How do you think Google translates the entire webpage to another language in a matter of seconds or Alexa understands your language (human speech) and executes the order? All this has become possible through the power of deep learning. It is so embedded in our lives already that the intelligence no longer feels out of the box. Let’s navigate through What is Deep Learning exactly for better understanding.

What is Deep Learning?

Deep learning is, basically, a subset of machine learning which in turn is a subset of artificial intelligence. It is a type of machine learning inspired by the structure of the human brain. This structure here is referred to as an artificial neural network. As the deep learning algorithms are inspired by the human brain, deep learning functions based on large amounts of data. Just like we learn from experience, the deep learning algorithm learns by performing a task repeatedly, tweaking it each time to improve the outcome.

Let’s understand that with an example – 

Suppose you build a machine that can differentiate between tomatoes and apples. If this is done using machine learning – you’ll need to tell the machine the specific features using which the two can be differentiated (supervised learning). With DL, on the other hand, these features are extracted by the machine itself (through neural networks) without human intervention. 

Note that this means a much higher volume of data is required for deep learning to work.

As per research, we generate approximately 2.6 quintillion bytes of data—that’s the resource that makes deep learning possible.

How Does Deep Learning Work?

Suppose a neural network that is trained to identify handwritten numbers. 

  • Each handwritten number is present as an image –  28 times * 28 pixels. That is 784 pixels. 

  • These 784 pixels are inputted to the neurons (here’s where processing takes place) in the first layer or the input layer of the neural network. 

  • On the other end is the output layer and the layers in between are the hidden layers. 

  • Now, in the output layer, each neuron represents a digit. 

  • The information is transferred from one layer to another over channels. Each of these connecting channels has a value and is therefore known as a weighted channel. 

  • All the neurons have a unique number associated with them referred to as  – bias. The weighted sum of inputs that reach the neuron are added to this bias.

  • And further applied to a function known as the activation function. It is the result of this activation function that determines if the neuron will get activated or not. 

  • The activated neurons pass the information to the layers that follow. This goes on up to the second last channel. 

  • Finally, the one neuron activated in the output layer matches the input digit. 

  • Note that these weights and biases are adjusted continuously so that a well-trained network is formed.

Why Deep Learning? 

  1. A huge amount of data – There’s a humongous amount of data around us. And all this data is majorly present in a raw and unstructured state. Where machine learning is equipped to handle structured data, it is only deep learning that can process such huge amounts of unstructured data and derive meaningful information from it. 

  1. Handles complexity – DL works in a way that it can handle and process complex algorithms in a quick and easy manner as compared to other subsets of artificial intelligence. 

  1. Feature extractions – Machine learning algorithm work on labeled data. Deep learning, on the other hand, takes a massive volume of data and does the exhaustive process of feature extraction by itself. It extracts the features by which it can differentiate between different entities. All of it without human intervention. 

  1. In sync with the current demands – As the amount of data goes on increasing (which it is – at a rapid rate), the abilities of machine learning are bound to decrease. But deep learning leverages this data to produce results that can make a difference across industries. It works best with massive volumes of data. 

Real-life examples of DL

  •  Virtual assistants – It is through deep learning that Alexa, Siri, or Cortana, understands your language and carries out the appropriate task. It is what enables interaction between you and your personal assistant. 

  • Translations – There are so many different languages in the world. No one knows all. But deep learning does! 

Deep learning algorithms can facilitate translation into different languages in a matter of seconds. 

  • Self-driving cars – How do you think a car without a driver can understand when to slow down or stop, how does it regulate exactly as it would with a human driver? It has a role to play here too.

  • Facial recognition – Deep learning algorithms are adept at facial recognition. This can be used for security purposes. But you’ve surely come across such a situation in real-life too. Tagging people on our facebook posts for example. It is through DL that the tag is displayed without explicit mention.

  • Pharmaceuticals industry– Deep learning algorithms can bring personalized medicines for the individual’s specific genome. And this will prove to be one of the greatest revolutions in the medical sciences. 

Limitations of DL 

As deep learning explores possibilities that we couldn’t have imagined until a few years ago. There are still areas that need innovative solutions. 

For now, we can term them as limitations –  

  • Deep learning requires a huge amount of data to function optimally. However, processing this unstructured data is not possible for every machine. 

  • Training the neural network of DL requires Graphical Processing Units. And GPU’s are much more expensive as compared to CPU’s.  

  • Training neural networks can take up to months. This increases with the amount of data and layers in the network.

Deep Learning is nothing short of a boon right now. The best you can do is – leverage the opportunities and serve the stream with futuristic solutions.

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