Welcome to the lesson on Neural Network Basics! In this section, we will delve into the fundamentals of neural networks, which are at the heart of deep learning.
What is a Neural Network?
A neural network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
Components of a Neural Network
- Neurons: The basic building blocks of a neural network.
- Weights: These are the parameters that determine the strength of the connections between neurons.
- Bias: It is a value added to the weighted sum of inputs.
- Activation Function: Determines whether a neuron should be activated or not.
Types of Neural Networks
- Feedforward Neural Networks: The simplest type of neural network.
- Convolutional Neural Networks (CNNs): Used primarily for image recognition.
- Recurrent Neural Networks (RNNs): Ideal for sequence data like time series or natural language.
Applications of Neural Networks
- Image Recognition
- Natural Language Processing
- Speech Recognition
- Autonomous Vehicles
Further Reading
To dive deeper into neural networks, you might want to explore Neural Network Advanced Topics.
Learning Resources
Visualizing a Neural Network
To understand the structure of a neural network, consider the following image:
Stay tuned for more lessons on deep learning and neural networks!