Nowadays, we all hear a lot about Artificial Intelligence and related areas like Machine Learning, Deep Learning, etc. Technology changes all the time, and people always run behind new ideas and opportunities. So obviously, there comes a need to gain some basic knowledge about these growing technologies.
Let’s have a mere glance at machine learning and deep learning and find out the similarities they share and the differences that make them unique.
If you don’t know what machine learning or deep learning is, don’t worry. I got that covered. This part is for absolute beginners who do not have clarity about machine learning and deep learning. Let’s start with the basics and understand what they are.
If you already know the basics, great! You can skip this part and scroll down to see the actual differences between machine learning and deep learning.
For better understanding, let’s start by having a quick look at what machine learning and deep learning are.
What Is Machine Learning?
Every human individual can learn from their past experiences. We all know that, right? But do you know that humans can train machines and make them learn from past data?
If you haven’t ever heard about it, it is possible by machine learning. Machine Learning is not just about learning but understanding and reasoning.
Machine Learning is a sub-field of Artificial Intelligence (AI), which provides systems the ability to automatically learn and improve from experience, without being explicitly programmed.
Machine learning focuses on computer programs that can access data and use them to learn for themselves. In simple words, machine learning is finding useful information from existing data.
We can give some input data to the machine learning model, which then examines the input data and gives the output according to the algorithms applied.
If it is correct, we take the output as the final result, else we provide feedback to the training model and ask it to predict until it learns.
What Is Deep Learning?
Deep Learning is a subfield of machine learning, which is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. That means deep learning works similarly to the working of the human brain.
Deep Learning is thus a Machine Learning technique that learns features and tasks directly from data. Data can be images, text, sound, etc.
Deep Learning is also known as end-to-end learning because, in Deep Learning, the task is learned directly from the data.
Now, let us take an example to understand the concept clearly. Let’s say I have a set of images of pets. I want to recognize which category to which each image belongs, for example, a cat, dog, or rabbit.
I have started with labels of images for each image (supervised learning). Using the required deep learning algorithms, the model then learns how to classify the input images into the desired categories.
Our pet image classifier model learned a lot about different types of pets. Now, if I give a new image as the input to this model, it can classify the image into the correct category.
Similarly, deep learning is used for many complex tasks, such as image recognition, image classification, speech recognition, and a lot more, which traditional machine learning fails to do effectively.
I just tried to pour some basic concepts into your brain. Now it’s time to move on.
I just tried to pour some basic concepts into your brain. Now it’s time to move on. Let us see the differences between traditional machine learning and deep learning.
Machine Learning vs Deep Learning
Deep Learning is just a subset of Machine Learning, and machine learning is, in turn, a subset of Artificial Intelligence.
Deep Learning technically is machine learning, and it functions similarly, but its capabilities are different in many aspects.
The subtle difference is that deep learning uses neural networks as the algorithm and functions in a way similar to the human brain. The way the data is presented to the system is different in machine learning and deep learning. Most often, machine learning uses structured data, whereas deep learning is used to find insights from unstructured data.
We are going to find their differences based on the following criteria.
- Data dependencies
- Problem-solving approach
- Hardware dependencies
- Execution time
- Feature Extraction
Let’s see in detail what are the differences between machine learning and deep learning based on these criteria. It’s time to deep dive!
The most important difference between deep learning and machine learning is its performance as the data increases.
When the data is small, deep learning algorithms do not perform well as we expect. It is because deep learning algorithms require a huge amount of data to understand.
On the other hand, machine learning algorithms work on the given structured data rather than a huge amount of data under any conditions.
In simple words, if the data is small, and the problem isn’t complicated, then traditional machine learning is preferable. But, if there is a huge amount of unstructured data, deep learning can be the right option.
Problem Solving Approach
To solve a problem using a machine learning algorithm, it is important to break the problem down into different parts, solve them individually, and combine them to get the result.
Deep learning, in contrast, helps to solve the problem end-to-end. It means the task is learned directly from the data. Even if the problem is complicated, deep learning can handle it.
Deep learning algorithms highly depend on high-end machines, contrary to machine learning algorithms, which work on low-end machines. This is because a deep learning algorithm requires a GPU (Graphical Processing Unit), which is a major part of its working.
In general, deep learning tasks are highly intensive. It can be extremely difficult and risky to train a deep learning model using a computer with normal specifications. Hence, a GPU is a must-have requirement for deep learning.
A GPU is a specialized CPU that has many times the number of processing cores. The advantage of having a GPU is that the operations performed by deep learning algorithms can be efficiently optimized.
Deep Learning algorithms take a long time to train. This is because there are so many parameters in a deep learning algorithm to train. That’s why having a system with the required specifications is really important.
However, machine learning relatively needs much less time to complete training, varying from a few seconds to some hours. We can perform machine learning tasks using a system with normal specifications.
Deep learning is a subset of machine learning that takes data as an input and makes wise decisions using an artificial neural network.
On the other hand, machine learning being a super-set of deep learning takes data as an input, parses that data, tries to make decisions based on what it has learned while being trained.
Machine learning algorithms are interpretable regarding what parameters they chose and why they chose those parameters, but on the other hand, deep learning techniques are not like that.
Even if the deep learning algorithms can win over humans in performance, they are still not reliable when it comes to deploying them in the industry.
The output of machine learning is usually a numerical value like a score or a classification. However, the output of deep learning might be a score, an element, speech, text, etc.
Deep learning is considered to be a suitable method for extracting meaningful features from the raw data.
It does not depend on hand-crafted features like local binary patterns, and most importantly, it performs a hierarchical feature extraction.
On the other hand, machine learning is not the best way to extract meaningful information from the data. It relies on hand-crafted features as an input to perform well.
Artificial intelligence is a booming technology, and machine learning is a major sub-field of that. And, deep learning is an important sub-field of machine learning. Both machine learning and deep learning are similar in many ways, but they have some differences as well.
Both of them differ mainly based on their data dependencies, problem-solving approach, hardware dependencies, execution time, functioning, interpretability, output, and feature extraction.
We can conclude that for easier tasks and simpler data, we can use machine learning algorithms.
But for complex tasks like speech recognition, image recognition, etc., and problems involving a tremendous amount of data, we need deep learning for sure.
I hope this article was helpful to you. If you have any doubts or queries regarding this topic, feel free to let me know in the comments section. I will be happy to respond.
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