DEEP LEARNING: ALL THAT YOU NEED TO KNOW ABOUT THIS SUBSET OF MACHINE LEARNING.

In our past blogs, we have covered a variety of topics revolving around machine learning. However, a very important and growing subset has not been attended to so far. So, in this blog, we will be discussing all that you need to know about Deep Learning. 

Let’s get started. 

What is Deep Learning?

Deep learning, a subset of machine learning, essentially refers to a neural network containing three or more layers. The behavior of the human brain is attempted to be stimulated by these said networks. The three or more layers help in accurate optimization and refinement, a step further than the approximate predictions which can be made through a single network.

Numerous applications related to Artificial Intelligence are driven by this algorithm, which drive services that help in improving automation, performing analytical and physical tasks. Daily use emerging products including audio support remotes, upcoming self driving cars  and credit card fraud detectors are operable using the Deep Learning technology. 

Working of Deep Learning

Deep Learning networks work in several complex forms. Using a mixture of data inputs, weights, and bias, its networks focus on mimicking the working of the human brain, but in a more efficient way. Together, these elements recognize, classify and describe objects in the data set.

The neural network comprises several layers of nodes that are connected with each other, for the refinement and optimization of prediction as well as categorization. The progression of computations within this is called forward propagation. Visible layers are attributed as the input and output layers of the neural network. 

Algorithms are used in backpropagation in order to ascertain the errors arising in the predictions and to rectify the function by going backward within the layers. Forward propagation and backward propagation ensure that the network forms predictions and rectifies errors as and when needed. 

The functioning of Deep Learning also varies on the basis of the neural network involved, therefore this is just a preliminary idea of how it works. Some examples are as follows-

• Convolutional Neural Networks that are used majorly in computer visioning and classification of image applications use complex working styles that can detect features and patterns in the image classification applications, recognition and detection of objects. 
• Recurrent Neural Networks are usually used in natural language and speech recognition applications as they are responsible for leveraging sequential and times series data.

Difference between Machine Learning and Deep Learning

The difference between the two exists due to the type of data used in both and the process of analyzing the data. While machine learning usually works with structured data to produce predictions, deep learning consumes unstructured data and runs its actions with it. Therefore, the point of difference in the two is essentially in their types of learning and the data they consume. Where the former structures all sorts of data in a pre-run, the latter does not engage in this and simply consumes data as it is. 

Applications of Deep Learning

1. Deep Learning can be used to enforce the law by analysing patterns that may indicate criminal activity.
2. It is used in Financial Services to predict future trends using algorithms and helps in portfolio management.
3. Many organisations have started using it as a means of providing Customer Services with the use of Chat and Audio bots. 
4. Image recognition applications have helped in supporting medical specialists and radiologists, which has been possible due to this algorithm. 

With this, we cover the basics of Deep Learning and understand all the important aspects of this constantly emerging technology. 

If you haven’t already, don’t forget to check out the Top 10 benefits of Machine Learning.