22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

Self-paced bootcamp on Generative AI. Tutorials on ML fundamentals, Ollama, LLMs, RAGs, LangChain, LangGraph, Fine-tuning, DSPy & AI Agents (CrewAI), (Using ChatGPT, gpt-oss, Claude, Qwen, Gemma, Llama, Gemini)

In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

A guide to LLM hacking: fundamentals, prompt injection, offense, and defense

Recent advancements propelled by large language models (LLMs), encompassing an array of domains including Vision, Audio, Agent, Robotics, Fundamental Sciences such as Mathematics, and Ominous.

lm-scratch-pytorch - The code is designed to be beginner-friendly, with a focus on understanding the fundamentals of PyTorch and implementing LLMs from scratch,step by step.

A comprehensive AI learning roadmap covering Python fundamentals, mathematics, machine learning, deep learning, LLMs, and agentic systems — focused on hands-on projects, practical tools, and real-world deployment.

https://graphacademy.neo4j.com/courses/llm-fundamentals/

This repo covers LLM, Agents, MCP Tools, Skills concepts with sample codes: LangChain & LangGraph, AWS Strands Agents, Google Agent Development Kit, Fundamentals.

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Build an AI communication analyzer from scratch to understand how AI products actually work. Learn prompt engineering, reasoning pipelines, and local LLM integration using Node.js - no frameworks, no abstractions, just fundamentals

Exploring the fundamentals and advanced concepts of Large Language Models (LLMs) through practical implementations and collaborative learning.

Notes for CS294/194-196: Large Language Model Agents (Fall 2024, UC Berkeley), summarizing 12 lectures on LLM fundamentals, reasoning, planning, tool use, agent design, and applications. A resource for learners and AI researchers.

Hands-on course for building autonomous AI agents in Go: LLM fundamentals, tool/function calling, RAG, evals, multi-agent, safety, and observability

CLI tool for fast, fundamentals-driven equity screening with LLM-assisted reading of 10-K filings

Learn the fundamentals of LLMs & Retrieval-Augmented Generation (RAG) through hands-on notebooks, then build your own AI-powered Q&A chat app using Streamlit. Bring your own documents and get started with our video guide!

Multi-agent trading backtest framework (Rigid): Sentiment, Fundamental, and Valuation agents feed a Reasoning Agent that makes trading decisions; a Portfolio Manager handles allocation. Uses manually curated news data and historical prices to evaluate LLM performance in financial analysis.

Researchers have made remarkable and groundbreaking achievements in exploring the mechanisms and the fundamental nature of intelligence in AI models, particularly LLMs. This paper repository aims to document these milestones, providing a quick overview of the advancements in this field.

Complete 90-day learning path for AI security: ML fundamentals → LLM internals → AI threats → Detection engineering. Built from first principles with NumPy implementations, Jupyter notebooks, and production-ready detection systems.

Manimator is an multi-agent LLM animation tool designed to create engaging educational videos on any topic. Whether it's visualizing complex mathematics or explaining the fundamentals of blockchain, Manimator transforms intricate subjects into concise, accessible clips for audiences of all ages.

This is the code repository for AI Security Fundamentals - LLM Threats and OWASP Principles 2026, published by Packt

Combines LLMs with fundamental analysis for algorithmic trading. Features: LLM evaluation of S&P 500 income statements Automated stock scoring and selection Strategy backtesting Performance visualization Uses Python, pandas, numpy, matplotlib, Groq API, and yfinance.

Ebbiforge: 10 Million agents. Zero LLM cost. Rust-core swarm intelligence that outperforms traditional frameworks on 8 fundamental benchmarks.

Course on LLMs and Prompt Engineering. Covers LLM fundamentals, training, evaluation, prompting techniques, RAG, multimodal capabilities, agents, MCP, and LLM-powered software engineering tools.

An AI-powered portfolio analysis assistant for Zerodha Kite users, built to learn MCP + LLM + RAG + Agentic AI fundamentals

The demo notebooks used in Class 2 to elaborate on the LLM fundamentals

Educational, from-scratch implementation of a LLaMA-style LLM using PyTorch to explore Transformer architecture fundamentals.

Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices.

TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions.