The Contrast: Machine Learning Vs. Deep Learning
All of a sudden everybody has started discussing the terms- Machine Learning and Deep Learning regardless of whether they are understanding the dissimilarity or not!
If you too have been questing yourself about what deep learning and machine learning is? Read till the end; keep on reading for exploring a detailed comparison in a simple layman’s guide.
Machine learning and Deep Learning, both of them offer ways to train models and classify data. Let us start discussing it with a classic example of Dogs vs Cats. In the picture shown below, are you able of identifying cat and dog?
If yes, then How? The chances are, you may have seen cats and dogs over and so you’ve learned how to identify them.
This is what through machine learning and deep learning beings are trying to make the computer understand- learning from the recognized examples. However, sometimes even a human makes mistakes and so we can expect a computer system making similar errors.
What is Machine Learning and Deep Learning?
For maintaining a transparency let’s start with discussing the basics of Machine Learning and Deep Learning.
– Machine Learning
The widely explained definition by Tom Mitchell of Machine Learning in a nutshell; it’s a science of getting the computers to learn and act without being explicitly programmed. Lately, machine learning has given us self-driven cars, effective web search, practical speech recognition, and a huge understanding of the human genome. Moreover, machine learning is so extensive that today every individual uses it in his daily life without even knowing it.
– Deep Learning
Machine learning is the leading and an exciting field out there. On the other hand, deep learning symbolizes its true leading edge. However, deep learning isn’t new in the market but due to its extensive publicity, it is getting more attention.
Machine Learning Vs. Deep Learning
Now that you have understood the variations between machine learning and deep learning, we will, by taking a few important points; discuss the two techniques:
1. Data Dependencies:
One of the most important differences between machine learning and deep learning is, their performance based on the scale of increasing data. When the data is small; deep learning algorithms don’t carry out that well. The reason behind it is: deep learning algorithms need a huge amount of data to grasp it perfectly. On the other hand, the traditional machine learning algorithms with its handcrafted rules sound more powerful and superior.
2. Hardware Dependencies:
Deep learning algorithms heavily depend on high-end machines; in opposition to machine learning algorithms that are compatible with the low-end machines. The reason behind this is the requirement of a huge GPU; that is an integral part of its functioning. The deep learning algorithms innately do a lot of multiplication operations. These operations can be easily optimized using a GPU, as the GPU’s are meant for this purpose only.
3. Problem Solving Approach:
When working on problem-solving; with the help of machine learning algorithm, it is suggested to break the problem into multiple sections, solve them individually and later, combine them to get the desired results. Deep learning, in contrary; supports solving problems end-to-end.
Let’s perform an example for better understanding:
Supposedly, you are provided with a task of multiple objects detection. The task is of identification of what the object is and where is it located in the image. In machine learning approach, the task is divided into two simple steps; object detection and object recognition. first, you would have to use a bounding box detection algorithm like the GrabCut; to quickly skim through the image and finding out the objects. Later, for all the recognized objects you would have to use some object recognition algorithms like SVM with HOG for recognizing the relevant objects.
On the other hand, in deep learning, you would process end-to-end. For example, in a deep learning algorithm, you would have to pass in an image and in the result, it will automatically give out the location along with the name of the object.
4. Execution time:
Generally, deep learning algorithms take a lot of time to get trained; this is due to the huge parameters in a deep learning algorithm that training them takes longer than usual. Unlike deep learning, machine learning takes lesser time from few seconds to few hours.
This, in turn, goes reversed during the testing phase. During the test, machine learning takes lesser time to run whereas in deep learning the test increases on the increase of the size of data. Moreover, this isn’t applicable on all machine learning algorithms as few of them take lesser time as well.
Machine learning and deep learning are being applied to various domains like Computer Vision, Information Retrieval, Marketing, Medical Diagnosis, Natural Language Processing, Online Advertising, and more.
One prime example of the company using Machine Learning and Deep Learning is Google.