Blog | Exploring the Different Types of Neural Networks

Exploring the Different Types of Neural Networks

NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades.

Neural networks are the foundation of many artificial intelligence and machine learning applications. There are several types of neural networks, each designed to address specific types of problems. In this post, we'll explore the most common types of neural networks and their applications.

Feedforward Neural Networks (FNNs)

Feedforward neural networks, also known as FNNs, are the simplest type of neural network. They consist of an input layer, one or more hidden layers, and an output layer. Information in FNNs flows in one direction, from the input to the output.

 Understanding Feed Forward Neural Networks With Maths and Statistics 

Convolutional Neural Networks (CNNs)

Convolutional neural networks (CNNs) are designed for image processing and computer vision tasks. CNNs use convolutional layers to scan images for local patterns, and pooling layers to reduce spatial dimensions. They excel at image classification and object detection.

A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way

Recurrent Neural Networks (RNNs)

Recurrent neural networks (RNNs) are designed to process sequential data, such as time series or text. RNNs have connections that loop back, allowing them to capture temporal dependencies. Variants such as LSTMs and GRUs address challenges like vanishing gradients.

Understanding RNN and LSTM

Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs) consist of a generator and discriminator network that engage in an adversarial game. The generator creates synthetic data, while the discriminator evaluates its authenticity. GANs have applications in image synthesis and data augmentation.

 Understanding Generative Adversarial Networks (GANs)


Autoencoders are neural networks used for dimensionality reduction and feature extraction. They consist of an encoder that compresses input data and a decoder that reconstructs the original data. Autoencoders are used for image denoising and anomaly detection.

 Applied Deep Learning - Part 3: Autoencoders


Neural networks come in various types, each suited for specific tasks. FNNs are basic networks with one-way information flow. CNNs excel in image processing. RNNs handle sequential data. GANs generate synthetic data through adversarial training. Autoencoders perform data compression and reconstruction.

Further Reading

  1. Neural Networks and Deep Learning - Michael Nielsen
  2. Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville


  • Neural Networks
  • FNNs
  • CNNs
  • RNNs
  • GANs
  • Autoencoders
  • Machine Learning
  • Deep Learning
  • AI
  • Image Processing
  • Sequential Data
  • Generative Models



Post date:

Friday, May 5th, 2023 at 9:13:25 AM