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ML, GenAI & LLM Interview Questions ( With details )
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ML, GenAI & LLM Interview Questions ( With details )

About this course

Our expertly designed AI , Machine Learning, GenAI, and LLM practice tests are built to help learners stay ahead in the fast-evolving field of Artificial Intelligence and Large Language Models (LLMs). Whether you're preparing for a career in AI, studying for machine learning certifications, or aiming to crack technical interviews at top tech companies, our tests offer a hands-on, industry-aligned experience.We cover a wide range of in-demand AI topics, including LLM architectures like GPT, LLaMA, BERT, and DistilBERT, as well as CLIP, Transformer models, and the foundational Attention Mechanism. Learners gain practical understanding of LLM pretraining, fine-tuning techniques such as LoRA, and implementation using the popular Hugging Face Transformers library.Our questions are modeled after real-world interview problems asked by leading companies in the AI and tech space. These practice tests are ideal for data scientists, ML engineers, AI developers, and technical professionals looking to upskill in the age of Generative AI.Master cutting-edge tools, stay updated with the latest AI trends, and boost your confidence to thrive in the digital future with our Udemy courses.Course Topics Covered: Model Architectures Hugging Face Transformers LibraryModel Compression TechniquesLLM Model Lifecycle:Embedding ModelsDiffusion ModelsVision-Language ModelsMultimodal ModelsRetrieval Augmented Generation (RAG) Systems:LLM Model D

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56/100

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32/45
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What you'll learn

  • understand different LLM architectures like GPT, LLaMA, BERT, and DistilBERT
  • gain practical knowledge of Hugging Face Transformers library
  • learn about model compression techniques and the LLM model lifecycle
  • familiarize with retrieval augmented generation systems
Machine Learning Artificial Intelligence #fine-tuning #hugging face #technical interviews #gpt #transformers #data science #multimodal models #model compression #llm #ai #bert #attention mechanism #ml #embedding models #diffusion models
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