🤖 MLX8-W3-Transformers

Week 3: Advanced Transformer Architectures & Implementation

Building state-of-the-art transformer models from scratch with modern MLOps practices

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📺 Educational Resources

🎥 Neural Networks Fundamentals - 3Blue1Brown Series

🧠 Neural Networks Fundamentals Playlist by 3Blue1Brown

Click the image above to watch the complete series 📹

📚 What you'll learn from this series:

🎯 Core Concepts Neural network basics Gradient descent intuition Backpropagation explained Mathematical foundations

🔬 Visual Understanding Interactive visualizations Mathematical animations Intuitive explanations Beautiful graphics

🚀 Foundation for Transformers Building blocks of deep learning Optimization principles Network architecture design Mathematical rigor

🎬 Series Breakdown:

Episode	Topic	Duration	Key Concepts
1	But what is a neural network?	19 min	Neurons, layers, MNIST
2	Gradient descent, how neural networks learn	21 min	Cost functions, optimization
3	What is backpropagation really doing?	14 min	Chain rule, derivatives
4	Backpropagation calculus	10 min	Mathematical details

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🎓 Course Content - Advanced Transformers

🎥 Week 3 Main Lecture: From Neural Networks to Transformers

📋 Advanced Transformer Implementation Workshop

📋 What you'll learn in this video:

• 🔧 Transformer Architecture Deep Dive: Understanding attention mechanisms, positional encoding, and layer normalization
• 🚀 Implementation from Scratch: Building transformers with PyTorch, including multi-head attention and feed-forward networks
• 📊 Training Strategies: Advanced techniques for training large transformer models efficiently
• 🎯 Fine-tuning & Transfer Learning: Adapting pre-trained models for specific tasks
• 🛠️ MLOps Integration: Using modern tools like UV for dependency management and reproducible environments
• 📈 Performance Optimization: Memory management, gradient checkpointing, and distributed training

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🚀 Quick Start

Prerequisites

• Python 3.12+ (3.13 for GPU environments)
• CUDA 12.06+ (for GPU training)
• UV Package Manager
• Recommended: Watch the 3Blue1Brown series first! 🎥

🏃‍♂️ Get Running in 60 Seconds

# 1. Clone the repository
git clone https://github.com/your-username/MLX8-W3-Transformers.git
cd MLX8-W3-Transformers

# 2. Install UV (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# 3. Setup environment (auto-detects your platform)
uv sync

# 4. Run your first transformer!
uv run python examples/basic_transformer.py

🖥️ Platform-Specific Setup

🪟 Windows 11 Development

echo "3.12" > .python-version
uv sync --extra dev
uv run python examples/cpu_training.py

🍎 macOS (Intel & Apple Silicon)

echo "3.12" > .python-version  
uv sync --extra dev
uv run python examples/cpu_training.py

🐧 Ubuntu 22.04 + CUDA 12.06

echo "3.13" > .python-version
export UV_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cu121"
uv sync --extra gpu-dev
uv run python examples/gpu_training.py

🐧 Ubuntu 24.04 + CUDA 12.8

echo "3.13" > .python-version
export UV_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cu128"  
uv sync --extra gpu-dev
uv run python examples/gpu_training.py

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