Update README.md

Author: Awrsha

README.md

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-# CUDA Course
-
-> Note: This course is designed for Ubuntu Linux. Windows users can use Windows Subsystem for Linux or Docker containers to simulate the ubuntu Linux environment.
-
-## Table of Contents
-
-1. [The Deep Learning Ecosystem](01%20Deep%20Learning%20Ecosystem/README.md)
-2. [Setup/Installation](02%20Setup/README.md)
-3. [C C++ Python R and Mojo review](03%20C%20C%2B%2B%20Python%20R%20and%20Mojo%20review)
-4. [Gentle Intro to GPUs](04_Gentle_Intro_to_GPUs/README.md)
-5. [Writing Your First Kernels](05_Writing_your_First_Kernels/README.md)
-6. [CUDA APIs (cuBLAS, cuDNN, etc)](06_CUDA_APIs/README.md)
-7. [Optimizing Matrix Multiplication](07_Faster_Matmul/README.md)
-8. [Triton](08_Triton/README.md)
-9. [PyTorch Extensions (CUDA)](08_PyTorch_Extensions/README.md)
-10. [Final Project](09_Final_Project/README.md)
-11. [Extras](10_Extras/README.md)
-
-## Course Philosophy
-
-This course aims to:
-
-- Lower the barrier to entry for HPC jobs
-- Provide a foundation for understanding projects like Karpathy's [llm.c](https://github.com/karpathy/llm.c)
-- Consolidate scattered CUDA programming resources into a comprehensive, organized course
-
-## Overview
-
-- Focus on GPU kernel optimization for performance improvement
-- Cover CUDA, PyTorch, and Triton
-- Emphasis on technical details of writing faster kernels
-- Tailored for NVIDIA GPUs
-- Culminates in a simple MLP MNIST project in CUDA
-
-## Prerequisites
-
-- Python programming (required)
-- Basic differentiation and vector calculus for backprop (recommended)
-- Linear algebra fundamentals (recommended)
-
-## Key Takeaways
-
-- Optimizing existing implementations
-- Building CUDA kernels for cutting-edge research
-- Understanding GPU performance bottlenecks, especially memory bandwidth
-
-## Hardware Requirements
-
-- Any NVIDIA GTX, RTX, or datacenter level GPU
-- Cloud GPU options available for those without local hardware
-
-## Use Cases for CUDA/GPU Programming
-
-- Deep Learning (primary focus of this course)
-- Graphics and Ray-tracing
+# 🚀 Advanced CUDA Programming & GPU Architecture
+
+> *Unlocking the Power of Parallel Computing*
+
+## 🎯 Course Mission
+Transform complex GPU programming concepts into practical skills for high-performance computing professionals. Master CUDA programming through hands-on projects and real-world applications.
+
+## 🛠️ Core Technologies
+- **CUDA** - NVIDIA's parallel computing platform
+- **PyTorch** - Deep learning framework with CUDA support
+- **Triton** - Open-source GPU programming language
+- **cuBLAS & cuDNN** - GPU-accelerated libraries
+
+## 📚 Curriculum Roadmap
+
+### Phase 1: Foundations
+#### 1. Deep Learning Ecosystem Deep Dive
+- Modern GPU Architecture Overview
+- Memory Hierarchy & Data Flow
+- CUDA in the ML Stack
+- Hardware Accelerator Landscape (GPU vs TPU vs DPU)
+
+#### 2. Development Environment Setup
+- 🐧 Linux Environment Configuration
+- 🐋 Docker Containerization
+- 🔧 CUDA Toolkit Installation
+- 📊 Monitoring & Profiling Tools
+
+#### 3. Programming Language Mastery
+- C/C++ Advanced Concepts
+- Python High-Performance Computing
+- Mojo Language Introduction
+- R for GPU Computing
+
+### Phase 2: Core CUDA Concepts
+#### 4. GPU Architecture & Computing
+- SM Architecture Deep Dive
+- Memory Coalescing
+- Warp Execution Model
+- Shared Memory & L1/L2 Cache
+
+#### 5. CUDA Kernel Development
+- Thread Hierarchy
+- Memory Management
+- Synchronization Primitives
+- Error Handling & Debugging
+
+#### 6. Advanced CUDA APIs
+- cuBLAS Optimization
+- cuDNN for Deep Learning
+- Thrust Library
+- NCCL for Multi-GPU
+
+### Phase 3: Optimization & Performance
+#### 7. Matrix Operations Optimization
+- Tiled Matrix Multiplication
+- Memory Access Patterns
+- Bank Conflicts Resolution
+- Warp-Level Primitives
+
+#### 8. Modern GPU Programming
+- Triton Programming Model
+- Automatic Kernel Tuning
+- Memory Access Optimization
+- Performance Comparison with CUDA
+
+#### 9. PyTorch CUDA Extensions
+- Custom CUDA Kernels
+- C++/CUDA Extension Development
+- JIT Compilation
+- Performance Profiling
+
+### Phase 4: Applied Projects
+#### 10. Capstone Project
+- MNIST MLP Implementation
+- Custom CUDA Kernels
