If you have often questioned to yourself what is the distinction separating machine learning and deep learning, read on to find out a comprehensive comparison in plain layman style.
On the basic note of comparison between deep learning and machine learning, you will find the detailed versions of the concept in this article. Undoubtedly they are covering the future trends and also vary over several points. Once you will flip the coin of artificial intelligence, machine learning, and deep learning will be the two sides appearing to you.
Deep learning and machine learning are of course not two different things but they even don't stand together. Overall both of them act towards empowering the computer programs with the basic feature of adaptability and learning.
They also deal with making accurate decisions and instant predictions. Although the time consumption is incredibly high in deep learning but it only enhances the performance.
1. Know what Machine Learning is
Following from the very beginning we can say that in order to authenticate the artificial intelligence, machine learning was implemented. Numbers of algorithms were used for the same concept like random forests, decision trees, artificial neural networks and many more.
There are generally three types of machine learning algorithms which are:
- Supervised machine learning algorithm - this algorithm works for the pattern of searching among the data points and the value labels that are included under it.
- Unsupervised machine learning algorithm - under this learning there is no involvement of labels with data points occurs and they also deal with the organization of data into the cluster. It also deals with the structure and complexity to keep the data organized.
- Reinforcement machine learning algorithm - in order to formulate any action this algorithm is used and it is also based over the data points. The algorithm, in this case, keeps changing in order to perform better.
- Machine learning also promotes the developmental plans for computer programming and also provides access to learn them. It also deals with the analysis and operation of massive data and delivers faster and accurate results.
In order to process the large volume of data, this is a beneficial method. Most of the leading platforms are using machine learning in order to provide their users with a better online experience. Websites like Amazon, Netflix uses machine learning so that users can get multiple options based on their requirements.
2. Know what Deep Learning is
The concept behind deep learning not only involves solving the real world problems but also it focuses on the idea that lays the fundamentals. This is the reason why deep learning is more accurate and requires more computing. It also refers to the large association of neural networks comprising of multiple features.
Deep learning also promotes high-end solutions without being actually programmed. Deep learning is known by several names as deep structured learning, hierarchical learning or learning through a neural network. The architectures of deep learning are-
- Deep neural networks
- Deep belief networks
- Recurrent neural networks
- Convolutional neural networks
Generally, any of the neural networks of three types of layers which are as
- Input layer
- Output layer
- Hidden layer
Deep learning delivers highly accurate results but it also requires more hardware and time. Deep learning also performs well where the involvement of unstructured data occurs as search blobs of pixels or many more.
The concept behind deep learning works in the way a human learns and adapts. Deep learning entirely depends upon the structure of algorithms which are known as an Artificial Neural Network (ANN). It can also be said that deep learning is the backbone of artificial intelligence.
3. Deep Learning Vs Machine Learning
Machine learning or machine algorithms is typically used to parse the data or to learn from the data. The idea behind this is to make decisions based on learning and deep learning is also used to create an artificial neural network.
This network is created in order to make intelligent decisions on its own and hence deep learning can be said as a subfield for machine learning. There are a number of factors that can plot for the comparison between the two of them
- Dependency over data - one of the major differences between machine learning and deep learning is performance. The drawback with deep learning is when the data is in small quantities then it cannot function well and that is the only possible reason for which deep learning requires a large amount of data to perform accurately.
- Dependency over hardware - machine learning usually requires advanced hardware while deep learning depends upon high-end machines. GPUs also required in the deep learning algorithm which is also an integral part behind its working. An ample amount of operations of matrix multiplication can also be done under this.
- Features engineering - under this segment, all of the domain knowledge is put forward to create the extract in order to simplify the data. Patterns of the algorithm are also more visible which enhances the learning. This process requires a lot of expertise and is time-consuming.
- Execution time - machine learning takes very little time which could be from a few minutes to hours. On the other hand, deep learning can take up to weeks or months.
- Method of interpretability - in machine learning some of the algorithms are very easy to interpret like logistics, decision tree, etc while other algorithms are toughest like SVM, XGBoost. On the other hand, the algorithms of deep learning are beyond impossible to interpret.
The major fundamental behind machine learning is that the entire learning procedures can be carried out without human interaction. Machines can find their own way to evolve and modify things without actually being cared of.
Deep learning is majorly involved in artificial intelligence in which the data is in a surplus amount to be entertained. If enough time is spent over deep learning then it definitely is a fruitful and impressive outcome especially when it comes for the translation and image recognition.
Also, the extraction is carried out over the multiple layers of the network. As for future trends, deep learning is definitely surprising everyone. Demands for deep learning and machine learning are increasing rapidly these days.
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