What is deep learning, its concept and how it works?

Introduction
Let’s first understand what deep learning is.
What is the concept of Deep learning?
What are types of Deep learning algorithms?
How does Deep learning work?
Takeaway

Introduction

Deep learning started receiving all-time high appreciation in 2012. Many applications have been developed using deep learning, such as computer vision, speech recognition, language translation, etc. It is also widely used for making self-driving cars possible.

This blog explains how it works and its concept in simple words.

Let’s first understand what deep learning is.

Deep learning technology is a powerful tool for solving a common problem faced by researchers and data scientists, which is to come up with a prediction or a decision taking action from the given set of data.

It is the new technique used by computers to master tasks just like humans. It deals with algorithms that can learn multiple levels of representation and abstraction for decision making.

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What is the concept of Deep learning?

The concept, in simple words, is a class of machine learning techniques that utilises multiple processing layers to learn from the input data. It is done by passing input data through each layer and getting some output that becomes the following layer’s input. This process continues until all required layers are reached.

In simple words, the concept of deep learning algorithms work on two fundamental ideas:

Learning: It is the process of feeding information to a deep learning algorithm and giving output.
Representation: This is how this input data fed to the computer is represented so that it can learn from it. For example, we need to decide how to represent our data (images, text etc.). The better representation allows faster processing and more accurate results.

What are types of Deep learning algorithms?

There are two types of deep learning algorithms that exist:

Supervised Algorithms-

We have labelled data in this algorithm, which means we already know what is there in the data, so for example, let us consider we have a dataset of images, and each image has some information regarding whether it belongs to the ‘flower’ category or not. So here, under supervised learning, the algorithm will learn from these labels and then apply them to other data sets. With this method, the accuracy rate is more than the unsupervised one because labels are already provided to machine learning algorithms to work with it and direct predictions.

Unsupervised Algorithms-

In this type of deep learning algorithm, input datasets don’t contain any labels or information about what is present in the dataset. After processing through several layers of these algorithms, the machine learning algorithm will detect different data patterns, classes, and relations. With this type of algorithm, the accuracy rate is low because no labels or information about the data set is given to the algorithm, so it has to find out itself.

These are some basic deep learning algorithms that exist which can be explicitly used for specific tasks, but other types also exist like Deep Belief Networks, Deep Boltzmann Machines etc., Only thing that needs to know here is each one has its use cases and applications where they perform best, and we should pick them up accordingly.

How does Deep learning work?

Deep learning is a part of machine learning. It can be defined as a process where we feed the computers with data, and these machines themselves learn from these datasets and recognise patterns, objects etc. It lets us teach or train computers to do specific tasks without writing any code because if we need to write code, it will not be that efficient.

So, how does deep learning work? After understanding deep learning, let’s try to understand the working principle of this algorithm now. So, for us to know how deep learning works, first of all, we should know about neurons which are cells present in our brain that capture inputs coming through sensory organs like eyes, ears, etc.

When a neuron receives a signal, it has some information or not, but it has to be activated to give an output. Hence, the neuron passes the electrical signal through its body, and it gets activated. This output will either stimulate or inhibit other neurons, so by activating these neurons further, we can get the complete picture of what is given as input.

Example of how deep learning works:

Here is a simple example of how deep learning recognises handwritten numbers, and we will try to make an algorithm that can identify these handwritten digits. So, for this, we need to know what all things are required and how it will work?

Input: We need some images containing handwritten numbers like (0-9). Suppose our input image has more than one number. In that case, we should be able to combine them both as an input for the machine. Because if we feed only a single image (containing either 0 or 9), it won’t be easy for the machine to guess which one is present in the image because there are infinite possibilities of either being ‘0’ or ‘9’.
Representation: Now, how will the machine learn about these numbers so it can give us output for our input image? For this, we have to choose appropriate features or properties that are needed for this task, keeping in mind what all things are present in the image which are required. So, different properties are present in an image like lines, curves etc.
Classification Algorithm: It is known to us that there are infinite possibilities of either being ‘0’ or ‘9’ because the same could be true with other digits. But, if we somehow manage to delimit all these possibilities, the problem becomes easy because now only one digit is left, which tells us which number is written.

So this is how deep learning works. After choosing the proper representation of the dataset, we have to select a classification algorithm responsible for selecting the right features from the given data set.

Takeaway

We can say that deep learning is a part of machine learning, where we let computers learn from the data fed to them without writing any code.

By choosing appropriate features or proper representation of our data set, we should feed these machines to recognise patterns on their own, which makes the process faster and efficient.

So, this was a brief about deep learning, its concept, and how it works.

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Also, comment below if you wish to contribute by offering more insights into deep learning. We’d be glad to hear from you.

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