+- Performance Optimization
+- Multi-GPU Scaling
+
+#### 11. Advanced Topics
+- Ray Tracing
 - Fluid Simulation
-- Video Editing
-- Crypto Mining
-- 3D modeling
-- Anything that requires parallel processing with large arrays
-
-## Resources
-
-- GitHub repo (this repository)
-- Stack Overflow
-- NVIDIA Developer Forums
-- NVIDIA and PyTorch documentation
-- LLMs for navigating the space
-
-## Other Learning Material
-
-- https://github.com/CoffeeBeforeArch/cuda_programming
-- https://www.youtube.com/@CUDAMODE
-- https://discord.com/invite/cudamode
-
-## Fun YouTube Videos:
-- [How do GPUs works? Exploring GPU Architecture](https://www.youtube.com/watch?v=h9Z4oGN89MU)
-- [But how do GPUs actually work?](https://www.youtube.com/watch?v=58jtf24uijw&ab_channel=Graphicode)
-- [Getting Started With CUDA for Python Programmers](https://www.youtube.com/watch?v=nOxKexn3iBo&ab_channel=JeremyHoward)
-- [Transformers Explained From The Atom Up](https://www.youtube.com/watch?v=7lJZHbg0EQ4&ab_channel=JacobRintamaki)
-- [How CUDA Programming Works - Stephen Jones, CUDA Architect, NVIDIA](https://www.youtube.com/watch?v=QQceTDjA4f4&ab_channel=ChristopherHollinworth)
-- [Parallel Computing with Nvidia CUDA - NeuralNine](https://www.youtube.com/watch?v=zSCdTOKrnII&ab_channel=NeuralNine)
-- [CPU vs GPU vs TPU vs DPU vs QPU](https://www.youtube.com/watch?v=r5NQecwZs1A&ab_channel=Fireship)
-- [Nvidia CUDA in 100 Seconds](https://www.youtube.com/watch?v=pPStdjuYzSI&ab_channel=Fireship)
-- [How AI Discovered a Faster Matrix Multiplication Algorithm](https://www.youtube.com/watch?v=fDAPJ7rvcUw&t=1s&ab_channel=QuantaMagazine)
-- [The fastest matrix multiplication algorithm](https://www.youtube.com/watch?v=sZxjuT1kUd0&ab_channel=Dr.TreforBazett)
-- [From Scratch: Cache Tiled Matrix Multiplication in CUDA](https://www.youtube.com/watch?v=ga2ML1uGr5o&ab_channel=CoffeeBeforeArch)
-- [From Scratch: Matrix Multiplication in CUDA](https://www.youtube.com/watch?v=DpEgZe2bbU0&ab_channel=CoffeeBeforeArch)
-- [Intro to GPU Programming](https://www.youtube.com/watch?v=G-EimI4q-TQ&ab_channel=TomNurkkala)
-- [CUDA Programming](https://www.youtube.com/watch?v=xwbD6fL5qC8&ab_channel=TomNurkkala)
-- [Intro to CUDA (part 1): High Level Concepts](https://www.youtube.com/watch?v=4APkMJdiudU&ab_channel=JoshHolloway)
-- [Intro to GPU Hardware](https://www.youtube.com/watch?v=kUqkOAU84bA&ab_channel=TomNurkkala)
+- Cryptographic Applications
+- Scientific Computing
+
+## 🎓 Learning Outcomes
+By the end of this course, you will be able to:
+- Design and implement efficient CUDA kernels
+- Optimize GPU memory usage and access patterns
+- Develop custom PyTorch extensions
+- Profile and debug GPU applications
+- Deploy multi-GPU solutions
+
+## 🔍 Prerequisites
+### Required:
+- Strong Python programming skills
+- Basic understanding of C/C++
+- Computer architecture fundamentals
+
+### Recommended:
+- Linear algebra basics
+- Calculus (for backpropagation)
+- Basic ML/DL concepts
+
+## 💻 Hardware Requirements
+### Minimum:
+- NVIDIA GTX 1660 or better
+- 16GB RAM
+- 50GB free storage
+
+### Recommended:
+- NVIDIA RTX 3070 or better
+- 32GB RAM
+- 100GB SSD storage
+
+## 📚 Learning Resources
+
+### Official Documentation
+- [NVIDIA CUDA Documentation](https://docs.nvidia.com/cuda/)
+- [PyTorch CUDA Documentation](https://pytorch.org/docs/stable/cuda.html)
+- [Triton Documentation](https://triton-lang.org/)
+
+### Community Resources
+- 💬 NVIDIA Developer Forums
+- 🤝 Stack Overflow CUDA tag
+- 🎮 Discord: CUDAMODE community
+
+### Video Learning
+#### Fundamentals
+- 🎥 [GPU Architecture Deep Dive](https://www.youtube.com/watch?v=h9Z4oGN89MU)
+- 🎥 [CUDA Programming Essentials](https://www.youtube.com/watch?v=QQceTDjA4f4)
+
+#### Advanced Topics
+- 🎥 [Matrix Multiplication Optimization](https://www.youtube.com/watch?v=DpEgZe2bbU0)
+- 🎥 [Multi-GPU Programming](https://www.youtube.com/watch?v=4APkMJdiudU)
+
+## 🌟 Course Philosophy
+We believe in:
+- Hands-on learning through practical projects
+- Understanding fundamentals before optimization
+- Building real-world applicable skills
+- Community-driven knowledge sharing
+
+## 📈 Industry Applications
+- 🤖 Deep Learning & AI
+- 🎮 Graphics & Gaming
+- 🌊 Scientific Simulation
+- 📊 Data Analytics
+- 🔐 Cryptography
+- 🎬 Media Processing