Deep Learning with Neural Networks- Part 2

Part 2: Feed-Forward Neural Network (FFNN)

This article, mainly discussing Feed Forward Neural Network(FFNN) which is commonly used in Supervised Learning with neural networks.

(1) In Supervised Learning Model

  • Normally in the Supervised Learning model, there are mainly 4 types Feed Forward Neural Network(FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network(RNN), and Encoder-Decoder Architectures.

(2) What is Feed Forward Neural Network?

  • A Feed Forward Neural network is an artificial neural network in which the connections between nodes do not form a cycle.

(3) How does a Feed-Forward Neural Network work?

  • A Feed Forward Neural Network is commonly seen in its simplest form as a single layer perceptron.

(4) Single Layer perceptron concept

  • The single-layer perceptron is an important model of feed-forward neural networks and is often used in classification tasks

(5) Multi-Layer perceptron concept

  • In multi-layered perceptrons, the process of updating weights is nearly analogous, however, the process is defined more specifically as back-propagation.

(5) Applications of Feed-Forward Neural network

  • While Feed Forward Neural Networks are fairly straightforward, their simplified architecture can be used as an advantage in particular machine learning applications.

e.g: pattern recognition, multivariate regression, robust regression, and handling of instrumental drifts.

This article is mainly about Feed Forward Neural Network(FFNN) which is commonly used in Supervised Learning with neural networks. The next article is mainly focusing on Convolutional Neural networks (CNN)

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