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:
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…
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…Continue
Added 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 …Continue
Added 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…Continue
Added 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
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. …Continue