Deep Learning Vs Machine Learning Vs Artificial Intelligence

Introduction

Artificial intelligence, machine learning, and deep learning have turned out to be buzz words in the field of technology and are being used in today’s IT industry.

Many people bear misconceptions about these three terms. That’s why we have brought you an insight into them and let you have accurate information about them.

Let’s get started with the understanding of all these terms!

Machine Learning VS Deep Learning

Most of us have at least a rough idea of what artificial learning is. So, we can move to have a close look at machine learning and deep learning for making a difference between these two.

• Machine learning makes use of classical algorithms for performing various tasks like clustering, regression, or classification.
• These algorithms must be trained on data and the more data you will expose to algorithms, the better it will become.
• The training part of machine learning indicates that this model tries to optimize specific dimensions.
• In simple words, you can understand it to minimize the error between their predictions.
• For this, you must know the error function, which is also called loss function or an objective function.

How you will minimize the error?

For this, you need to do the following:

• One way can be the comparison between the prediction of the model with the truth value of ground and the adjusted parameters of the model in such a way that the next time, the error between these two values gets lowered.
• Until the predictions reach a good level, the difference between the predictions of the model and the value of ground are small to the maximum.
• Within short machine learning, models turn to optimized algorithms.

DEEP LEARNING- The next thing

Now, we need to focus on the next main thing, which is deep learning.

It can be said to be a very new thing in the field of artificial learning, which is based on artificial neural networks.

It can be seen as a subfield of machine learning because the algorithms of deep learning also require data for learning to solve tasks.

That’s why both the terms Machine learning and deep learning are assumed to be the same. The fact is that both these terms have different system capabilities.

• This technology uses the structure of multiple layers of algorithms that are called neural networks.
• Those networks are equipped with various capabilities that allow deep learning models to solve various tasks that machine learning models can never solve.
• It is because of deep learning that there have been advances in intelligence.
• Without deep learning, a few things would not have been possible like self-driving cars, chatbots, or personal assistants like Alexa and Siri.
• We can even say that the new industrial revolution is being driven by an artificial neural network with the collaboration of deep learning.

DEEP LEARNING IS BETTER THAN MACHINE LEARNING

This technology is better than machine learning because of the following reasons:

1. Feature extraction

When it comes to the advantage of deep learning, the first advantage is the needlessness of the so-called feature extraction.

Feature extraction usually leads to complexity and requires detailed knowledge of the problem. This is something that must be adapted, tested, and refined over several illustrations.

• When it comes to artificial neural networks, they do not require the step of feature extraction.
• Here, you get a more and more abstract and compressed representation of the raw data is created over several layers of artificial neural networks.
• This compressed representation is utilized for producing the result.
• We can say that feature extraction is a part of the process that occurs in an artificial neural network.
• This step is also utilized during the training for obtaining the best possible abstract representation.
• It also indicates that the models of deep learning require little to no manual effort for performing the feature extraction process.

2. The Era of big data

The second advantage that can also be understood as one of the biggest reasons for the popularity is powered by a large amount of data.

• Big data is expected to provide various opportunities for new innovations in deep learning.
• The models of deep learning increase their accuracy with the increasing amount of training data.

In other words, here we are trying to make you aware of the significance of data.

Concluding Words

All in all, we can say that deep learning is at the infancy level and needs time to grow. Machine learning is a much mature part and can be said the parent of deep learning.

Artificial learning is highly mature among these three that enjoys hegemony in various sectors. AI is something that you can easily find even in small companies.

So, we can say that AI wins here as it has grown fully and others need time to flourish.

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