# Deep Learning Practical Guide

This guide covers implementing deep learning models using modern frameworks.

## Neural Network Architectures

### Convolutional Neural Networks (CNNs)
CNNs are designed for processing grid-like data such as images. They use convolutional layers to automatically learn spatial hierarchies of features.

**Key Components:**
- Convolutional layers with filters/kernels
- Pooling layers for downsampling
- Fully connected layers for classification

**Popular Architectures:**
- ResNet: Residual connections for very deep networks
- VGG: Simple but effective architecture
- Inception: Multi-scale feature extraction

### Recurrent Neural Networks (RNNs)
RNNs process sequential data by maintaining hidden state across time steps. Variants include:
- LSTM (Long Short-Term Memory): Handles long-term dependencies
- GRU (Gated Recurrent Unit): Simplified LSTM architecture

### Transformers
Modern architecture using self-attention mechanism. Powers models like BERT, GPT, and T5 for natural language processing tasks.

## Training Best Practices
- Use batch normalization for stable training
- Apply dropout for regularization
- Learning rate scheduling: Reduce on plateau
- Data augmentation to prevent overfitting
- Early stopping based on validation loss

## Frameworks
- **PyTorch**: Dynamic computation graphs, pythonic API
- **TensorFlow**: Production-ready, extensive ecosystem
- **JAX**: High-performance numerical computing with automatic differentiation
