Your choice of Deep Net - Ep. 4 (Deep Learning SIMPLIFIED)

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.

Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv

A forewarning: this section contains several new terms, but rest assured – they will all be explained in the upcoming video clips.

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.

Credits:
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal