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Sefik Ilkin Serengil

Code wins arguments

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Tag: neural networks

Incorporating Momentum Into Neural Networks Learning

Newton’s cradle is the most popular example of momentum conservation. A lifted and released sphere strikes the stationary spheres and … More

gradient descent, momentum, neural networks

Building Neural Networks with Weka In Java

Building neural networks models and implementing learning consist of lots of math this might be boring. Herein, some tools help researchers to … More

classification, Java, neural networks, regression, weka

Hyperbolic Tangent as Neural Network Activation Function

In neural networks, as an alternative to sigmoid function, hyperbolic tangent function could be used as activation function. When you … More

activation function, neural networks

Backpropagation Implementation: Neural Networks Learning From Theory To Action

We’ve focused on the math behind neural networks learning and proof of the backpropagation algorithm. Let’s face it, mathematical background … More

backpropagation, Java, neural networks, sinus wave

The Math Behind Neural Networks Learning with Backpropagation

Neural networks are one of the most powerful machine learning algorithm. However, its background might confuse brains because of complex … More

backpropagation, neural networks, sigmoid

Sigmoid Function as Neural Network Activation Function

Sigmoid function (aka logistic function) is moslty picked up as activation function in neural networks. Because its derivative is easy to demonstrate. … More

activation function, neural networks

Introduction to Neural Networks: A Mechanism Taking Lessons From The Past

Neural Networks inspired from human central nervous system. They are based on making mistakes and learning lessons from past errors. They … More

classification, lion king, neural networks, regression

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