Transformers: In-Depth Breakdown of Each Layer

In this video, we take you through a comprehensive breakdown of the Transformer architecture, explaining each layer in detail to help you understand how these models power state-of-the-art AI systems.

You'll learn about:

Embeddings: How input data is converted into high-dimensional vectors.
Positional Encodings: The method transformers use to capture the order of sequences.
Multi-Head Attention: How transformers focus on different parts of the input simultaneously.
Self-Attention: The mechanism that enables transformers to weigh the importance of each word in relation to others.
Masked Multi-Head Attention: How transformers handle sequence generation tasks like language modeling.
Normalization: The role of layer normalization in stabilizing model training.
Encoder and Decoder: How transformers process input and generate output through these two main components.
By the end of this video, you'll have a solid grasp of each building block of transformers, enabling you to understand how they achieve such impressive results in natural language processing and beyond.

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