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I would like to use deep leaning for identifying cars; I want the system to predict wether an object is a car or not. How can I do that knowing that im still a beginner in the Deep Learning field ? I am considering visual recognition. The system must recognize the car from anything else on the road.

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  • $\begingroup$ The first thing you should consider is about the recognition -- are you considering visual, audio, olfactory, ..l recognition? And, assuming audio, are you recognizing cars vs. what, everything? Recognizing cars from e.g. trucks is much different than recognizing cars from silence. And this is just the beginning. The point is that the question has to be better defined if you want a meaningful answer $\endgroup$ – Teusz May 8 '17 at 14:17
  • $\begingroup$ @Teusz That doesn't make any sense. The point of deep-learning is to provide a huge data set for the model to figure out how to classify a car vs a truck. There is no need to consider audio from a car image. Your comment is wrong and misleading. $\endgroup$ – SmallChess May 8 '17 at 14:18
  • $\begingroup$ Identification need not be performed only with basis on image data. Possible also to recognize from audio (or audio-visual) data. $\endgroup$ – Teusz May 8 '17 at 14:19
  • $\begingroup$ @Teusz Well... The question is about "an object is a car or not". OP didn't ask if the sound come from a car or a truck. I still believe your comment is wrong. $\endgroup$ – SmallChess May 8 '17 at 14:50
  • $\begingroup$ Sound can be used to classify objects -- that's the entire field of acoustic event detection. Check it out! $\endgroup$ – Teusz May 8 '17 at 15:20
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That's definitely possible with Convolutional Neural Networks. You should follow Google's tutorial at:

https://www.tensorflow.org/tutorials/deep_cnn#cifar-10_model

The tutorial will teach you how to build a deep learning model for classifying cars, trucks etc. It's an easy tutorial to follow.

Your model will build on images pixels. The classifier will know what a car looks like.

enter image description here

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