Tag: machine learning

After playing with lane lines detection and traffic signs recognition, it's time to feed a Deep Neural Network with a video recorded during human driving. The model will be trained afterwards to act "humanlike" and predict correct steering angles while driving autonomously. Such procedure is also called Behavioral Cloning. For simplicity (and public safety 馃檪 ) I will collect video data and test the resulting DNN in a game-like simulator developed by Udacity. This project stands as a third assignment in "Self-Driving Car Engineer" course.
More Teaching a Deep Neural Network to steer a car - Behavioral Cloning

Self-Driving Cars

This is the last post in series about Support Vector Machine classifier. We already feel the basics of SVM. We have our data preprocessed. Finally, we know the influence of some major hyperparameters on the classifier. Now, let's choose proper hyperparameters for a given problem. This is done by validation or cross-validation. These techniques are very common in Machine Learning and are also helpful in finding a proper SVM model. The example will cover building the classifier for the foreground/background estimation problem in Flover project.
More SVM model selection - how to adjust all these knobs pt. 2

Flover Project

This time we are going to discuss the influence of two basic variables on the quality of SVM classifier. They are called hyperparameters to distinguish them from the parameters optimized in a machine learning procedures. Two previous posts introduced Support Vector Machine itself and data preprocessing for this classifier. As in other Machine Learning techniques there is also a need to properly adjust some system variables to find the best model for our needs. Here, we will focus on description of complexity parameter and gamma parameter from the Gaussian kernel. In the next article we will find an optimum SVM model for the foreground/background estimation problem in Flover project using model validation techniques.
More SVM model selection - how to adjust all these knobs pt. 1

Flover Project

Data preprocessing is a very important and quite underestimated step in Machine Learning pipelines. It provides cleaned and relevant datasets which then can be used in further steps like classification or regression. I will describe a study case for data which is fed to the SVM classifier to predict if a given image segment belongs to foreground or background. This is a second article about Support Vector Machine which is used for image segmentation in my flower species recognition project Flover.

More Data preprocessing for SVM classifier

Flover Project

Ze wzgl臋du na to, 偶e przy operacji oddzielania t艂a od obrazu w projekcie Flover u偶ywam algorytmu Support Vector Machine (SVM), postaram si臋 go dzisiaj przyst臋pnie opisa膰. To bardzo popularna metoda znana ze swojej skuteczno艣ci w dziedzinie Machine Learning. Ma za zadanie jak najlepiej odseparowa膰 od siebie elementy r贸偶nego typu ze zbioru ucz膮cego, czyli umie艣ci膰 je w optymalnie oddzielonych grupach.
More Support Vector Machine

Flover Project

W rozpoznawaniu obraz贸w cz臋sto mamy do czynienia聽segmentacj膮. Przeprowadzamy j膮 gdy chcemy聽zmieni膰 reprezentacj臋 obrazu na 艂atwiejsz膮 do analizy - zamiast obrazu w postaci pikseli otrzymujemy regiony, kt贸re s膮 w jaki艣 spos贸b jednorodne, np. pod wzgl臋dem koloru, odcieni szaro艣ci czy tekstury. Te wydzielone obszary nazywamy r贸wnie偶 superpikselami. Jest ich zazwyczaj o wiele mniej od normalnych pikseli,聽dlatego algorytmy przetwarzaj膮ce dalej dany obraz maj膮 bardziej og贸lne informacje o obrazie i s膮 szybsze. Jednym z lepszych a zarazem 艂atwych algorytm贸w segmentacji jest metoda SLIC.

More SLIC-zne superpiksele

Flover Project