Certification in Large Language Model (LLM)

Learn concepts and architectures behind LLMs, GPT, BERT, T5, and PaLM, Training, Scaling Applications, deployment of LLM

 

[Free Udemy Course] Certification in Large Language Model (LLM)

What you'll learn

  • You 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

 

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