MACHINE LEARNING vs. DEEP LEARNING
Have you ever thought about how Facebook knows whose face is in a photo or which Netflix show you will love to watch next? It is overwhelming to see the latest advancement in artificial intelligence and its relevant domains, such as machine learning and deep learning. Machine learning and deep learning are gaining popularity, and all of a sudden, everyone is talking about these streams. In these years, people are getting benefitted from apps containing concepts like music recommender algorithms, Google maps, product purchasing recommendations, etc., and many more applications that are powered by these machine learning and deep learning algorithms.
Many people are not familiar, machine learning, which is a part of artificial intelligence, was originated in the year 1950. Mr. Arthur Samuel programmed the first computer program with learning capability in 1959, where an IBM computer became better at playing checkers more and more it played. Since then, a new division of AI was growing slowly and boiled down to what you can witness as ML and Deep Learning. People these days talk about these two terminologies irrespective of whether they can differentiate between Machine Learning and Deep Learning. So, it is essential to know Machine Learning vs. Deep Learning. Many aspirants have this one confusion, is deep learning and machine learning the same? In this article, you will learn about what is machine learning and deep learning and what is the difference between machine learning and deep learning.
What is Machine Learning?
Machine learning is a sub-domain of artificial intelligence that provides machines and other systems the ability to automatically learn and improve their functioning from experience without being explicitly programmed. With Machine Learning, computer systems gain the ability to learn from the data fed to the machine as input. This way, the system does not require reprogramming. In other words, with the use of machine learning algorithms, the computer can continuously enhance their performance on different tasks without human intervention. These advanced, sophisticated algorithms (such as neural networks) parse those input data, learn from those data, and then reapply it by recognizing what to understand from it for making informed decisions.
The wide use of machine learning ranges from on-demand music streaming with the recommendation to the shortest path in cab-apps to playing games against a human with machine intelligence. Various domains and industries like science, arts, finance, healthcare, entertainment use machine learning. There are several methods through which machines can learn. The training can be as simple as making a primary decision tree or a more complex algorithm containing recurring layers of artificial neural networks. There are two popular methods used today Machine learning with R and machine learning with Python to design and write such algorithms.
What is Deep Learning?
There is one common question in the mind of aspirants, is the deep learning part of machine learning? Deep Learning is the sub-domain of Machine Learning that focuses on designing algorithms for mimicking the structures and functioning of human brains called artificial neural networks. Using these deep learning, machine learning algorithms, we can intend machines to perform tasks at human intelligence. Deep learning is a combination of other domains like neural networks, machine learning, and algorithms that can learn from enormous amounts of data. We call it “Deep” Learning because there are various deep layers of neural networks applied in it to enable a machine to learn. We produce a staggering amount of data every day, and these enormous acts as fuel to make deep learning possible.
In recent years, the capability of deep learning has grown to a tremendous level, and every industry is leveraging its potential. AI as a Service also pushed small-scale businesses to access and utilize artificial intelligence technology and algorithms using deep-learning models to perform various tasks without a large investment. Using deep learning algorithms, we can design and allow machines to solve large and complex problems even when the dataset is diverse, interconnected, or unstructured. The more a machine can learn from these datasets, the better it gets trained and performs.
Now let us familiarize ourselves with what is the difference between deep learning and machine learning in the next section.
What is Machine Learning vs. Deep Learning?
Now, since you have understood what deep learning and machine learning are, it is time to understand the various Deep Learning and Machine Learning differences. The aspirants who opt for data science or artificial intelligence as their career path must understand what Deep Learning vs. Machine Learning difference is? In this section, you will learn about Machine Learning vs. Deep Learning examples.
· Human Intervention: In the case of machine learning systems, machines need slight human intervention to identify and hand-code to apply its feature on various datasets. This human intervention is not essentially required in deep learning algorithms as the models can learn those features through experience and repetitive approach with additional data as we humans do. The volume of data involved in training such a deep learning algorithm is enormous. Hence, with the time and iteration on that data model, deep learning algorithms can grow more neural networks and train them accordingly.
· Algorithm complexity: Since Machine learning algorithms require listed data, they are not the ultimate solutions for solving complex queries. That is where deep learning algorithms come into action that utilizes a tremendous amount of data to learn which experience is valid and which is critical.
· Hardware usage: Due to large and complex mathematical calculations, deep learning systems demand much more robust and powerful hardware than implementing machine learning algorithms. It uses a high-powered Graphics Processing Unit (GPU), which are specialized electronic circuits designed to manipulate and accelerate memory to work faster on extensive data. On the other hand, machine learning algorithms comparatively require less-powerful hardware and can run on lower-end systems.
· Time: As we all know, the more data we have, the more time a system will take to compile for a solution. Due to the tremendous amount of dataset involving complex mathematical formulae used in Deep Learning system, deep learning systems takes a lot of time (fro few hours to a few weeks) to train its algorithm for precision. Machine learning algorithms, on the other hand, are lightweight and takes a few hours.
· Approach: Both machine and deep learning are subsets of AI and are connected to data to make a representation of a specific form of intelligence. Machine learning algorithms tend to parse data in chunks, analyze them, and then combine those parts to come up with a solution.
Let us now understand the difference between machine learning and deep learning with an example. If your machine learning algorithm wants to identify a particular object from a set of image data, the algorithm will perform object detection. Here, you have to put similar images to make the algorithm recognize that object. Once it understands which object to detect, it will perform object recognition from the image. On the other hand, deep learning algorithms will take input of those images, analyze them within themselves, and with training, yield both the identified object along with their location.
· Algorithms used: Apart from understanding all the extreme learning machine vs. Deep Learning differences, it is also essential for aspirants and professionals to know the different algorithms used in machine learning and deep learning. Some machine learning algorithms are Linear Regression, Decision Tree, kNN, K-Means, SVM, Logistic Regression, Naive Bayes, etc. Some deep learning algorithms are Convolutional Neural Network, Multilayer Perceptron Neural Network, Recurrent Neural Network, Backpropagation, Generative Adversarial Network, etc.
Conclusion
Due to the technological growth in every industrial sector, companies are looking for the best ways to find solutions. That is why they are hiring professionals who have sound knowledge of machine learning and deep learning. There is more to discover regarding the potential of machine learning and deep learning. If you want to be a part of this cutting-edge machine and deep learning technology, join the Machine Learning and Deep Learning courses of Jigsaw academy.
