Introduction to Transformers and Attention Mechanisms

  • IntermediateLevel

  • 3 hrs 0 minsDuration

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About this Course

  • Build NLP models with real-world applications, applying practical techniques and insights.
  • Master self-attention, multi-head attention & Transformer architectures for NLP tasks
  • Explore RNNs, GRUs & LSTMs to efficiently process sequential data and text inputs.
  • Apply NLP techniques for text classification, generation, and translation with real-world use cases.

Learning Outcomes

Transformers in Action

Understand how Transformers revolutionize NLP models and tasks.

Master in Self-Attention

Master self-attention and multi-head attention mechanisms.

Building NLP Models

Develop models for classification, translation, and generation.

Who Should Enroll

  • AI & ML enthusiasts eager to explore NLP and deep learning models for real-world applications.
  • Data Scientists & Engineers – Professionals looking to master Transformers and self-attention.
  • Students & Researchers – Learners aiming to apply NLP techniques to real-world challenges.

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Apoorv Vishnoi

Head-Training vertical

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

Get this Course Now

With this course you’ll get

  • 3 hour

    Duration

  • Apoorv Vishnoi

    Instructor

  • 4.8

    Average Rating

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility

Frequently Asked Questions

Looking for answers to other questions?

NLP is the field of computer science focused on enabling machines to understand, interpret, and generate human language. It powers applications like chatbots, translation services, and sentiment analysis.

RNNs are neural networks designed to work with sequences. They maintain a form of memory of previous inputs, which is useful for processing language where the order of words matters.

Self-attention is a mechanism that helps a model determine the relevance of each word in a sentence relative to others. It allows the model to weigh different words based on their importance, capturing context and relationships effectively.

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