Introduction to Transformers and Attention Mechanisms

The basic ideas behind transformer topologies and attention processes that underpin contemporary AI models like BERT, GPT, and T5 are introduced in this free intermediate-level course from Analytics Vidhya. Before moving on to encoder-decoder architectures, self-attention, multi-head attention, transformers, and real-world NLP applications like text classification, summarization, translation, and text generation, learners start with sequence models like RNNs, GRUs, and LSTMs. Additionally, the training incorporates practical exercises utilizing pretrained transformer models.

• Provider/Creator: Analytics Vidhya
• Platform: Analytics Vidhya
• Category: NLP
• Level: Intermediate
• Duration: 3 Hours
• Certificate: Yes
• Rating: ★4.8/5
• Direct Course Link: Introduction to Transformers and Attention Mechanisms
• Recommended For: This course is strongly advised for
  • Fans of AI and machine learning who want to comprehend the underlying principles of large language models (LLMs).
  • Machine learning engineers and data scientists seeking hands-on expertise with transformer designs.
  • Researchers and students with an interest in natural language processing (NLP).
  • professionals getting ready for advanced courses in NLP, LLM, or generative AI.
• Prerequisites:
  • fundamental understanding of Python.
  • knowledge of deep learning and machine learning principles.
  • While not required, knowledge of neural networks is beneficial.