Introduction to Transformers and Attention Mechanisms
IntermediateLevel
3 Hrs Duration
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.
Course Curriculum
Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.
1. Understanding RNN
2. Back Propogation in RNN
3. Types of RNN
4. Building a basic classification model
5. Word Embeddings
6. Hands on : Building a RNN model with word indexing
7. Advanced RNN Architecture
8. Hands on : Advanced RNN Architecture
9. Understanding GRUs
10. Hands on: Bi-Directional GRU model
11. Understanding Long Short Term Memory (LSTM) Network
12. Hands on: Bi-Directional LSTM model.
1. Introduction to Seq2Seq Models
2. Working of Encoder Decoder in Traning and Testing Page
3. Introduction to Problem Statement: Text Summarization
4. Hands on: Buidling a Seq2Seq Models for Headline Extraction
5. Attention Mechanism
6. Hands On: Encoder Decoder Attention
7. Introduction to Transformers
8. Flow of information in Transformers.
1. Origin of Transformers
2. Pre Trained Transformers : BERT
3. Hands on: Using Pre Trained Transformer BERT
4. Hands On: Headline extraction using T5
5. BERT v/s GPT.
Get this Course Now
With this course you’ll get
- 3 Hours
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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