Types of Neural Networks

Types of Neural Networks you should know as a Data Scientist.

A neural network is a subset of machine learning that mimics the workings of a human brain while solving a complex problem based on deep learning. Neural networks are inspired by neurons found in the human brain. In this article, I’m going to introduce you to the types of neural networks you need to know as a Data Scientist.

Types of Neural Networks

Neural networks are classified according to their architectures. There are 7 types of neural networks that you need to know:

  1. Perceptron
  2. Artificial neural networks
  3. Multilayer Perceptron
  4. Radial networks
  5. Convolutional neural networks
  6. Recurrent neural networks
  7. Long-Short-Term Memory

So these are the types of neural networks you should know as a Data Scientist. Now let’s go through all of these types of neural networks one by one.

Perceptron is the most basic architecture of neural networks. It is also known as a single layer neural network because it contains only one input layer and one output layer. There are no hidden layers present in the perceptron. It works by taking inputs, then it calculates the weighted input of each node, then it uses an activation function. Perceptrons are a basic form of neural networks, so this type of architecture is only preferred for classification-based problems.

An artificial neural network is also known as a fast forward neural network. In this type of neural network, all perceptrons are layered such that the input layers take input and the output layers generate output. In an artificial neural network, all nodes are fully connected, which means that every perceptron in one layer is connected to every node in the next layer. These types of neural networks are the best to use in computer vision applications.

Artificial Neural Networks has a shortcoming to learn with backpropagation, this is where multilayer perceptrons come in. Multilayer perceptrons are the types of neural networks which are bidirectional by which I mean that they forward propagation of the inputs and the backward propagation of the weights. Here all the neurons in a layer are connected to all the neurons in the next layer.

Multilayer perceptrons can be used in any type of deep learning application but they are slow due to their architecture, this is where radial networks come in. Radial networks are different from all types of neural networks because of their faster learning rate. The difference between a radial basis network and an artificial neural network is that radial basis networks use a radial basis function as an activation function. This architecture is best to use when the problem is based on classification.

Convolutional neural networks are one of the best types of neural networks that can be used in any computer vision task, especially in image classification. You can use a CNN for most computer vision problems because it contains multiple layers of neurons that are used to understand the most important features of an image. In a convolutional neural network, the first layers of neurons are used to understand lower level features and the remaining layers of neurons are used to understand high-level features.

Recurrent neural networks are types of artificial neural networks where each neuron present inside the hidden layer receives an input with a specific delay. When we need to access the previous set of information in current iterations, it is best to use recurrent neural networks. It can be used in very complex deep learning applications such as machine translation systems and robot control applications.

Long Short Term Memory or LSTM networks are used in deep learning applications where data is processed with memory gaps. The best part about LSTMs is that they can remember data for longer. So, whenever your neural network fails to remember the data, you can use LSTM networks. One of the applications where it is widely used is the prediction of time series. So we can say that when you want to use deep learning for regression-based problems, you can use LSTM networks.

Summary

A neural network is a subset of machine learning that mimics the workings of a human brain while solving a complex problem based on deep learning. There are 7 major types of neural networks that you should know:

  1. Perceptron
  2. Artificial neural networks
  3. Multilayer Perceptron
  4. Radial networks
  5. Convolutional neural networks
  6. Recurrent neural networks
  7. Long-term short-term memory

I hope you liked this article on the types of neural networks that you should know as a Data Scientist. Feel free to ask your valuable questions in the comments section below.

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