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Understanding Mixture Density Neural Networks Training Ppt

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Presenting Understanding Mixture Density Neural Networks. These slides are 100 percent made in PowerPoint and are compatible with all screen types and monitors. They also support Google Slides. Premium Customer Support available. Suitable for use by managers, employees, and organizations. These slides are easily customizable. You can edit the color, text, icon, and font size to suit your requirements.

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Slide 1

This slide gives an introduction to Mixture Density Networks. A Mixture Density Network is an interesting model for working on supervised learning issues where a single standard probability distribution cannot represent the target variable. It is created within the basic framework of neural networks and probability theory.

Instructor’s Notes: In essence, an MDN allows you to model a conditional probability distribution p(y|x) as a mixture of distributions, with the individual distributions and mixing coefficients defined by functions of the inputs x.

Slide 2

This slide depicts the two components that a Mixture Density Network is made of. These are a neural network and a mixture model

Instructor’s Notes: 

  • Neural Network: Any viable architecture that takes the input X and converts it into a set of learned features is a Neural Network (we can think of it as an encoder or backbone)
  • Mixture Model: The Mixture Model is a probability distribution model composed of a weighted sum of simpler distributions

Slide 3

This slide shows how a Mixture Density Network looks like. The Mixture Density Network is a feed-forward neural network that determines the parameters of a mixture density model using its outputs.

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