Deep Nets come in a large variety of structures and sizes, so how do you decide which kind to use? The answer depends on whether you are classifying objects or extracting features. Let’s take a look at your choices.

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If your goal is to train a classifier with a set of labelled data, you should use a Multilayer Perceptron (MLP) or a Deep Belief Network (DBN). Here are some guidelines if you are targeting any of the following applications:

- Natural Language Processing: use a Recursive Neural Tensor Network (RNTN) or Recurrent Net.
- Image Recognition: use a DBN or Convolutional Net
- Object Recognition: use a Convolutional Net or RNTN
- Speech Recognition: use a Recurrent Net

If your goal is to extract potentially useful patterns from a set of unlabelled data, you should use a Restricted Boltzmann Machine (RBM) or some other kind of autoencoder. For any work that involves the processing of time series data, use a Recurrent Net.

What deep nets do you see a use for? Please comment and let me know your thoughts.

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Sliding window object detection is a technique that allows you to detect objects in a picture. This technique is not very efficient as it is very compute intensive. Recently new techniques has been discovered that tried to improve performance such as R CNN, Fast R CNN, Faster R CNN etc. YOLO (You only look once) is a state of the art most modern technique that outperforms all other previous techniques such as sliding window object detection, R CNN, Fast and Faster R CNN etc. We will cover YOLO in future videos.

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