Master LLM Optimization: Boost AI Performance & Efficiency
What you'll learn
- Learn to use Google Colab for unleashing the power of Python's text analysis and deep learning ecosystem
- Introduction to the basic concepts around LLMs and Generative AI
- Get acquainted with common Large Language Model (LLM) frameworks including LangChain
- Learning about using the Hugging Face hub for accessing different LLMs
- Introduction to the theory and implementation of LLM Optimization
Requirements
- Prior experience of using Jupyter notebooks
- Prior exposure to Natural Language Processing (NLP) concepts will be helpful but not compulsory
- An interest in using Large Language Models (LLMs) for your own documents
Description
Master LLM Optimization: Boost AI Performance & Efficiency
Unlock the power of Large Language Models (LLMs) with our cutting-edge course, "Master LLM Optimization: Boost AI Performance & Efficiency." Designed for AI enthusiasts, data scientists, and developers, this course offers an in-depth journey into LLMs, focusing on optimization techniques that elevate AI capabilities. Whether you're a beginner in LLM implementation or an experienced practitioner seeking to refine your skills, this course equips you with the knowledge and tools to excel in this rapidly evolving field.
Course Overview:
This course deep dives into LLM frameworks like OpenAI, LangChain, and LLAMA-Index, empowering you to build and fine-tune AI solutions like Document-Reading Virtual Assistants. With a comprehensive curriculum, you'll explore the theory and practical implementation of LLM optimization, gaining hands-on experience with popular LLM models like GPT and Mistral through Hugging Face. By the end of the course, you’ll have mastered advanced techniques for harnessing LLMs, enabling you to develop AI systems that are both efficient and powerful.
Key Learning Outcomes:
Foundations of Generative AI and LLMs: Understand the core concepts of Gen AI and LLMs, laying a solid foundation for more advanced topics.
Introduction to LLM Frameworks: Get hands-on experience with popular LLM frameworks, including OpenAI, LangChain, and LLAMA-Index, enabling you to build and deploy AI applications with ease.
Accessing LLM Models: Learn how to access LLM models via Hugging Face, work with cutting-edge models like Mistral, and implement them effectively.
LLM Optimization Techniques: Discover advanced optimization methods such as quantization, fine-tuning, and scaling, essential for enhancing LLM performance in real-world applications.
Retrieval-Augmented Generation (RAG): Gain insights into RAG and its role in LLM optimization, enabling more accurate and efficient AI responses.
Leveraging LLM Tools for Summarization & Querying: Master using LLM tools for abstract summarization and querying, ensuring you can harness the full potential of large language models.
Why Enroll?
Guided by an expert instructor with an MPhil from the University of Oxford and a data-intensive PhD from Cambridge University, this course offers unparalleled expertise in LLM optimization. You'll benefit from a supportive learning environment, practical assignments, and a community of AI enthusiasts, ensuring a comprehensive understanding of LLM implementation.
Ready to Become an LLM Expert?
Enrol now to transform your AI capabilities, master LLM optimization techniques, and unlock the potential of text data with large language models. Join us and elevate your expertise in AI today!
Who this course is for:
- Students with prior exposure to NLP analysis
- Those interested in using LLM frameworks for learning more about your texts
- Students and practitioners of Artificial Intelligence (AI)
Instructor
- 4.4 Instructor Rating
- 27,678 Reviews
- 143,278 Students
- 60 Courses
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).