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This article was written by James Le.

Some examples of tasks best solved by machine learning include:

  • Recognizing patterns: Objects in real scenes, Facial identities or facial expressions, Spoken words.
  • Recognizing anomalies: Unusual sequences of credit card transactions, Unusual patterns of sensor readings in a nuclear power plant.
  • Prediction: Future stock prices or currency exchange rates, Which movies will a person like.

Application of Recurrent Neural Network

Neural Networks are a class of models within the general machine learning literature. Here are the 3 reasons  to study neural computation:

  • To understand how the brain actually works: It’s very big and very complicated and made of stuff that dies when you poke it around. So we need to use computer simulations.
  • To understand a style of parallel computation inspired by neurons and their adaptive connections: It’s a very different style from sequential computation.
  • To solve practical problems by using novel learning algorithms inspired by the brain: Learning algorithms can be very useful even if they are not how the brain actually works.

After finishing the famous Andrew Ng’s Machine Learning Coursera course, I started developing interest towards neural networks and deep learning. Thus, I started looking at the best online resources to learn about the topics and found Geoffrey Hinton’s Neural Networks for Machine Learning course. In this blog post, I want to share the 10 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.

10 Neural Network Architectures

  • Perceptrons
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Long / Short Term Memory
  • Gated Recurrent Unit
  • Hopfield Network
  • Boltzmann Machine
  • Deep Belief Networks
  • Autoencoders
  • Generative Adversarial Network

To read the whole article, with illustrations, click here. For more on neural networks, follow this link

 

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