language Models (LLM) - Attention in AI
• Large Language Models (LLM) - GLU (Gated Liner Unit) in AI
• Large Language Models (LLMs) - Normalization (RMS or RMSNorm) in AI
• Large Language Models (LLM) - Unembedding in AI
• Large Language Models (LLM) - Temperature in AI
• Large Language Models (LLM) - Model size an Parameter size in AI
• Large Language Models (LLM) - Training in AI
• Large Language Models (LLM) - Hardware acceleration, GPUs, NPUs in AI
• Large Language Models (LLM) - Templates in AI
• Large Language Models (LLM) - Putting it all together - The Architecture of LLama3