Types of Neural Networks

There are seven types of neural networks that can be used.

  • Feedforward Neteworks: A feedforward neural network is a simple artificial neural network architecture in which data moves from input to output in a single direction. It has input, hidden, and output layers; feedback loops are absent. Its straightforward architecture makes it appropriate for a number of applications, such as regression and pattern recognition.
  • Multilayer Perceptron (MLP): MLP is a type of feedforward neural network with three or more layers, including an input layer, one or more hidden layers, and an output layer. It uses nonlinear activation functions.
  • Convolutional Neural Network (CNN): A Convolutional Neural Network (CNN) is a specialized artificial neural network designed for image processing. It employs convolutional layers to automatically learn hierarchical features from input images, enabling effective image recognition and classification. CNNs have revolutionized computer vision and are pivotal in tasks like object detection and image analysis.
  • Recurrent Neural Network (RNN): An artificial neural network type intended for sequential data processing is called a Recurrent Neural Network (RNN). It is appropriate for applications where contextual dependencies are critical, such as time series prediction and natural language processing, since it makes use of feedback loops, which enable information to survive within the network.
  • Long Short-Term Memory (LSTM): LSTM is a type of RNN that is designed to overcome the vanishing gradient problem in training RNNs. It uses memory cells and gates to selectively read, write, and erase information.

What is a neural network?

Neural Networks are computational models that mimic the complex functions of the human brain. The neural networks consist of interconnected nodes or neurons that process and learn from data, enabling tasks such as pattern recognition and decision making in machine learning. The article explores more about neural networks, their working, architecture and more.

Table of Content

  • Evolution of Neural Networks
  • What are Neural Networks?
  • How does Neural Networks work?
  • Learning of a Neural Network
  • Types of Neural Networks
  • Simple Implementation of a Neural Network

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Evolution of Neural Networks

Since the 1940s, there have been a number of noteworthy advancements in the field of neural networks:...

What are Neural Networks?

Neural networks extract identifying features from data, lacking pre-programmed understanding. Network components include neurons, connections, weights, biases, propagation functions, and a learning rule. Neurons receive inputs, governed by thresholds and activation functions. Connections involve weights and biases regulating information transfer. Learning, adjusting weights and biases, occurs in three stages: input computation, output generation, and iterative refinement enhancing the network’s proficiency in diverse tasks....

How does Neural Networks work?

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Learning of a Neural Network

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Types of Neural Networks

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Simple Implementation of a Neural Network

Python3 import numpy as np # array of any amount of numbers. n = mX = np.array([[1, 2, 3],              [3, 4, 1],              [2, 5, 3]]) # multiplicationy = np.array([[.5, .3, .2]]) # transpose of yy = y.T # sigma valuesigm = 2 # find the deltadelt = np.random.random((3, 3)) - 1 for j in range(100):       # find matrix 1. 100 layers.    m1 = (y - (1/(1 + np.exp(-(np.dot((1/(1 + np.exp(        -(np.dot(X, sigm))))), delt))))))*((1/(            1 + np.exp(-(np.dot((1/(1 + np.exp(                -(np.dot(X, sigm))))), delt)))))*(1-(1/(                    1 + np.exp(-(np.dot((1/(1 + np.exp(                        -(np.dot(X, sigm))))), delt)))))))     # find matrix 2    m2 = m1.dot(delt.T) * ((1/(1 + np.exp(-(np.dot(X, sigm)))))                           * (1-(1/(1 + np.exp(-(np.dot(X, sigm)))))))    # find delta    delt = delt + (1/(1 + np.exp(-(np.dot(X, sigm))))).T.dot(m1)     # find sigma    sigm = sigm + (X.T.dot(m2)) # print output from the matrixprint(1/(1 + np.exp(-(np.dot(X, sigm)))))...

Advantages of Neural Networks

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Disadvantages of Neural Networks

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Frequently Asked Questions (FAQs)

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