Neural Network Architectures

Understanding different neural network architectures and their applications

Neural Network Architectures

This section covers various neural network architectures, their components, and applications in deep learning.

Feedforward Neural Networks (FNN)

Architecture Components

  • Input layer
  • Hidden layers
  • Output layer
  • Activation functions
  • Weight matrices

Design Considerations

  • Layer width
  • Network depth
  • Skip connections
  • Initialization strategies

Convolutional Neural Networks (CNN)

Core Components

  • Convolutional layers
  • Pooling layers
  • Fully connected layers
  • Feature maps
  • LeNet
  • AlexNet
  • VGG
  • ResNet
  • Inception/GoogleNet

Advanced Concepts

  • Dilated convolutions
  • Depthwise separable convolutions
  • Attention mechanisms
  • Skip connections

Recurrent Neural Networks (RNN)

Basic RNN

  • Sequential processing
  • Hidden state
  • Backpropagation through time
  • Vanishing/exploding gradients

Advanced RNN Variants

  • LSTM
  • GRU
  • Bidirectional RNN
  • Deep RNNs

Applications

  • Sequence modeling
  • Time series analysis
  • Natural language processing
  • Speech recognition

Transformer Architecture

Core Components

  • Self-attention mechanism
  • Multi-head attention
  • Position encodings
  • Feed-forward networks

Architecture Variants

  • Encoder-only (BERT)
  • Decoder-only (GPT)
  • Encoder-decoder (T5)
  • Efficient transformers

Autoencoders

Basic Structure

  • Encoder
  • Decoder
  • Bottleneck layer
  • Reconstruction loss

Types

  • Vanilla autoencoders
  • Denoising autoencoders
  • Variational autoencoders (VAE)
  • Sparse autoencoders

Graph Neural Networks (GNN)

Core Concepts

  • Node embeddings
  • Edge features
  • Message passing
  • Graph pooling

Common Architectures

  • Graph Convolutional Networks
  • Graph Attention Networks
  • GraphSAGE
  • Message Passing Neural Networks

Hybrid Architectures

CNN-RNN Combinations

  • Image captioning
  • Video analysis
  • Action recognition
  • Visual question answering

Transformer-CNN Hybrids

  • Vision Transformers (ViT)
  • Swin Transformer
  • ConvNeXT
  • Hybrid models

Best Practices

  1. Architecture selection
  2. Layer configuration
  3. Hyperparameter tuning
  4. Training strategies
  5. Model optimization

Implementation Considerations

  • Computational efficiency
  • Memory requirements
  • Training stability
  • Inference speed
  • Model complexity

Tools and Frameworks

  • PyTorch
  • TensorFlow
  • JAX
  • MXNet
  • Keras
  • Model Training
  • Optimization Methods
  • Regularization Techniques
  • Transfer Learning