Back to Generative AI Language Modeling with Transformers
Learner Reviews & Feedback for Generative AI Language Modeling with Transformers by IBM
121 ratings
About the Course
This course provides a practical introduction to using transformer-based models for natural language processing (NLP) applications. You will learn to build and train models for text classification using encoder-based architectures like Bidirectional Encoder Representations from Transformers (BERT), and explore core concepts such as positional encoding, word embeddings, and attention mechanisms.
The course covers multi-head attention, self-attention, and causal language modeling with GPT for tasks like text generation and translation. You will gain hands-on experience implementing transformer models in PyTorch, including pretraining strategies such as masked language modeling (MLM) and next sentence prediction (NSP).
Through guided labs, you’ll apply encoder and decoder models to real-world scenarios. This course is designed for learners interested in generative AI engineering and requires prior knowledge of Python, PyTorch, and machine learning. Enroll now to build your skills in NLP with transformers!
Top reviews
AB
Dec 29, 2024
This course gives me a wide picture of what transformers can be.
RR
Oct 10, 2024
Once again, great content and not that great documentation (printable cheatsheets, no slides, etc). Documentation is essential to review a course content in the future. Alas!
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