Neural network refresher
A refresher on neural networks and deep learning concepts
Neural Network Refresher
Welcome to the Neural Network refresher chapter! Here we'll review the fundamental concepts of neural networks and deep learning.
What you'll learn
In this chapter, we'll cover:
- Neural Network basics
- Activation functions
- Forward and backward propagation
- Training neural networks
- Common architectures
Getting Started
Neural networks are the foundation of deep learning. This chapter will refresh your knowledge of neural network concepts and prepare you for more advanced topics.
Neural Network Basics
A neural network is a computational model inspired by the way biological neural networks in the human brain process information. It consists of:
Neurons (Nodes)
- Process and transmit information
- Apply activation functions
Connections (Weights)
- Connect neurons
- Store learned patterns
Layers
- Input layer
- Hidden layers
- Output layer
Activation Functions
Common activation functions include:
- ReLU (Rectified Linear Unit)
- Sigmoid
- Tanh
- Softmax
Forward Propagation
The process of passing input through the network:
- Input processing
- Weight multiplication
- Bias addition
- Activation function application
Backward Propagation
The learning process:
- Error calculation
- Gradient computation
- Weight updates
- Bias adjustments
Training Process
Steps in training a neural network:
- Data preparation
- Network initialization
- Forward propagation
- Error calculation
- Backward propagation
- Parameter updates
- Iteration
Common Architectures
We'll explore these architectures:
- Feedforward Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transformers