Enroll Course: https://www.coursera.org/learn/generative-ai-llm-architecture-data-preparation
In the rapidly evolving landscape of Artificial Intelligence, Generative AI and Large Language Models (LLMs) are at the forefront of innovation. For anyone looking to dive deep into this exciting field, IBM’s short course on Coursera, ‘Generative AI and LLMs: Architecture and Data Preparation,’ offers a fantastic entry point. As part of the ‘Generative AI Engineering Essentials with LLMs Professional Certificate,’ this course is a must-take for aspiring and current data scientists, machine learning engineers, deep-learning engineers, and AI engineers.
The course provides a comprehensive overview of generative AI, covering its various types and showcasing its diverse real-world applications. What truly sets this course apart is its clear explanation of different generative AI architectures. Understanding these underlying structures is crucial for anyone wanting to build or deploy these powerful models.
The syllabus is thoughtfully structured. The first module, ‘Generative AI Architecture,’ delves into the significance of generative AI models and their widespread use in content generation across numerous fields. It meticulously explains common architectures and models, highlighting the differences in their training approaches. A particularly exciting part is learning how LLMs are leveraged to build Natural Language Processing (NLP)-based applications. The hands-on experience of building a simple chatbot using Hugging Face’s transformers library is an invaluable practical takeaway.
The second module, ‘Data Preparation for LLMs,’ addresses a critical, often overlooked, aspect of working with LLMs: data preparation. This section equips learners with the skills to prepare data for LLM training, focusing on the implementation of tokenization. You’ll gain a thorough understanding of tokenization methods and the practical use of tokenizers. Furthermore, the course explains the purpose of data loaders and demonstrates how to utilize PyTorch’s DataLoader class. The practical exercises involve implementing tokenization with popular libraries like nltk, spaCy, BertTokenizer, and XLNetTokenizer, and creating a data loader with a collate function for processing text batches. This module is essential for anyone serious about fine-tuning or training their own LLMs.
Overall, ‘Generative AI and LLMs: Architecture and Data Preparation’ is an excellent foundational course. It strikes a perfect balance between theoretical understanding and practical application, making complex concepts accessible. Whether you’re new to generative AI or looking to solidify your knowledge, this IBM course on Coursera is highly recommended for its clarity, practical relevance, and the valuable skills it imparts.
Enroll Course: https://www.coursera.org/learn/generative-ai-llm-architecture-data-preparation