Learning plan cover

I want to learn the fundamentals of LLMs

LEARNING PLAN

I want to learn the fundamentals of LLMs

Large Language Models are revolutionizing how we interact with technology and information. This learning plan provides essential knowledge for developers, AI enthusiasts, and professionals who want to understand LLM capabilities, limitations, and future potential, enabling them to make informed decisions about implementing and working with this transformative technology.

ByCommunity User
1 h 56 m
9 coursesUpdated 63 days ago
LLM Fundamentals: Attention Is All You NeedWhat Is ChatGPT Doing ... and Why Does It Work?The Mathematical Magic Behind Large Language Models9 sources

How This Learning Plan Was Made

This plan was crafted by BeFreed's proprietary AI to help you learn I want to learn the fundamentals of LLMs with ease. It is curated from in-depth research on the topic and structured around the most effective learning journeys proven by BeFreed users.

Each episode delivers bite-sized, high-impact lessons distilled from world-class sources — bestselling books, research papers, and expert insights. Together, they form a sophisticated yet accessible path to mastering I want to learn the fundamentals of LLMs.

What You'll Learn

  • Understand how LLMs function and the technology behind them
  • Master the neural architecture and transformer mechanisms of LLMs
  • Apply advanced techniques like retrieval-augmented generation and context engineering
  • Evaluate ethical implications and alignment challenges of LLM technology
  • Develop skills to leverage LLMs as AI agents for practical applications

Understand the foundational concepts and mechanisms behind LLMs, including how they work and the technology that powers them.

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6 sources
LLM Fundamentals: Attention Is All You Need

Deep dive into how ChatGPT and large language models actually work, from the revolutionary attention mechanism to probabilistic text generation. Perfect for understanding the core concepts behind modern AI.

9 m
What Is ChatGPT Doing ... and Why Does It Work? cover
What Is ChatGPT Doing ... and Why Does It Work?

In-depth analysis of ChatGPT's AI mechanisms and effectiveness

9 m

Dive into the neural architecture, transformer mechanisms, and mathematical principles that enable large language models to process and generate text.

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Understanding LLMs: A Comprehensive Overview from Training to InferenceMathematical Foundations of Large Language Models - MediumTransformer Architecture Explained With Self-Attention MechanismLLM Transformer Model Visually Explained
6 sources
The Mathematical Magic Behind Large Language Models

Explore how LLMs transform raw text into intelligent responses through tokenization, transformer architecture, and neural networks. From training on massive datasets to generating coherent output.

10 m
Make Your Own Neural Network cover
Make Your Own Neural Network

Step-by-step guide to building neural networks for beginners.

8 m
Hands-On Machine Learning with Scikit-Learn and TensorFlow cover
Hands-On Machine Learning with Scikit-Learn and TensorFlow

Step-by-step guide to machine learning with Python

8 m

Explore cutting-edge LLM techniques, including retrieval-augmented generation, context engineering, and how LLMs are transforming into AI agents.

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What is RAG? - Retrieval-Augmented Generation AI Explained - AWSWhat is Retrieval Augmented Generation (RAG)? - DatabricksRAG vs Traditional LLMs: Key Differences - Galileo AIIntroduction to RAG (Retrieval Augmented Generation) and Vector ...
6 sources
RAG vs LLMs: The AI Revolution Explained

Deep dive into Retrieval-Augmented Generation and vector databases - discover how RAG transforms AI accuracy by 13%, cuts costs 20x, and why it's replacing traditional LLMs in enterprise applications.

20 m
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What Is ChatGPT Doing ... and Why Does It Work?Make Your Own Neural NetworkChatGPT For DummiesArtificial Intelligence and Generative AI for Beginners
13 sources
Building AI Agents: Beyond Chatbots

Discover how LLMs have evolved from text generators to action-taking AI agents. Learn the neural architecture behind these systems and how to build your own agents that can understand goals and execute complex tasks autonomously.

32 m

Understand the ethical implications, alignment problems, and future potential of large language models in society and technology.

The Alignment Problem cover
The Alignment Problem

A riveting exploration of AI's ethical challenges and the quest to align machine learning with human values.

11 m
Artificial Intelligence cover
Artificial Intelligence

A captivating exploration of AI's potential and limitations, demystifying the hype and addressing crucial questions about machine intelligence.

9 m

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