Deep learning has been making huge waves in the world these days. Let’s explore what deep learning is and how it works. By the end of this post, you’ll be able to take your first steps into this industry confidently.

What is deep learning?

Deep learning is a sub-field of machine learning, which deals with mimicking the way the human brain works. Here, CIO companies use neural networks to collect large quantities of data, identify patterns and make predictions of what could happen in different scenarios.

The goal of deep learning

The primary objective of deep learning is to automate the process of predictive analytics. CIO managers and IT managers task their team to use unstructured data for predictive analysis. This includes images, videos, texts, audio files, etc. In fact, this is how deep learning differs from machine learning, which uses processed data. Deep learning algorithms are designed to distinguish individual features based on how they look or sound. This is why some of the most powerful surveillance tools are powered by deep learning.

In deep learning, CIO companies use multiple artificial intelligence programs and tools are used together to collate and analyze data without human intervention.

How does deep learning work?

- Deep learning works on three neural network layers - Input Layer, Hidden Layers and Output Layer. These neural layers are the brain of deep learning, where data processing takes place. Each layer is connected to another and this is what facilitates data processing and analysis.

- A very large volume of data is collated to work on the analysis and is put into the deep learning program. This forms the input layer.

- The data is transferred to the hidden layers (there are multiple hidden layers) through connecting channels. Each channel is attached to an individual inputted entity and it is given a specific weightage of importance.

- To the weighted channel is added a Bias. Essentially, the bias is a number that is used to account for any instances of data bias. This is where specific data sets may have a higher likelihood of being represented/used more than others. 

- An activation function is applied to the data being worked on. This helps the deep learning algorithm learn how to compare and identify differences between each data value. This activation function helps demystify complex data patterns and allows the deep learning algorithm to become smarter. 

- Finally, the activated data is passed through all the hidden layers, where it is adjusted to remove any bias and report clean, verified data. All the results you receive through the output layer are standardized according to the parameters you have set.

Real-world applications of deep learning

- Businesses use bots that are powered by deep learning to simulate realistic conversation when customers visit the company website. 

- Scientists predict potential natural or man-made disasters and help cities prepare for them.

- Medical professionals chart the course of an illness or pandemic in advance and develop the right treatments quickly. Medical equipment also uses deep learning to identify and evaluate MRI scans, pick out cancer cells, etc. 

- Law enforcement officials use deep learning to solve and prevent crimes by identifying criminal patterns and faces from surveillance tapes.

- Automobile manufacturers are developing self-driving cars, which use deep learning to understand traffic patterns and drive the car safely.

- IT managers and CIO managers design and develop near-human robots to automate work, etc.