Machine Learning is the process of automatically learning without any particular set of instructions. It is the study of statistical models and algorithms to act like humans without the interference of humans. On the other hand, Deep learning is a subset of machine learning with the implementation of neural networks. The outcome of deep learning is natural as humans. So, there is a complex situation between ML and DL. In this article, we will minutely discuss Machine Learning vs Deep Learning with the best example. Be sure that you will be through and clear within 5 minutes!
Machine Learning
To understand Machine Learning vs Deep Learning we have to know the definition of each segment. The idea of machine learning comes in the late 1980s or early 1990s. It is a subset of artificial intelligence for using statistical methods to allow the machine to work with experience. It is the algorithm that gives the ability to compute or learn from data to make any prediction.
Machine Learning Example
In our day to day life, we use lots of machine learning applications like making any prediction, find the spam mail, virtual personal assistant, traffic prediction, online fraud detection, stock market trading, automatic translation, and image recognition.
Defining More Details
We want to clear the machine learning example in a more detail form. Suppose you want to judge cloth whether it is a mini dress or long pants? You have to clear the computer regarding all input. For example for them for the mini-dress, it should have neck size, shoulder straps, no zipper and holes for arms on each side of the neck. That means you have to tell the computer what a mini dress looks like.
Again if you want to give input of a pant you have to define the criteria like a split for legs, opening on top is a smaller, buttons, belts and pocket in front. Show the computer will make the model. If you show the image of pants or mini dress it will tell exactly what it is.
How it will works?
After getting data (image) from your computer it will define the feature automatically. It will create a model and make a prediction. Now, The machine-learning algorithm to predict your questions. If you show the thousands of pants on the mini dress it will tell exactly the correct answer.
Deep Learning
Deep learning is a subset of machine learning that is connected with machine learning model and algorithms works for the function and structure of a brain which we called it artificial intelligence network. You can think about our brain that we have millions of neurons. Every time we can see a lot of new things. Neurons electricity find between neurons and find new things automatically. So, deep learning is similar to the neurons network. The scientists call it an artificial neural network where there is a layer of neuron network.
In machine learning, we predefined the data which is called data labeling. But in deep learning, there is no requirement for leveling the data. It automatically learns from the situation and gives you the ultimate answer.
Why Deep Learning is Everywhere?
To establish or implemented deep learning you need three conditions who are given below:
- Lots of leveled data
- High-performance GPU’s
- Sophisticated algorithm
Which One Should You Pick?
So based on the particular scenario we have to select the appropriate one like machine learning or deep learning. When we have lots of labeled data we can go for deep learning otherwise we have to remain with machine learning. On the other hand, if the performance of GPU is high then you should go for deep learning otherwise we will use machine learning. Moreover, for the sophisticated algorithm, we always choose machine learning.
Machine Learning vs Deep Learning
In machine learning, after providing an image the machine extracts the features, matches machine learning and tells what the objectives are.
In deep learning, there is no requirement for adding the input manually. It automatically extracts the images and matches with the features. After analyzing it provides the answer. The image below will narrate itself automatically about deep learning.
We want to make clear the concept again based on another image. The example is based on hardware and data size.
If you have good hardware and lots of data you can go for deep learning. If you have good hardware and less data you may also choose deep learning. if the header configuration is low but data is huge then go for deep learning. But if low configuration hardware and less data then go for machine learning.
Machine Learning vs Deep Learning With Example
We want to start the example of the picture. In the picture, you can see a cat or dog? How you will tell the correct answer? In your life, you have seen thousands of cats and dogs. Even in your life, you may be confused after watching the image of what the actual answer is? So the computer also may have some error.
In a machine learning model we labeled the data who is called training data, the machine extracts the feature and classifies a machine learning model. After testing the data answer based on prediction.
You can show another example of machine learning Vs deep learning. It is a search engine if you find a particular topic it will show you some results. If you skip the first, second and third page, the Google machine learning all think, oh! the user doesn’t like this result. It will learn your preference.
The deep learning if you show an image after sufficient processing it will show you the colored image permutation, combination, and application of neural network. After getting one black and white image it searches on the web for available data library to recognize the feature. It maps a particular color to an object and shows the fully colored image.
How Are Machine Learning and Deep Learning Related?
Though our today’s topic is machine learning Vs deep learning. They are more dependent on each other rather than conflict. Which is the choice of the user which should be used for problem-solving?
Deep learning is highly expensive than machine learning. It requires a higher configuration of hardware compared to ML. When the data size is enormous we use deep learning otherwise we use machine learning. So we can see there is a reverse relationship between machine learning and deep learning.
Should I Learn Machine Learning or Deep Learning?
After studying my article you will feel that it is a funny question. Artificial intelligence is above all of the hierarchy. Machine learning is a subpart of artificial intelligence. On the other hand, deep learning is a subpart of machine learning. So without the knowledge of artificial intelligence and machine learning, you cannot go with only deep learning.
When the learning process is over you can choose anyone to solve your problem. If the dataset is big to select the deep learning process or simply select the machine learning procedure. Here the Idea machine learning Vs deep learning is useless.
Is Deep Learning Supervised, Unsupervised or Something Else?
The terms supervised or unsupervised is the method of machine learning. Another hand deep learning is a subpart of machine learning. Machine learning and deep learning is a subset of artificial intelligence.
