LLM Interview Questions.pdf
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Top 50 LLM Interview Questions!
#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels
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🤖🧠 Build a Large Language Model From Scratch: A Step-by-Step Guide to Understanding and Creating LLMs
🗓️ 08 Oct 2025
📚 AI News & Trends
In recent years, Large Language Models (LLMs) have revolutionized the world of Artificial Intelligence (AI). From ChatGPT and Claude to Llama and Mistral, these models power the conversational systems, copilots, and generative tools that dominate today’s AI landscape. However, for most developers and learners, the inner workings of these systems remain a mystery until now. ...
#LargeLanguageModels #LLM #ArtificialIntelligence #DeepLearning #MachineLearning #AIGuides
🗓️ 08 Oct 2025
📚 AI News & Trends
In recent years, Large Language Models (LLMs) have revolutionized the world of Artificial Intelligence (AI). From ChatGPT and Claude to Llama and Mistral, these models power the conversational systems, copilots, and generative tools that dominate today’s AI landscape. However, for most developers and learners, the inner workings of these systems remain a mystery until now. ...
#LargeLanguageModels #LLM #ArtificialIntelligence #DeepLearning #MachineLearning #AIGuides
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🎓 Stanford has released a new course: “Transformers & Large Language Models”
The authors are the Amidi brothers, and three free lectures are already available on YouTube. This is probably one of the most systematic introductory courses on modern LLMs.
Course content:
• Transformers: tokenization, embeddings, attention, architecture
• #LLM basics: Mixture of Experts, decoding types
• Training and fine-tuning: SFT, RL, LoRA
• Model evaluation: LLM/VLM-as-a-judge, best practices
• Tricks: RoPE, attention approximations, quantization
• Reasoning: scaling during training and inference
• Agentic approaches: #RAG, tool calling
If you are already familiar with this topic — it’s a great opportunity to refresh your knowledge and try implementing some techniques from scratch.
https://cme295.stanford.edu/syllabus/
https://t.iss.one/CodeProgrammer🌟
The authors are the Amidi brothers, and three free lectures are already available on YouTube. This is probably one of the most systematic introductory courses on modern LLMs.
Course content:
• Transformers: tokenization, embeddings, attention, architecture
• #LLM basics: Mixture of Experts, decoding types
• Training and fine-tuning: SFT, RL, LoRA
• Model evaluation: LLM/VLM-as-a-judge, best practices
• Tricks: RoPE, attention approximations, quantization
• Reasoning: scaling during training and inference
• Agentic approaches: #RAG, tool calling
If you are already familiar with this topic — it’s a great opportunity to refresh your knowledge and try implementing some techniques from scratch.
https://cme295.stanford.edu/syllabus/
https://t.iss.one/CodeProgrammer
Please open Telegram to view this post
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