Hello, TensorFlow!

I read a book one day and my whole life was changed. That’s the opening sentence of Orhan Pamuk‘s The … More

Becoming the MacGyver of Machine Learning with Neural Networks

You would most probably remember MacGyver if you are a member of generation Y. He is famous for creating materials … More

Homer Simpson Guide to Backpropagation

  Backpropagation algorithm is based on complex mathematical calculations. That’s why, it is hard to understand and that is the … More

Step Function as a Neural Network Activation Function

Activation functions are decision making units of neural networks. They calculates net output of a neural node. Herein, heaviside step … More

Homer Sometimes Nods: Error Metrics in Machine Learning

Even the worthy Homer sometimes nods. The idiom means even the most gifted person occasionally makes mistakes. We would adapt this … More

AI: a one-day wonder or an everlasting challenge

Debates between humans and computers start with mechanical turk. That’s an historical autonomous chess player costructed in 18th century. However, … More

Adaptive Learning in Neural Networks

Gradient descent is one of the most powerful optimizing method. However, learning time is a challange, too. Standard version of gradient … More

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

Even Superheroes Need to Rest: Working on Trained Neural Networks in Weka

Applying neural networks could be divided into two phases as learning and forecasting. Learning phase has high cost whereas forecasting … More

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