These days Artificial Intelligence has frenzied the minds of almost every person on the planet. Whether they understand it or not, they are seen spouting about it. The parallel update alongside artificial intelligence boils down to two concepts namely Machine Learning and Deep Learning. However, a large number of audience is still unaware of the terms and willing to know what is machine learning and deep learning. So, here is a quick view of both machine learning vs deep learning
Both the terms are spoken in ways that make them sound like synonymous buzzwords. But to understand the real Learning, it is important to know the difference between them. In this blog post, we will discuss the concepts that master the conversations about Machine Learning and Deep Learning and how exactly are they different. Take a look!
What is Machine Learning?
Machine learning is a subgroup of artificial intelligence that makes use of statistical techniques in order to provide the capacity to “learn” with data to the computers without getting them explicitly programmed. In simple words, Machine Learning can be defined as a science of making computers act and learn like humans and enhance their learning capacity over time in independent fashion by feeding them with information and data in the form of real-world interactions and observations.
Machine learning encourages every kind of automated spans and tasks all around various industries, starting from malware or data security firms to finance professionals who are searching for favorable trades. Let us take an example of a famous music streaming service which has to make decisions about which new artist or song should be recommended to a listener. Machine learning algorithms will assist the listeners’ choices with other listeners who have the same taste. In this case, Machine Learning will work as a virtual assistant who will provide the users with the information about the new tastes and demands in the music industry according to which you can recommend new songs to the listeners. Now comes the question what is deep learning neural networks?
What is Deep Learning?
Here is the short description of deep learning that defines the meaning and working of deep learning. Let’s also understand how Machine Learning is related to Deep Learning.
Deep learning is subset of machine learning which is based on learning data representations, unlike task-specific algorithms. It is inspired by the function and structure of the brain known as artificial neural networks. Deep learning acquires tremendous flexibility and power by learning to display the world as a fixed hierarchy of concepts that are defined in relation to simpler concepts, and more abstract delegations calculated on the basis of less abstract ones. Although the term deep learning has been spoken for years now, these days with all the hype, it is getting more attention.
To understand the concept, take an example of an animal recognizer which helps to recognize whether the given image is of a lion or a deer. When we solve this as a traditional machine learning problem, we will relate to features like if the given animal has ears or not, if it has whiskers or any other organ. In simple words, we will define the facial features to let the system identify the animal. On the other hand, in deep learning, we start with one step ahead. Deep learning automatically defines the features which are crucial for classification. Deep learning will first identify what are the most relevant factors to find out a lion or a deer. Later it will start identifying the combination of shapes and edges to recognize the object more deeply. For example, if the object has ears or if it has whiskers. And after defining a consecutive hierarchical recognition of such concepts, it will then decide which features are responsible for finding the right answer. Let us take a look at the difference between ML and DL and when to use Deep Learning vs Machine Learning?
1. Data Dependencies
The most important difference between the traditional machine learning and deep learning is its performance when the scale of data escalates. Deep learning algorithms don’t operate well when the data is small because they need big data to recognize and understand it perfectly. Whereas, the machine learning algorithms work in this scenario.
2. Hardware Dependencies
Deep learning algorithms are heavily dependent on high-end machines because deep learning includes GPUs, an integral part of its working. As deep learning genetically operates on a massive amount of matrix multiplication, these operations are productively optimized by using a GPU which is specially built for this purpose. On the other hand, the traditional Machine Learning algorithms can work on low-end machines also.
3. Feature Engineering
Feature engineering refers to the process of putting domain knowledge while creating feature extractors in order to reduce data complexity and make patterns visible to learning algorithms so that they can work. The entire process is very expensive and difficult and requires significant time and expertise. In traditional Machine learning, all the applied features are identified by an expert who later hand-codes them as per the data type and domain. For instance, features can be shape, pixel values, textures, orientation, and position. The performance of Machine Learning algorithm depends on the accuracy of the identified and extracted features. On the other hand, deep learning algorithms identify these high-level features from data which, therefore, reduces the effort of developing completely new feature extractor for each and every problem.
Accountability is one of the main factors which is why deep learning is given significant thoughts before applying to the industry. For instance, if we use deep learning for giving an automated scoring to any essays. Although its performance will be quite excellent it will not reveal the reasons for giving that score. You can always mathematically find out about the nodes of a deep neural network that were activated while scoring, but you will never know what these neurons were to model and what they were doing collectively. So deep learning fails us to interpret the results. Whereas, machine learning algorithms give us a set of crisp rules according to which it chose the scores. So it becomes easy to interpret the logic behind it.
Where to Use Machine Learning and Deep Learning?
- Computer Vision: useful for applications like vehicle number, facial recognition, and plate identification
- Information Retrieval: useful for applications like search engines, image search, and text search
- Marketing: useful for applications like target identification and automated email marketing
- Medical Diagnosis: useful for applications like cancer or any other severe disease detection.
- Natural Language Processing: useful for applications like photo tagging, Online Advertising, and sentiment analysis
Which Way Forward?
In this article, we have discussed an overview and comparison between machine learning and deep learning techniques. Although deep learning has enabled a large number of practical applications of Machine Learning by expanding the overall field of data science, there are a number of examples where still machine learning is proven to be an ideal choice. Deep learning is a subset of Machine learning, but it has proven as a larger and drastically modern use in the industry when compared to the traditional machine learning algorithms. It breaks down tasks in such a manner that makes every kind of machine assists possible and even likely. Driverless cars, better movie recommendations, and even, better preventive health care, all are the result of deep learning which has successfully made Artificial Intelligence the present and the future of the world. But in spite of its modernistic applications which are not possible in traditional machine learning, it has proved to be illogical in various places. Therefore before using any technique among them, it is important to be well-versed with the extent of their applications and the logic driven out by their results.