What Is Deep Learning Vs Machine Learning?

You are constantly hearing about artificial intelligence. Two words are frequently mentioned machine learning and deep learning. They are not the same thing. It is good to know the difference to know what technology is about today.

Imagine it is a series of Russian dolls. AI is the biggest doll. Machine learning is embedded within it. Deep learning is a smaller doll of machine learning. One is a subset of the other.

Let’s break it down.

What is Machine Learning?

Machine learning is an element of AI. It is a means of computer learning. We do not set them strict rules. Rather, we feed them information. The computer uncovers patterns and uses that information to arrive at a decision.

Suppose you have to train a computer to identify cats in images. You would not say it about fur or whiskers. You would show it thousands of cat pictures. You would also show it catless pictures. The program studies the patterns that constitute a cat. It improves with time as it has more data.

This is the core idea. The machine is an example learner.

What is the Process of Machine Learning?

Models are usually made manually. They select what is significant about the data. Features are the things that the model must observe. In our cat example, features can be colour, shape or texture.

A specialist may determine that the length of the whiskers is an important characteristic. They help guide the model. It requires the involvement of humans. These features are then used to learn using the model.

Machine learning can be of three types:

Supervised Learning: This method involves training a model using labeled datasets. For instance, every photo is identified as either containing a cat or not.

• **Unsupervised Learning: There are no labels on the data. The model tries to find latent patterns. It could cluster similar photos together without the knowledge of the reasons.
• **Reinforcement Learning: The model is a trial and error learner. It rewards good decisions and punishes bad decisions. It is able to learn how to make improved choices in order to get more rewards.

So, What is Deep Learning?

Deep learning is a particular form of machine learning. It is very powerful. It learns by means of artificial neural networks.

Such networks are modeled after the human brain. They are connected node layers. The term “deep” is used since these networks consist of several stacked layers.

Automation is the most important distinction. Deep learning does not require a human being to select features. It determines the significant features on its own.

Let’s go back to the cat photos. Deep learning is a process where you simply feed the system a lot of pictures. You tell it which have cats. What to look for is determined by the deep learning model. It may find out that ear shape and whiskers matter. It acquires these features automatically.

The Neural Network: Deep Learning.

Consider a neural network as a group of individuals. Every individual considers a small aspect of the issue.

Basic things are seen in the first layer of the network. It may seek edges or lines in an image.

The following layer picks up those lines and integrates them. It can start to see simple figures like circle or square.

Those shapes are combined in a deeper layer. It might find a nose or an eye.

The last layers complete it. They look at an entire face and make the decision, “This is a cat.

One layer shares its results with the next. It is the way these layers communicate with each other that the network learns. It gets better with more data.

Important Comparisons: Simple Compare and contrast.

Machine Learning	Deep Learning
Often needs a human to pick features.	Finds features on its own.
Can work with smaller amounts of data.	Needs huge amounts of data to work well.
Works on regular computers.	Needs powerful computers with special chips.
Easier to understand how it makes a choice.	Hard to know why it made a choice. It is a “black box.”
Good for simpler tasks like sorting data.	Excellent for complex tasks like image and speech recognition.

Which One Should We Use?

The approach you choose is shaped by the kind of issue you’re aiming to address.

Use machine learning when:
In this case, you’re dealing with a huge dataset that contains an overwhelming amount of information.

• You must know how the model has made its decision.
• You have a relatively simple problem. This involves making predictions on sales or spamming.

Use deep learning when:

• You are dealing with a huge amount of data.
• The problem is very complex. Imagine the power to read faces, to decode languages or to drive cars.
• The information is not structured such as images or sound files.

Final Thought

Machine learning is a general data learning tool. That tool is an improved version of deep learning. By leveraging neural networks, it becomes capable of solving problems that are far more challenging.

Most of the wow in tech today is due to deep learning. This technology fuels applications like voice recognition, autopilot systems in cars, and more.
Nevertheless, machine learning remains enormously significant. Not all the issues require deep learning solution. In some cases it is smarter to use a simpler machine learning model.

Now you know the difference. You may see how they both teach computers, and teach us too.

Are you confused about deep learning and machine learning? Get acquainted with the essential differences, examples, and practical uses of ML vs DL in this easy to understand guide.

Frequently Asked Questions For Deep Learning Vs Mechie Learning