NLP Fundamentals: Core Concepts and Architectures
NLP Fundamentals: Core Concepts and Architectures
Master the essential concepts of Natural Language Processing, from text preprocessing to transformer architectures. This course provides a solid foundation in NLP theory and core techniques without diving into production complexities.
Learning Objectives
- Understand text preprocessing and tokenization fundamentals
- Learn traditional and modern word embedding approaches
- Master the transformer architecture and attention mechanisms
- Explore the evolution from RNNs to modern language models
- Implement basic text generation techniques
- Apply NLP to common tasks like classification and named entity recognition
Lessons
Introduction to Text Preprocessing
45 minLearn the essential techniques for preparing text data for NLP tasks, including tokenization methods, stemming, lemmatization, and feature extraction.
Start Lesson →Advanced Tokenization Techniques
60 minDive deep into modern tokenization approaches including BPE, WordPiece, SentencePiece, and other subword tokenization methods.
Start Lesson →Word Embeddings: From Word2Vec to FastText
60 minExplore traditional word embedding techniques like Word2Vec (CBOW and Skip-gram), GloVe, and FastText, understanding their principles and applications.
Start Lesson →Contextual Embeddings and Modern Representations
60 minUnderstand why contextual embeddings outperform traditional approaches, explore MTEB leaderboard, and learn about innovations like CLIP.
Start Lesson →Pre-Transformer Models: RNN, LSTM, and GRU
60 minLearn about recurrent neural networks and their variants that were state-of-the-art before the transformer revolution.
Start Lesson →Transformer Architecture Deep Dive
90 minUnderstand the revolutionary transformer architecture in detail, including attention mechanisms, positional encoding, and the encoder-decoder structure.
Start Lesson →Text Generation: Deterministic Methods
30 minMaster the foundational approaches to text generation from language models, including greedy search and beam search.
Start Lesson →Text Generation: Probabilistic Sampling
35 minExplore advanced probabilistic sampling methods for creative and diverse text generation from language models.
Start Lesson →Evolution of Transformer Models: From BERT to GPT-4
40 minExplore the foundational development of transformer architectures and understand the key innovations that shaped modern NLP.
Start Lesson →Modern Language Models: Understanding the Landscape
30 minGet an overview of the current language model landscape, including key players like Llama 3, Claude 3, Gemini, and Mixtral.
Start Lesson →Essential NLP Tasks and Applications
45 minLearn about fundamental NLP tasks like text classification and named entity recognition, and how to approach them with modern techniques.
Start Lesson →