Deep learning can be used as supervised or unsupervised or both. Face recognition, object detection, and image classification are an example of supervised learning. Image encoding and word embedding at the example of unsupervised learning. You can use deep learning by any of the methods.
Why Do All Researchers Use Deep Learning in Image Processing?
Researchers like deep learning than machine learning in image processing. Because of images, not a small thing as we think. An image has illumination, background, crowded scene, camera, angles pixels, etc. The researchers play with a large volume of images. Where it is not possible to analyze with machine learning. Show the researcher use deep learning in image processing.
Another reason is that machine learning requires data labeling as input but deep learning takes input automatically. The advanced neural network analyzes the images and shows the result. So deep learning is more popular with the researchers.
What’s The Difference Between AI, ML, And DL?
The artificial intelligence think and act like a human being, the machine learning system think without being program to do so and the deep learning thinks like a human brain using artificial neural network.
ML vs DL In the Broader Aspect
In the broader aspect if we think the total set then you will find the whole process is called artificial intelligence. Machine learning is a subset of artificial intelligence. Again deep learning is the subset of machine learning. So that each of the terms is connected based on a hierarchical basis. Where artificial intelligence is the top of the hierarchy and deep learning is the below of the hierarchy.
Now we will know the difference between deep learning and machine learning. To differentiate both the learning we will select one example. You can choose one Android application to find out face, makeup item or even clothes. In our example, we will select the clothes item to show the difference between machine learning and deep learning.
The first step to recognize your product you have to apply here supervised machine learning. It is called train the machine with leveled input. For example, you have to you narrate at first what the half pants, jacket, underwear look like.
Difference Between ML And DL in Tabular Form
Now I want to show machine learning Vs Deep learning in a tabular form for better assimilation. I will also clarify with examples.
| Serial | Machine learning | Deep learning |
| 1 | Machine learning enables machines to take the decision on their own based on past data. | Deep learning enables machines to make decisions with the help of an artificial neural network. |
| 2 | It needs only a small amount of training data. | It works with only a large amount of data. |
| 3 | It was with a low configuration computer. | It requires higher configuration GPU. |
| 4 | You have to manually code or label for the future. | The machine automatically learns the features from the data. |
| 5 | In machine learning, the problem is segmented into several parts and solves the problem individually then it accumulates the total answers. | It follows the end to end manner to solve the problem. |
| 6 | The time of testing takes longer. | Deep learning takes very little time compared to machine learning. |
| 7 | The crisp rules explain why a certain decision has taken. | The reasoning may be difficult to interpret because that is decision is based on logic. |
| 8 |  |
 |
When machine learning is better than deep learning?
Machine learning is often better than deep learning for simpler tasks. It is generally faster and requires less data. Machine learning algorithms are easier to interpret and implement. They work well for tasks where data is not massive or complex. For straightforward problems, ML can be more efficient and cost-effective. Deep learning shines with large datasets and complex patterns. Machine learning is better for speed and simplicity, while deep learning excels in accuracy with big data. Use ML for simpler, smaller-scale tasks and DL for advanced, data-rich problems. Generally, deep learning is more accurate for complex tasks than traditional machine learning.
Should I learn deep learning or machine learning first?
If you’re new to AI, start with machine learning (ML). Learning ML first builds a strong foundation. Machine learning concepts are simpler and more manageable for beginners. Once you understand ML basics, you can move on to deep learning (DL). Deep learning is a subset of ML that requires a good grasp of ML fundamentals. You don’t need to learn both simultaneously; focus on ML first. After mastering ML, delve into DL for more advanced topics. Starting with ML helps you grasp the core concepts, making deep learning easier to understand later. Learning ML first is a practical approach in AI education.
Is ChatGPT machine learning or deep learning?
ChatGPT is based on deep learning. It uses advanced neural network algorithms. Specifically, ChatGPT employs transformer models. These models help it understand and generate human-like text. ChatGPT is trained on vast amounts of text data from diverse sources. This training allows it to respond accurately and contextually. The deep learning approach helps ChatGPT grasp complex language patterns. By learning from large datasets, it improves over time. Overall, ChatGPT’s performance relies on sophisticated deep learning techniques and extensive training data.
Some Example of ML and DL
Netflix uses deep learning for content recommendations. Convolutional Neural Networks (CNNs) are a type of deep learning. Tesla relies on deep learning for self-driving features. Neural networks are part of deep learning, not just machine learning. NASA uses machine learning for data analysis and predictions. SpaceX also employs machine learning for optimizing rocket launches and missions. Both organizations use advanced algorithms to improve their technologies. Deep learning helps with complex tasks like image recognition, while machine learning handles a range of data-driven problems.
Final Thought
The choice of machine learning Vs deep learning depends on your requirements. It also depends on the problem you want to solve. You can use each of them separately or make both using approaches. So far we have learned, machine learning, deep learning, the difference between ML and DL and some relevant examples. In our previous articles, we have discussed details of ML which may make you more knowledgeable.
Nasir H is a business consultant and researcher of Artificial Intelligence. He has completed his bachelor’s and master’s degree in Management Information Systems. Moreover, the writer is 15 years of experienced writer and content developer on different technology topics. He loves to read, write and teach critical technological applications in an easier way. Follow the writer to learn the new technology trends like AI, ML, DL, NPL, and BI.