Welcome to the tutorial on neural network architectures. In this guide, we will explore various types of neural network architectures and their applications. Whether you are a beginner or an experienced AI practitioner, this tutorial will provide you with valuable insights into the world of neural networks.
Types of Neural Network Architectures
1. Feedforward Neural Networks
Feedforward neural networks are the simplest form of neural networks. They consist of an input layer, one or more hidden layers, and an output layer. Data flows in only one direction, from the input layer to the output layer.
2. Convolutional Neural Networks (CNNs)
CNNs are primarily used for image recognition and processing. They automatically and adaptively learn spatial hierarchies of features from input images.
3. Recurrent Neural Networks (RNNs)
RNNs are designed to work with sequence data, such as time series or natural language. They have loops in their architecture, allowing information to persist across time steps.
4. Autoencoders
Autoencoders are neural networks that learn to compress and then reconstruct their input data. They are often used for unsupervised learning tasks, such as anomaly detection or feature extraction.
Further Reading
For more information on neural network architectures, check out our comprehensive guide on Neural Network Basics.
Happy learning! 🎓