Introduction to Large Language Models

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Advanced
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About the Course

Explore advanced NLP techniques with a focus on Large Language Models. Gain hands-on experience with pretrained transformers and learn to apply them to real-world business problems. Ideal for learners looking to deepen their expertise in machine learning and NLP.

Learning Outcomes

By the end of this course, participants will be able to:

  • Explain the architecture and working principles of transformers and attention mechanisms.
  • Implement transformer encoders and decoders from scratch.
  • Develop and fine-tune large language models like BERT and GPT for various NLP tasks.
  • Evaluate and deploy LLMs effectively in real-world applications.
  • Utilize RAG techniques to enhance the capabilities of LLMs in information retrieval and generation tasks.

Curriculum

  • Module 1: Attention Mechanism

    Overview:

    This module explores the fundamental concepts of transformers and the attention mechanism, including hands-on implementation from scratch.

    Topics to Cover:

    • What is the Attention Mechanism?
    • Build the Attention Mechanism from scratch

  • Module 2: Transformer Encoder

    Overview:

    This module allows participants to gain insights into the architecture and functioning of the transformer encoder.

    Topics to Cover:

    • Why Transformers?
    • Different encoding methods
    • Implementing encoders

  • Module 3: Transformer Decoder

    Overview:

    This module focuses on understanding the transformer decoder’s role in generating sequences and how to build it from scratch.

    Topics to Cover:

    • What is Decoder?
    • Implementation of decoder
    • Encoder and Decoder comparison

  • Module 4: BERT

    Overview:

    This module delves into large language models, starting with an introduction to BERT, its architecture, and implementation.

    Topics to Cover:

    • What is LLM?
    • Steps in a BERT model
    • Prepare data for LLM models
    • Training and Prediction using BERT

  • Module 5: GPT

    Overview:

    This module examines the principles behind the Generative Pre-trained Transformer (GPT) and its decoding strategies.

    Topics to Cover:

    • What is a GPT model?
    • Steps in creating a GPT model
    • Apply different decoding methods to model

  • Module 6: Pretrained Models

    Overview:

    This module teaches various pretrained models and their applications in NLP tasks.

    Topics to Cover:

    • How to use pretrained models?
    • Applying various pretrained models to solve business problem
    • Compare results of various pretrained models

  • Module 7: Transformers for NLP Models

    Overview:

    This module investigates the application of transformers in different NLP scenarios, including many-to-one, many-to-many, and one-to-many tasks.

    Topics to Cover:

    • How to apply transformers?
    • Many to one, many to many, and one to many differences

Tools

Python
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