Deep Learning

Why is deep learning important to humans?

Deep Learning is a creation of machines which use techniques inspired by the human brains ability to learn recently we simply didn’t have enough data and processing power to train a machine to learn deep learning neural networks learn many levels of abstraction they range from simple concepts to complex ones this is what puts the deep in deep learning.

Keep one thing in mind, deep learning is not just about piling a bunch of layers of neurons and training them, it’s about the fascinating backgrounds that come into knowledge when you consider compositionality that the world around you is built of complicated features which are in turn built out of smaller easier features that are useful where you are doing machine learning in general.

To interpret what deep learning is, it’s first essential to distinguish it from the other branch of knowledge within the field of AI. One thing to keep in mind is that it is highly time-consuming.

Why everyone wants to be in the deep learning game?

In 2011 Andrew Ng Stanford’s CS professor founded Google’s GOOGLE Brain project, which formed a neural network trained with deep learning algorithms, which excellently proved capable of accepting high-level concepts such as cats.

Deep networks can learn features in an unsupervised manner. A lot of classical work in machine learning for practical applications  (such as speech recognition, image classifications) involved handcrafting features for the particular applications. Deep learning takes features engineering out of the picture.

With enough data and a good network architecture ( there are several heuristics one can use crafting good architectures for a particular problem), neutrons in a deep neural network can discover abstract features the deeper you will go in the network, the more abstract the features will be. So for many other applications, you can just drop in your deep network and let the network discover features by itself.

 The neural network not only learns how to learn these features but also knows how to combine them well. This is because it knows how crucial definite features are compared to others for the categorization task at hand. This distributed feature representation is therefore very powerful (in some sense, it has more degrees of freedom and therefore it can approximate more complex functions well) compared to other learning representations.

Again, in fiat to discover such a representation, you need a lot of data. However, as the world beget more and more data each day, it is only sensible to use technologies which can discover features from them in a machine-driven fashion.

And with past betterments in GPU technology courtesy to gamers out there, a lot of matrix computations (which are computational bottlenecks) can be done very efficiently in parallel. Therefore these were the some of the reasons why deep leaning is important to humankind.

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Image credit: gigaom

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