NLP
Learn how AI understands and generates human language. Explore transformers, attention mechanisms, and modern NLP.
Prerequisites
Complete Level 2: Deep Learning
🎯What You'll Learn
- ✓Text tokenization and word embeddings
- ✓Recurrent networks and LSTMs for sequential data
- ✓The transformer architecture and attention mechanism
- ✓BERT and GPT model architectures
- ✓Text generation and machine translation
💪Skills You'll Gain
🏆Learning Outcomes
📖Interactive Modules (10)
Text Tokenization Playground
Learn tokenization techniques, breaking text into words, subwords, or characters for NLP models.
Word Embeddings Visualizer
Understand Word2Vec, GloVe, and how words are represented as dense vectors in space.
RNN & LSTM Basics
Master recurrent neural networks and LSTMs for sequential data and time-series processing.
Sentiment Analysis Demo
Build models to analyze text sentiment: positive, negative, or neutral emotional tone.
Attention Mechanism Explorer
Understand attention mechanisms that allow models to focus on relevant parts of input.
Transformer Architecture
Deep dive into Transformer architecture, the foundation of modern NLP and large language models.
BERT Breakdown
Explore BERT (Bidirectional Encoder Representations from Transformers) and pre-training techniques.
GPT Architecture Visualizer
Understand GPT architecture, autoregressive language modeling, and how it generates text.
Text Generation Playground
Experiment with text generation, temperature, top-k sampling, and creative language models.
Machine Translation Demo
Learn neural machine translation, seq2seq models, and translating between languages.