In a previous blog-post we have seen how we can use Signal Processing techniques for the classification of time-series and signals.

A very short summary of that post is: We can use the Fourier Transform to transform a signal from its time-domain to its frequency domain. The peaks in the frequency spectrum indicate the most…

ContinueAdded by Ahmet Taspinar on December 20, 2018 at 9:30pm — No Comments

Recurrent Neural Nets (RNN) detect features in sequential data (e.g. time-series data). Examples of applications which can be made using RNN’s are anomaly detection in time-series data, classification of ECG and …

ContinueAdded by Ahmet Taspinar on July 5, 2018 at 11:48am — No 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…

ContinueAdded by Ahmet Taspinar on April 12, 2018 at 6:00am — No Comments

In a previous blog post we have seen how to build Convolutional Neural Networks (CNN) in Tensorflow, by building various CNN architectures (like LeNet5, AlexNet, VGGNet-16) from scratch and training them on the MNIST, CIFAR-10 and Oxflower17 datasets.

It starts to get interesting when you start thinking about the practical applications of CNN and other Deep Learning methods. If you have been following the latest technical developments you probably know that CNN’s are…

ContinueAdded by Ahmet Taspinar on December 4, 2017 at 5:00am — No 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 contents of this blog-post is as follows:

- Tensorflow basics:
- 1.1 Constants and Variables
- 1.2 Tensorflow Graphs and Sessions
- 1.3 Placeholders and…

Added by Ahmet Taspinar on August 15, 2017 at 4:00am — No Comments

For python programmers, scikit-learn is one of the best libraries to build Machine Learning applications with. It is ideal for beginners because it has a really simple interface, it is well documented with many examples and tutorials.

Besides supervised machine learning (classification and regression), it can also be used for clustering, dimensionality reduction, feature extraction and engineering, and pre-processing the data. The interface is consistent over all of these methods, so…

Added by Ahmet Taspinar on May 26, 2017 at 4:30am — 1 Comment

Machine Learning is a vast area of Computer Science that is concerned with designing algorithms which form good models of the world around us (the data coming from the world around us).

Within Machine Learning many tasks are - or can be reformulated as - classification tasks.

In classification tasks we are trying to produce a model which can give the correlation…

ContinueAdded by Ahmet Taspinar on December 15, 2016 at 2:00pm — No Comments

Logistic Regression is one of the most powerful classification methods within machine learning and can be used for a wide variety of tasks. Think of pre-policing or predictive analytics in health; it can be used to aid …

ContinueAdded by Ahmet Taspinar on May 7, 2016 at 9:30am — No Comments

Reading the academic literature Text Analytics seems difficult. However, applying it in practice has shown us that Text Classification is much easier than it looks. Most of the Classifiers consist of only a few lines of code.In this three-part blog series we will examine the three well-known Classifiers; the Naive Bayes, Maximum Entropy and Support Vector Machines. From the…

ContinueAdded by Ahmet Taspinar on February 15, 2016 at 10:00pm — No Comments

As a precursor to research about Sentiment Analysis with Text Classifiers (Naive Bayes, Maximum Entropy, SVM), Sentiment Analysis with bag-of-words was done and Positive / Negative Sentiment was detected with an accuracy of 60%. This is when only unigrams are used. This percentage will be much when bigrams or trigrams are used (in a next blog-post). See the results at:

part 1: http://tinyurl.com/gnlfqqm

part 2:…

Added by Ahmet Taspinar on February 1, 2016 at 1:30pm — 1 Comment

We all know that with Machine Learning you can automatically classify text documents or analyze its subjectivity. We've just released a guide that gives a brief introduction to Text Classification.

It cover the three most used classifiers; Naive Bayes, Maximum Entropy and Support Vector Machines and will give practical examples in the form of the sentiment analysis of book reviews. …

ContinueAdded by Ahmet Taspinar on January 11, 2016 at 6:30am — 1 Comment

- A guide for using the Wavelet Transform in Machine Learning
- Building Recurrent Neural Networks in Tensorflow
- Machine Learning with Signal Processing Techniques
- Using Convolutional Neural Networks to detect features in sattelite images
- Building Convolutional Neural Networks with Tensorflow
- Classification with scikit-learn
- Naive Bayes Classification explained with Python code

- Naive Bayes Classification explained with Python code
- The Naive Bayes Classifier explained
- Text Classification & Sentiment Analysis tutorial / blog
- Building Convolutional Neural Networks with Tensorflow
- Logistic Regression and Maximum Entropy explained with examples and code
- A guide for using the Wavelet Transform in Machine Learning
- Sentiment Analysis with the bag-of-words

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- neural (3)
- python (3)
- tensorflow (3)
- Analysis (2)
- Bayes (2)
- Learning (2)
- Machine (2)
- Naive (2)
- Python (2)
- classification (2)
- convolutional (2)
- transform (1)
- wavelet (1)
- Analytics (1)
- FFT (1)
- Fourier (1)
- Sentiment (1)
- Signal (1)
- Text (1)
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- image (1)
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- naive bayes (1)
- recurrent (1)
- sattelite (1)
- scikit-learn (1)
- sentiment analytics (1)
- signal (1)
- text analytics (1)
- time-series (1)

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