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Welcome to this course on Generative AI Language Modeling with Transformers.

In your generative AI journey, this course is a first step toward the fundamental concepts of transformer-based models for natural language processing (NLP). This course is ideal for learners who wish to apply transformer-based models for text classification, specifically focusing on the encoder component.

Let’s get into the details.

This course is part of a specialized program tailored for individuals interested in Generative AI engineering. In this course, you will explore the significance of positional encoding and word embedding, understand attention mechanisms and their role in capturing context and dependencies, and learn about multi-head attention. You'll also learn how to apply transformer-based models for text classification, explicitly focusing on the encoder component. Finally, you will learn about language modeling with the decoder mini-GPT.

You will get hands-on opportunities through labs based on attention mechanism and positional encoding, applying transformers for classification, and using transformers for translation. You will also build decoder GPT-like models and encoder models with baby BERT.

After completing this course, you will be able to:

• Apply positional encoding and attention mechanisms in transformer-based architectures to process sequential data.
• Use transformers for text classification.
• Use and implement decoder-based models, such as GPT, and encoder-based models, such as BERT, for language modeling.
• Implement a transformer model to translate text from one language to another.

Who should take this course?

This course is suitable for those interested in AI engineering, such as Deep Learning Engineers, Machine Learning Engineers, and Data Scientists. It includes creating, optimizing, training, and deploying AI models to transform. It is specifically designed for those who want to learn about NLP-based applications, data science, and machine learning.

Recommended background

As this is an intermediate-level course, it assumes you have a basic knowledge of Python and PyTorch. You should also be familiar with machine learning and neural network concepts.

Course content

This course is approximately 7 hours long and divided into two modules. You can complete one module weekly or at your own pace.

Week 1 - Module 1: Fundamental Concepts of Transformer Architecture

In module 1, you will learn positional encoding, which consists of a series of sine and cosine waves. This enables you to incorporate information about the position of each embedding within the sequence using PyTorch.

As you delve into this module, you will explore the concepts of self-attention mechanisms that employ the query, key, and value matrices. You can apply the attention mechanism to word embeddings and sequences. This process helps capture contextual relationships between words. You will also learn about the language modeling and self-attention mechanisms that generate the query, key, and value using the input word embeddings and learnable parameters.

Additionally, you will explore scaled dot-product in attention mechanisms with multiple heads and how the transformer architecture enhances the efficiency of attention mechanisms. You will also learn to implement a series of encoder layer instances in PyTorch.

As you delve into this module, you will learn how transformer-based models are used for text classification, how to create the text pipeline and model, and how to train the model.