What is Deep Learning and Why It is Important?

To understand what deep learning is, we first need to understand the relationship deep learning has with machine learning, neural networks, and artificial intelligence.

The best way to think of this relationship is to visualize them as concentric circles:

At the outer most ring you have artificial intelligence (using computers to reason). One layer inside of that is machine learning. With artificial neural networks and deep learning at the center.

Broadly speaking, deep learning is a more approachable name for an artificial neural network. The “deep” in deep learning refers to the depth of the network. An artificial neural network can be very shallow.

Neural networks are inspired by the structure of the cerebral cortex. At the basic level is the perceptron, the mathematical representation of a biological neuron. Like in the cerebral cortex, there can be several layers of interconnected perceptrons.

1)The first layer is the input layer. Each node in this layer takes an input, and then passes its output as the input to each node in the next layer. There are generally no connections between nodes in the same layer and the last layer produces the outputs.We call the middle part the hidden layer. These neurons have no connection to the outside (e.g. input or output) and are only activated by nodes in the previous layer.

2)Think of deep learning as the technique for learning in neural networks that utilizes multiple layers of abstraction to solve pattern recognition problems. In the 1980s, most neural networks were a single layer due to the cost of computation and availability of data.

3)Machine learning is considered a branch or approach of Artificial intelligence, whereas deep learning is a specialized type of machine learning.

4)Machine learning involves computer intelligence that doesn’t know the answers up front. Instead, the program will run against training data, verify the success of its attempts, and modify its approach accordingly.

5)Machine learning typical requires a sophisticated education, spanning software engineering and computer science to statistical methods and linear algebra.

There are two broad classes of machine learning methods:

??* Supervised learning

??* Unsupervised learning

In supervised learning, a machine learning algorithm uses a labeled dataset to infer the desired outcome. This takes a lot of data and time, since the data needs to be labeled by hand. Supervised learning is great for classification and regression problems.

For example, let’s say that we were running a company and want to determine the effect of bonuses on employee retention. If we had historical data – i.e. employee bonus amount and tenure – we could use supervised machine learning.

With unsupervised learning, there aren’t any predefined or corresponding answers. The goal is to figure out the hidden patterns in the data. It’s usually used for clustering and associative tasks, like grouping customers by behavior. Amazon’s “customers who also bought…” recommendations are a type of associative task.

While supervised learning can be useful, we often have to resort to unsupervised learning. Deep learning has proven to be an effective unsupervised learning technique.

Why is Deep Learning Important?

Computers have long had techniques for recognizing features inside of images. The results weren’t always great. Computer vision has been a main beneficiary of deep learning. Computer vision using deep learning now rivals humans on many image recognition tasks.

Facebook has had great success with identifying faces in photographs by using deep learning. It’s not just a marginal improvement, but a game changer: “Asked whether two unfamiliar photos of faces show the same person, a human being will get it right 97.53 percent of the time. New software developed by researchers at Facebook can score 97.25 percent on the same challenge, regardless of variations in lighting or whether the person in the picture is directly facing the camera.”

Speech recognition is a another area that’s felt deep learning’s impact. Spoken languages are so vast and ambiguous. Baidu – one of the leading search engines of China – has developed a voice recognition system that is faster and more accurate than humans at producing text on a mobile phone. In both English and Mandarin.

What is particularly fascinating, is that generalizing the two languages didn’t require much additional design effort: “Historically, people viewed Chinese and English as two vastly different languages, and so there was a need to design very different features,” Andrew Ng says, chief scientist at Baidu. “The learning algorithms are now so general that you can just learn.”

Google is now using deep learning to manage the energy at the company’s data centers. They’ve cut their energy needs for cooling by 40%. That translates to about a 15% improvement in power usage efficiency for the company and hundreds of millions of dollars in savings.