A PyTorch coding practice platform — covering LLM, Diffusion, PEFT, and more A friendly environment to help you deeply understand deep learning components through hands-on practice. Like LeetCode, but for tensors. Self-hosted. Supports both Jupyter and Web interfaces.

This is a repository of practicing the hands-on LLM from the book titled as "Hands-On Large Language Models" by Jay Alammar, Maarten Grootendorst

Hands-on Jupyter tutorials for practicing robust prompt engineering and minimizing hallucinations in LLM applications.

A hands-on Capture The Flag (CTF) environment designed to practice prompt injection techniques against a Large Language Model (LLM).

"🚀 A job-ready, hands-on repository for practical LLM development! Master prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and more with real-world examples and best practices. Perfect for AI engineers looking to build and deploy powerful language models."

Repository for the Complete Artificial Intelligence Training (by Prof. Fernando Amaral), covering Machine Learning, Deep Learning, LLMs, Generative AI, NLP, Agents, Computer Vision, Anomaly Detection, Genetic Algorithms, Fuzzy Logic, and more. Includes theory and hands-on practice with Python, projects, and notebooks.

Hands on LLM Practice

Hands-on practice with LangChain models covering LLMs, Chat Models, and Embedding Models across OpenAI, Anthropic, Google

Learn prompt engineering best practices for application development ; Discover new ways to use LLMs, including how to build your own custom chatbot; Gain hands-on practice writing and iterating on prompts yourself using the OpenAI API

Hands-on practice projects while learning LangChain from scratch. Covers chat models (OpenAI, Google Gemini, Claude, HuggingFace), embedding models, document similarity, and LLM-based experimentation.

LangChain Tutorial: Hands-on examples for building LLM applications with chat models (OpenAI, Anthropic, Google, Ollama), prompt templates, chains, and RAG implementation. Features ChromaDB integration, document chunking, and embeddings. Complete with environment setup and best practices.

A curated collection of my LLM Engineering Projects, showcasing hands-on implementations in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, and AI workflows. Each project highlights real-world use cases, best practices, and industry-level design patterns for building and evaluating advance AI systems.

This repository contains practice code for LangChain, created while learning from the CampusX LangChain playlist. It includes hands-on experiments and examples to understand core LangChain concepts and LLM workflows using Python.

A repository for applying and practicing stock market concepts using Large Language Models (LLMs). Focus areas include market analysis, quantitative finance, sentiment analysis, stock prediction, and portfolio optimization, providing hands-on tools for researchers and practitioners.

A living workspace for my LLM learning journey, assignments, experiments, and practical projects built as I explore modern Large Language Models. Expect continuous updates: prompt engineering, multi-agent systems, LiteLLM pipelines, RAG, evaluation tools, and everything I learn along the way.

LLM,Agents,Engineering-Hands-on Practice

hands on practice on Large Language Models (LLMs)

Hands-on practice on NLP and LLM applications

Daily hands-on practice with LLMs (Transformers)

Hands - on practice for building LLM from scratch

Personal practice of the project Hands-on-LLMs

Lectures, Hands-on, Practices and Notes while taking up LLM Zoomcamp by DataTalksClub

Repository to give me hands on practice with LLMs and vector database

The aim is to study and to practice llm work from the book titled Hands on LLM

My personal journey into LLM fine-tuning — notes, examples, experiments, and hands-on practice.

Hands-on SQL practice on an IMDb-style database, combined with NLIDB experiments on the Spider dataset using LLMs.

Hands‑on implementations, experiments, and notes on building agentic AI systems using LangChain, LangGraph, RAG pipelines, tool‑using agents, and modern LLM engineering practices.

My personal journey learning AI engineering, machine learning, LLMs, and RAG through hands-on practice and course exercises.

Hands-on practice for essential AI models — from classical ML to cutting-edge LLMs and AI agent systems.

this is a practice git repo to learn and hands-on finetuning a LLM to a specific domain.