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I have just gotten myself into Artificial Neural Networks and I have covered the Multi-Layer Perceptron, feed-forward and Back-propagation algorithm. Now that I want to see it working on a real project.

I was thinking to implement them on very simple project so that I can get to understand these things better. I want to work on either of the following topic:

  1. Image classification - if I load an image, the system must tell me whether it's a cat or a dog.

The challenge I have is I don't know where exactly to get myself started.I have read this example, it shaded light but the example was not in line with what I want to do. I am kindly asking for a step by step guide to work out this project.

PS: If you suggest better beginner projects, thanks. Also, I would want to code my project in Java - most preferably, but any other language is fine.

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The CS231n online Stanford course has some great assignments about training neural nets on images. They are written in Python and not Java though unfortunately. They include a good tutorial on Python for the course though.

Assignment 2, Q1 specifically is about training the CIFAR data set using a two layer neural network. Each question of the assignment comes with its own ipython notebook that take you through the whole process, step by step. It's pretty ideal for beginners if you know the theoretical background already. And if not the notes themselves are very comprehensive.

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  • $\begingroup$ Well, those are nice links! I see, its like I have to install a Python compiler to work through this great course, right? $\endgroup$
    – CN1002
    Commented Oct 8, 2015 at 8:34
  • $\begingroup$ Anaconda has all the dependencies you should need. continuum.io/downloads . Once you've downloaded that just change to the assignment2 directory then run the ipython notebook command in terminal and you're away. $\endgroup$
    – Henry E
    Commented Oct 8, 2015 at 8:36
  • $\begingroup$ It is working now, i have direction now :)- you guys you know your stuff $\endgroup$
    – CN1002
    Commented Oct 8, 2015 at 12:38

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