NLP → Transformers: Prerequisite Learning Path

NLP → Transformers: Prerequisite Learning Path

Before learning Large Language Models (LLMs) such as GPT, BERT, Gemini, or Claude, it is important to understand some foundational concepts in Natural Language Processing (NLP) and Deep Learning.

This roadmap provides a quick step-by-step learning path from basic NLP concepts to Transformers , which are the backbone of modern LLMs.

1. Introduction to Natural Language Processing

Understand what Natural Language Processing (NLP) is and why it is important in AI.

Topics to Learn

• What is NLP
• Applications of NLP
• Text preprocessing
• Tokenization
• Stopword removal
• Stemming and Lemmatization

Learning Resource

• Natural Language Processing In 5 Minutes | What Is NLP And How Does It Work?

2. Word Representations

Machines cannot understand raw text directly.
Words must be converted into numerical vectors.

Topics to Learn

• Bag of Words
• TF-IDF
• Word Embeddings

Learning Resource

• What are Word Embeddings?

3. Neural Networks for NLP

Deep learning models are widely used for NLP tasks.

Topics to Learn

• Basics of Neural Networks
• Forward propagation
• Backpropagation
• Activation functions
• Neural networks for text processing

Learning Resource

• Neural Network In 5 Minutes | What Is A Neural Network?

4. Sequence Models

Text is sequential in nature, so models must understand context and order of words.

Topics to Learn

• Recurrent Neural Networks (RNN)
• Long Short-Term Memory (LSTM)
• Gated Recurrent Units (GRU)
• Sequence modeling

Learning Resource

• [Illustrated Guide to Recurrent Neural Networks] (https://youtu.be/LHXXI4-IEns?si=IiUFY9F5uMYyCptP)

5. Transformers

Transformers are the foundation of modern LLMs.

They overcome the limitations of RNNs and allow models to process sequences in parallel using attention mechanisms.

Topics to Learn

• Attention mechanism
• Self-attention
• Transformer architecture
• Encoder–decoder structure
• Positional encoding

Learning Resource

• What are Transformers (Machine Learning Model)?

Next Step: Large Language Models

Once you understand Transformers, you can move to:

• BERT
• GPT models
• Generative AI
• Retrieval Augmented Generation (RAG)
• Agentic AI

These concepts are build on the Transformer architecture.