• views: 217
• date: 8 April 2025
• posted by: voska89

Understand the Fundamentals of Large Language Models (LLMs) like BERT, RoBERTa, GPT, LLAMA with Python, Google Colab

What you'll learn

large language models (LLMs) fundamentals
encoder-only transformer architectures (BERT, RoBERTa etc.)
decoder-only transformer architectures (GPT, LLaMA etc.)
transfer learning and fine-tuning
retrieval-augmented generation (RAG)

Requirements

machine learning fundamentals
Python programming fundamentals

Description

Unlock the power of Large Language Models (LLMs) and bring cutting-edge AI to your projects! This beginner-friendly yet comprehensive course takes you deep into the world of transformer-based models — from foundational architectures like BERT and RoBERTa, to generative giants like GPT and Meta's LLaMA.But we don't stop there.You'll also explore Retrieval-Augmented Generation (RAG) — one of the most powerful methods to enhance LLMs with real-time, context-aware information retrieval. Learn how RAG bridges the gap between static models and dynamic, knowledge-grounded generation — perfect for applications like chatbots, enterprise search, and AI assistants.Whether you're a beginner Python developer or someone curious about how LLMs really work, this course will give you the theory, hands-on skills, and real-world insights to work confidently with modern AI tools.What You'll LearnSection 1 - Transformersword embeddingspositional embeddings and encodingself-attention mechanismmaskingmulti-head architecturehow to train a transformer architecturetransformer architectures: GPT, BERT and LLaMASection 2 - Encoder-Only ArchitecturesBERT fundamentalspre-training and fine-tuning the modelthe[CLS] tokenBERT and RoBERTasentiment analysis, text classification and question answering with BERTSection 3 - Decoder-Only ArchitecturesGPT and LLaMA fundamentalsreinforcement learning from human feedback (RLHF)fine-tuning decoder-only architecturesLoRA and QLoRAfine-tuning models on custom datasetSection 4 - Retrieval-Augmented Generation (RAG)what is RAG?semantic search and vector databasesLSH and HNSW algorithmsusing RAG with PDF filesSection 5 - Prompt Engineeringprompt engineering fundamentalszero-shot promptingfew-shot promptingchain of thoughts (CoT)prompt chaining methodsJoin the course today and start your journey into the world of Large Language Models and Retrieval-Augmented Generation. Whether you're building smarter apps, enhancing your AI knowledge, or simply exploring the future of language technology — this course will give you the tools and confidence to level up.Enroll now and start building with the AI models shaping the future. Let's get learning!

Who this course is for

Beginner Python developers who are curious about generative AI and large language models (LLMs)
Homepage:

https://www.udemy.com/course/learn-large-language-models-llms-with-python-and-langchain/

   
   
   

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