An Introduction To Deep Learning – AI
An Introduction To Deep Learning – AI

Deep learning is a branch of machine learning based on the artificial neural networks which automatically optimize their learned rules/ patterns when there’s a wrong prediction.

Following are some of the Deep learning definitions from the sources.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

https://machinelearningmastery.com/what-is-deep-learning/

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

https://en.wikipedia.org/wiki/Deep_learning

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.

https://www.mathworks.com/discovery/deep-learning.html

The Concept – Deep Learning

  • Deep learning model consists of more than 2 layers of artificial neural networks with a number of nodes
  • Each node represents a weighted number which contributes to the overall calculation/interpretation of the output from the given input
  • Each node is started with a random weighted number which is incremented in each forward pass with a small ratio known as Learning Rate
  • The wights of each node are adjusted if the predicted value of the target class doesn’t match the actual value of the class.
  • The process is repeated until either the defined number of epochs/ iterations are completed or the model fully learn the hidden parameters to predict the target class with maximum success rate

Types of Deep Neural Networks

  • Feed Forward Neural Networks (FFNNs)
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Long Short Term Memory Network (LSTM)
  • Gated Recurrent Neural Network (GRNN)
  • Self Organizing Maps (SOMs)
  • Boltzmann Machines
  • Auto Encoders

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