Learn concepts and architectures behind LLMs, GPT, BERT, T5, and PaLM, Training, Scaling Applicatios, deployment of LLM
Sub Category
- Other IT & Software
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Objectives
- 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
Pre Requisites
- You should have an interest in AI, Natural Language Processing (NLP), and understanding how modern language models generate text, code, and other media
- A desire to learn deep learning, neural networks, and Transformer architectures.
- Interest in exploring popular LLM tools and APIs for real-world applications across industries.
- Willingness to learn how to build, fine-tune, and deploy LLMs using Python, open-source libraries, and cloud-based AI platforms.
- Familiarity with basic AI/ML concepts and Python programming is recommended.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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