Attention-based Models and Transformers for Natural Language Processing

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Attention mechanisms in natural language processing (NLP) allow models to dynamically focus on different parts of the input data, enhancing their ability to understand context and relationships within the text. This significantly improves the performance of tasks such as translation, sentiment analysis, and question-answering by enabling models to process and interpret complex language structures more effectively. Begin this course by setting up language translation models and exploring the foundational concepts of translation models, including the encoder-decoder structure. Then you will investigate the basic translation process by building a transformer model based on recurrent neural networks without attention. Next, you will incorporate an attention layer into the decoder of your language translation model. You will discover how transformers process input sequences in parallel, improving efficiency and training speed through the use of positional and word embeddings. Finally, you will learn about queries, keys, and values within the multi-head attention layer, culminating in training a transformer model for language translation.