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Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!
Large Language Models (LLMs) have become pivotal tools driving some of the most stunning advancements and applications in today's AI landscape. This hands-on course will equip you with the practical knowledge and skills needed to understand, build, and harness the power of LLMs for solving complex language tasks such as translation, language generation, and more.
Through interactive coding exercises, you'll discover different transformer architectures and how to identify them. You'll explore leveraging pre-trained language models and datasets from Hugging Face for fine-tuning and evaluating your model using advanced metrics that fit LLMs. Finally, you'll find out more about ethical and bias concerns relevant to LLMs and ways to identify these. By the end of this course, you will be able to build LLMs, fine-tune, and evaluate them using specialized metrics while understanding the key challenges and ethical considerations of enabling real-world LLM applications.
Uncover What's Behind the Large Language Models Hype
Large Language Models (LLMs) have become pivotal tools driving some of the most stunning advancements and applications in today's AI landscape. This hands-on course will equip you with the practical knowledge and skills needed to understand, build, and harness the power of LLMs for solving complex language tasks such as translation, language generation, and more.
Discover LLM Architecture and Leverage Pre-Trained Models
Through interactive coding exercises, you'll discover different transformer architectures and how to identify them. You'll explore leveraging pre-trained language models and datasets from Hugging Face for fine-tuning and evaluating your model using advanced metrics that fit LLMs. Finally, you'll find out more about ethical and bias concerns relevant to LLMs and ways to identify these. By the end of this course, you will be able to build LLMs, fine-tune, and evaluate them using specialized metrics while understanding the key challenges and ethical considerations of enabling real-world LLM applications.
