Creating Sequence Models and Transformers

Get introduced to Creating Sequence Models and Transformers, meet your instructor, and discover the course objectives.

Discover why the order of data matters, real-world applications of sequence models, key challenges, and foundational techniques for processing and modeling sequential information.

Learn how text is broken into tokens, assigned numerical IDs, and prepared for AI models. Explore tokenization strategies and their impact on model performance and costs.

Learn how to build a tokenizer in PyTorch, transforming text into numerical data for NLP models, and compare simple word tokenization with advanced subword techniques used in modern AI.

Learn how word embeddings transform words into vectors, letting machines grasp meaning, relationships, and analogies for smarter NLP applications like search, recommendations, and translation.

Learn to use pre-trained word embeddings in PyTorch, explore GloVe vectors, visualize relationships, solve analogies, and understand static versus contextual embeddings in NLP.

Learn how RNNs, LSTMs, and GRUs model sequences, overcome memory limitations, and enable applications like text prediction, translation, and time series forecasting.

Discover how RNN, LSTM, and GRU networks in PyTorch excel at predicting sequences, comparing their memory, accuracy, and efficiency in character-level text tasks.

Explore how Seq2Seq models use encoder-decoder architectures to transform sequences, enabling tasks like translation, summarization, and chatbot responses.

Build a Seq2Seq model in PyTorch for tasks like translation and Q&A, learning about encoders, decoders, teacher forcing, and the context vector bottleneck.

Learn how attention mechanisms let AI models focus on relevant input parts, overcoming Seq2Seq bottlenecks and improving tasks like translation and summarization.

Learn to enhance Seq2Seq models in PyTorch using attention mechanisms, robust QA evaluation with EM and F1, and data-centric strategies to address complex language tasks.

Explore Transformer architecture, self-attention, multi-head attention, and positional encoding to understand how modern AI models process language efficiently and contextually.

Discover how Transformers process language by exploring tokenization, contextual embeddings, and attention visualization using HuggingFace and BERT for model interpretability.

Learn to rigorously evaluate Transformer models using metrics like EM, F1, BLEU, and ROUGE, and qualitative error analysis for reliable, real-world language tasks.

Learn to implement and interpret evaluation metrics like EM, F1, Precision@k, and MRR for QA models using Hugging Face, and perform error analysis to guide model improvement.

Learn to fine-tune pretrained transformers for extractive Q&A: prepare data, train with Hugging Face, evaluate using SQuAD, EM, and F1, and apply techniques in real-world scenarios.

Compare RNNs (sequential processing) and Transformers (parallel self-attention) for sequence tasks in AI, focusing on strengths, limits, and real-world applications.