Transformer Encoder in 100 Lines of Code

Transformer Encoder in 100 Lines of Code

Dive into a comprehensive 50-minute video tutorial that breaks down the Transformer Encoder architecture into 100 lines of code. Learn about word embeddings, attention heads, dropout, data batching, and the intricacies of the encoder layers. Explore key concepts such as multi-head attention, layer normalization, and feed-forward networks. Gain hands-on experience with PyTorch implementations, including nn.Module and nn.Sequential. Understand the flow of data through the encoder and discover why certain components are crucial for the transformer's performance. Perfect for those looking to deepen their understanding of natural language processing and deep learning architectures.