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ahmet taspinar
  • Eindhoven
  • Netherlands
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Job Title:
Software Engineer
Job Function:
Business Analytics, Predictive Modeling, Data Mining, Web Analytics
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Ahmet taspinar's Blog

Machine Learning with Signal Processing Techniques

Posted on April 29, 2018 at 9:00am 0 Comments

Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals.

Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals.

Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with…


Building Convolutional Neural Networks with Tensorflow

Posted on September 7, 2017 at 7:30am 0 Comments

In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications.

The pictures here are from the full article. Source code is also provided.…


The Perceptron Algorithm explained with Python code

Posted on December 22, 2016 at 10:30am 0 Comments

1. Introduction

Most tasks in Machine Learning can be reduced to classification tasks. For example, we have a medical dataset and we want to classify who has diabetes (positive class) and who doesn’t (negative class). We have a dataset from the financial world and want to know which customers will default on their credit (positive class) and which customers will not (negative class).

To do this, we can train a Classifier with a ‘training dataset’ and after such a Classifier is…


Regression, Logistic Regression and Maximum Entropy

Posted on March 29, 2016 at 8:00am 0 Comments

One of the most important tasks in Machine Learning are the Classification tasks (a.k.a. supervised machine learning). Classification is used to make an accurate prediction of the class of entries in the test set (a dataset of which the entries have not been labelled yet) with the model which was constructed from a training set. You could think of classifying crime in the field of Pre-Policing, classifying patients in the Health sector, classifying houses in the Real-Estate sector.…


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