Introduction to Generative AI Transformer Models in Python

Explore how transformers encode inputs with multi-head self-attention and position-wise feed-forward networks, enriched by embedding and positional encoding, then decode autoregressively through masked self-attention and encoder-decoder attention.

Master multi-head self-attention, where parallel query, key, and value heads capture diverse relationships. Pair with positional encoding to add order, and apply cross-attention in decoders for translation, boosting transformer efficiency.

Self-attention in transformers weighs input relationships with query, key, and value vectors using scaled dot-product attention, enabling parallel encoding and decoding and multi-head attention for translation and summarization.

Explore how sinusoidal positional encoding adds order information to input embeddings, letting transformers process tokens in parallel while preserving sequence meaning without extra parameters.

Prepare text data for training transformer models using tokenization, subword tokenization, special tokens, padding, and attention masks; split into train, val, and test sets, batch with loaders, and apply augmentation.

Train the transformer by optimizing the loss during training with careful initialization and Adam, using a warmup learning rate, dropout, and weight decay, with data tokenization and attention masks.

Evaluate transformer model performance with metrics like accuracy, precision, recall, F1, and confusion matrix, across translation and summarization tasks, and use bleu, rouge, perplexity, and auc roc to guide improvements.

Explore how transformer models enable text classification in Python, covering data preparation, tokenization, normalization, vectorization to embeddings, and data augmentation techniques like synonym replacement and back translation.

Explore language translation with transformer models, using encoder-decoder architecture, self-attention, and positional encoding. Train on parallel data, subword tokenization, and vocabulary creation; evaluate with blue and rouge.

Discover how transformers power question answering with self-attention and contextual embeddings, including data annotation, squad-style datasets, and evaluating with exact match and F1 in Python.

Fine-tune pre-trained transformer models like BERT and GPT for NLP tasks such as text classification, QA, and named entity recognition. Apply data preparation, task-specific layers, and evaluation to optimize performance.

Explore the bidirectional Bert model, its masked language modeling and next sentence prediction pre-training, and how fine-tuning enables strong performance across search, question answering, classification, and named entity recognition.

Explore generative pre-trained transformer models, notably GPT-3 with 175 billion parameters, using autoregressive decoding and zero-shot and few-shot learning to power chatbots, content creation, and coding.