ou will learn about the Introduction to LLMs including fundamentals of Artificial Intelligence and Natural Language Processing (NLP)
Understand what makes Large Language Models (LLMs) unique in today’s AI landscape. You will also explore the key features and real-world capabilities of LLMs,
You will develop a solid understanding of the core concepts and architectures behind LLMs, beginning with the basics of neural networks and deep learning.
You will explore the role of attention mechanisms and study the Transformer architecture, which underpins most modern LLMs
Learn how tokenization and contextual embeddings work, and you’ll study popular architectures like GPT, BERT, T5, and PaLM in detail
You will gain in-depth knowledge of Training and Scaling LLMs. You will explore how large datasets are collected and preprocessed
You will study model optimization techniques, such as mixed-precision training, and learn how distributed computing enables the training of very large models
You will review real-world training practices behind advanced LLMs like OpenAI GPT, Meta LLaMA, and Google PaLM
You will learn about the Applications of LLMs across different industries, including text generation, summarization, chatbot creation, virtual assistants
Learn , sentiment analysis, customer insights, question answering systems, code generation, and automation.
You will master the process of fine-tuning and customizing LLMs to fit specific domains. You will study the techniques behind adapting pre-trained models
Work on real-world case studies including healthcare, legal, and e-commerce use cases. You will also fine-tune a pre-trained LLM
You will explore the strategies for the deployment and optimization of LLMs, including best practices for model inference, reducing latency
You will also learn about model compression techniques such as pruning and quantization, and explore various APIs and frameworks like OpenAI API, Hugging Face
You will understand the ethical and security considerations related to LLMs, including issues of bias, fairness, responsible AI practices, data privacy risks
Learn misinformation, deepfakes, and regulatory compliance. You will analyze real-world ethical dilemmas and explore strategies for building more trustworthy AI
You will explore the future of LLMs by studying advances in multimodal models like GPT-4 Vision, emerging trends in model efficiency, including sparse models
Learn memory-efficient architectures, and discover how LLMs are being applied in cross-disciplinary domains like healthcare, education, and scientific research
