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Private work in machine learning

Max Schneider > Blog > Private work in machine learning
Private work in machine learning
10 Aug

Private work in machine learning

Max Schneider Aug 10, 2015 10 1258

First steps

in 2010 I started to get interested in machine learning and artificial intelligence. This led me to start reading some books about neural networks which was the topic which fascinated me most. My first book was from the university library. I do not remember the name anymore, but it was a practical guide and was easy to read. After a few weeks reading was not enough anymore and I started to code my first neural network in VisualBasic. It could was a perceptron with one hidden layer. I successfully trained it on recognizing the letters A-Z, which where drawn on the input layer.

NeuralNet 2010 first steps in machine learning


Doing OCR with Convolutional Neural Networks

Parallel to my internship at EADS where I worked on vehicle detection with neural networks my interest in convolutional neural networs as proposed by  LeCun was rising. Together with a colleague we started developing an OCR app based on CNN for windows phones.

Unfortunatelly the development stopped after the internship ended for both of us and he went back to czech republic.

Here is a screenshot of the ongoing learning process of the cnn:


Attending the online course „Introduction to artificial intelligence“ from Prof. Thrun at the Standord University

Since I was still fascinated by machine learning and AI I took part in the online course of Sebastian Thrun. It was a one semester course about Bayes nets, Probability, reinforcement learning and basics of machine learning.


Later projects

After these first steps I worked on following topics:

  • Image classification with Random Forests
  • Face detection with CNN
  • create neural networks inspired by the Hierarchical Temporal Memory Architecture introduced by Jeff Hawkins and Numenta (Details here)
  • Handwriting synthesis with recurrent neural networks

detailed desciptions will follow…


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