Last Updated on August 22, 2021
Machine learning and deep learning both are subsets of Artificial intelligence. But we often use these terms interchangeably. And Machine learning is the biggest part of Artificial intelligence. All the AI-based products or services in the market are not possible without machine learning or deep learning.
Perhaps both technologies were introduced a few decades ago. But now, for the last few years, people are using its applications tremendously. Nick Bostrom said about AI that it may be the last invention humans ever need to make. In this article, we will look for:- machine learning vs deep learning.
Before we know the difference between machine learning and deep learning. First, we need to understand what they are.
What is Machine Learning?
Machine learning is the subset of artificial intelligence. It gives the system the ability to learn and improve itself through the experience without being programmed.
It uses statistics to find the pattern in a large amount of data and data can be a lot of things like numbers, images or words, or anything you have.
If it can be digitally stored, it can be fed into a machine learning algorithm. To train the system, you need a large amount of data. If you think that you will train the system with 100-200 data(such as images), it will give the output but with very low accuracy. You need around 10000-15000 data to train. It depends on what you are training them for. Kaggle is the best platform from where you can get the dataset.
There are four types of machine learning;- Supervised learning, Unsupervised learning, Semi-supervised learning, and reinforcement learning.
In brief, in supervised learning, input data has been defined(labeled) and you know what your output should be. In unsupervised learning, input data is not defined(unlabeled). And you are not sure what your output will be.
If you want to know full details regarding supervised and unsupervised with examples, click below:-
How does Machine Learning Work?
Suppose we have to train a model that can identify the given data(image) as a cat or dog. We will use the labeled(defined) input. We took thousands of images of cats and dogs each.
After that, we will do feature extraction. It means we will have to extract the features(such as color, eye, nose, ear, etc.) from raw input that defines or can differentiate between cat and dog.
Choosing the right data(features) will make the model successful or fail. Then we select an appropriate algorithm based on what type of problem it is and apply it to train the model. At last, when training is done, the model will predict the given input, which is a cat or dog.
Does Machine Learning Require Coding?
Yes, a programming language is necessary for machine learning. First, understand machine learning involves algorithms. And mathematics is mandatory to learn the concepts of algorithms.
But when you implement machine learning to solve real-world problems, you do need coding. Python and R is the preferred programming language in Artificial intelligence and data science field.
What is Deep Learning?
Deep learning is the subset of machine learning. that mimics the behavior of the human brain, learning things using ANN(artificial neural network). The artificial neural network is divided into three types:- i)Input layer ii)Hidden layer iii)Output layer.
Deep learning is said to be deep because of its hidden layer. Before deep learning, in the neural network, there were only two layers that are difficult to solve a complex problem. In deep learning, there are multiple hidden layers that constitute a larger network. It has the potential to solve complex problems.
How does Deep Learning Work?
In deep learning, the whole learning process is done by a neural network. Consider the same example we took in machine learning. when we input raw data(such as image, text, etc.). It will be fed directly to the neural network.
There is no need for manual feature extraction. The neural network will do this all by itself. Each input gets multiplied by its weight and flows to the next neural layer. The weight here signifies the importance of that input data to the output.
If the weight is not proper, it performs backpropagation where it goes back and updates all the required weight to give the appropriate output. The neural network performs all this operation by itself. That’s why it took up to weeks and lots of data to complete the whole process.
Difference Between Machine Learning and Deep Learning
|Machine learning||Deep learning|
|Data requirement(quantity)||require thousands of data point to train||require larger dataset for training|
|Hardware requirement||can run on CPU||require GPU to train the model|
|Execution time||takes few minutes to hour||up to weeks|
|human intervention||require minimal intervention of human||no requirement of human|
|Output||Output is in numerical value like a classification of score||It is in any form like from numerical to free form elements such as text or sound|
Is Deep Learning Supervised Learning?
Well, deep learning can be supervised or unsupervised. If input data is labeled(defined), it will be supervised. For example, image classification, face recognition, etc. Or if the data is unlabeled(undefined), it will be unsupervised. For example, word embedding, image encoding into a lower or high dimension, etc.
Which is better?- Machine Learning vs Deep Learning
As you see above in the table, there are pros and cons of both- machine learning and deep learning. One thing that needs to be considered in both i.e. human intervention.
Machine learning require human intervention because of feature extraction.
In machine learning, we input some raw data(like .csv, images, text, etc.) but the machine learning algorithm can not be applied to it. First, it requires preprocessing steps called feature extraction.
Feature extraction is the process that identifies important features and attributes and extracts them to further process. And after that machine learning algorithms can be applied to them.
Let’s take an example, we have to train a model to recognize that the given object is a car or not.
First, we need to identify the feature of a car(such as color, size, wheels, shape, etc.) and extracts them to feed the algorithm as input data. This process is called feature extraction.
In deep learning, we humans don’t need to do feature extraction. The model would itself recognize those features and extracts them. And then make predictions without human intervention. That’s why deep learning takes more time as compared to machine learning.
So, if you have a dataset that is not large, it is worthless to use deep learning. Here you should go for machine learning. Or if you don’t know how to use feature extraction in a specific domain, then deep learning would be preferable so you don’t need to worry about it.
I hope you like the article.
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