NVIDIA: Fundamentals of NLP and Transformers

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NVIDIA: Fundamentals of NLP and Transformers Course is the third course of the Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs - Associate Specialization. This course provides learners with foundational knowledge of Natural Language Processing (NLP) and practical skills for working with NLP pipelines and transformer models. It combines theoretical concepts with hands-on exercises to prepare learners for real-world NLP applications. This course covers key NLP topics, including tokenization, text preprocessing techniques, and word embeddings, along with the challenges of handling textual data. Learners will also explore sequence models (RNN, LSTM, GRU) and transformer architectures, gaining practical insights into self-attention mechanisms and encoder-decoder models. The course is structured into two modules, each comprising Lessons and Video Lectures. Learners will engage with approximately 3:00-3:30 hours of video content, covering both theoretical foundations and hands-on practice. Each module includes quizzes to reinforce learning and assess understanding. Course Modules: Module 1: Introduction to NLP: Concepts, Techniques, and Applications Module 2: Sequence Models and Transformers By the end of this course, a learner will be able to: - Understand NLP fundamentals, key tasks, and real-world applications. - Implement NLP techniques, including tokenization, word embeddings, and sequence models. - Explore transformer architecture, self-attention mechanisms, and encoder-decoder models. This course is intended for individuals interested in developing NLP expertise and working with transformer-based models. It is ideal for data scientists, machine learning engineers, and AI specialists seeking hands-on experience in modern NLP techniques.

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