Neural Networks

Neural networks are computational models inspired by biological neural networks, forming the foundation of deep learning. This guide covers essential concepts and techniques in neural network architecture and training.

Neural Network Basics

  • Architecture Components
    • Neurons (nodes)
    • Layers (input, hidden, output)
    • Weights and biases
    • Forward propagation
  • Types of Neural Networks
    • Feedforward Neural Networks (FNN)
    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN)
    • Transformers

Activation Functions, Gradients, and Backpropagation

  • Common Activation Functions
    • ReLU (Rectified Linear Unit)
    • Sigmoid
    • Tanh
    • Leaky ReLU
    • Softmax
  • Backpropagation
    • Chain rule
    • Gradient computation
    • Error propagation
    • Weight updates

Loss Functions and Regularization Strategies

  • Loss Functions
    • Mean Squared Error (MSE)
    • Cross-Entropy Loss
    • Binary Cross-Entropy
    • Hinge Loss
  • Regularization Techniques
    • L1/L2 Regularization
    • Dropout
    • Batch Normalization
    • Early Stopping

Optimization Algorithms and Hyperparameter Tuning

  • Optimization Algorithms
    • Stochastic Gradient Descent (SGD)
    • Adam
    • RMSprop
    • AdaGrad
  • Hyperparameter Optimization
    • Learning rate
    • Batch size
    • Number of layers/neurons
    • Cross-validation
    • Grid/Random search

Best Practices and Common Challenges

  • Training Best Practices
    • Data preprocessing
    • Weight initialization
    • Learning rate scheduling
    • Model evaluation metrics
  • Common Challenges
    • Vanishing/exploding gradients
    • Overfitting/underfitting
    • Local minima
    • Training stability

Resources and Further Reading