21 Jun MACHINE LEARNING vs DEEP LEARNING
The terms Artificial Intelligence, Machine Learning and Deep Learning are often considered as the same. However, these fields differ from each other in many ways of conduct and provide different results when applied in practical fields. You can clearly differentiate between the machine learning and deep learning by analyzing the functioning characteristics.
Artificial intelligence is a vast concept than machine learning, related to the use of computers to simulate human cognitive functions. When machines perform tasks based on algorithms, in a smart way, that is considered as Artificial Intelligence.
Machine learning is a subset of Artificial Intelligence and mainly works on the ability of the systems to get a set of data and learn about the system processing by changing algorithms because machines learn more about the information they process.
In general, Machine Learning is divided into three different categories, which are:
- Enhanced Learning
Enhanced learning is useful for applications such as autonomous vehicles, where the system can receive feedback about the performance of the vehicles and use this data over the time for improvement. An example of strengthening learning, is system giving instructions on how to play games and improving its performance.
- Supervised Learning
Supervised learning is mainly useful for the classification of applications, such as system differentiating between pictures of girls and pictures of boys. This method is believed to be “monitored” because it requires human assistance to tag images and supervise the process of system learning.
- Unsupervised Learning
In contrast, unsupervised learning is not based on human assistance to label system training data. Instead, the computer uses algorithms or other mathematical methods to detect similarities between data groups.
CHARACTERISTICS OF MACHINE LEARNING
- In programming, the program developer commands the computer exactly about the instructions to be follows. Provides the set of input commands and the system will return a number of results, as per the instructions given by the programmers.
Machine learning is different because the programmer doesn’t determine exactly what to do. Instead, they feed the data in the systems and allow it to learn from the repeated processing.
- The main idea of machine learning is that the computer can be trained to perform the tasks that are difficult and time taking to be done by human beings. The clear violation of traditional analysis is, that machine learning can make decisions with minimum human interaction.
Deep Learning is considered as a subset of Machine Learning. The concept of deep learning is also referred to as “deep neural networks”, which refers to the many layers of data. The neural network can also have only one layer of data, but the deep neural network has usually more than one layers of data.
OF NEURAL NETWORK
Deep Learning works on the principal of neural network which has some advantages that are listed below:
- Takes out the meaningful data from the complicated algorithms.
- Identify the updates and detects the algorithm patterns which are too complicated for the human brain to notice.
- Neural Networks learn by repeating the process.
- Speeds up the processing.
CHARACTERISTICS OF DEEP LEARNING
- In practice, deep learning is just a subset of machine learning. It is technically machine learning and works in the same way but its possibilities of results are different.
- A deep learning system is built to continuously do the analysis of the data with a logical structure resembling to how a person would take out the conclusions. Deep Learning design is based on the human brain’s biological neural network.
DIFFERENTIATION BETWEEN MACHINE LEARNING AND DEEP LEARNING.
The following table will help you to have a better understanding about the differences between Machine Learning and Deep Learning.
|FEATURES||MACHINE LEARNING||DEEP LEARNING|
|Dependence of Data||Machine Learning is applied mainly to work on a small/medium data-set||Deep Learning is applied mainly to work on a big/large data-set.|
|Dependence of Hardware||Can be installed on low-end machines.||Requires powerful machines to be processed.|
|Human Assistance||The software engineers need to understand the patterns and features of the data.||No human interaction is needed to understand the features of presented data.|
|Processing Time||Takes few minutes to hours for the execution.||Takes up to weeks for the execution.|
|Interpretability||Algorithms are easy to interpret in Machine Learning as compared to Deep Learning||Algorithms of Deep Learning are very difficult and almost impossible to interpret.|
|Training of dataset||Data-sets of Machine Learning require a small amount of training.||Data-sets of Deep Learning require a large amount of training.|
|Choose features||Machine Learning is enable to choose the features.||Deep Learning is not able to choose the features.|
|Algorithms||Machine Learning requires a huge set of algorithms for processing||Deep Learning requires only a few set of algorithms for processing.|
|Time for Training||Time of training a Machine Learning based systems is short.||The time period of training Deep Learning based systems is Longer|
|Functioning||Machine learning uses algorithms to analyze data, learn from the data provided and make informed decisions based on what it has learned||Algorithms of deep learning structures are created in layers to process an artificial neural network that can learn and make decisions wisely on its own.|
ROLE OF MACHINE LEARNING AND DEEP LEARNING IN CUSTOMER SERVICE
Most of the modern Artificial Intelligence applications in customer service use machine learning and deep learning algorithms, mainly to increase agent productivity, manage self-service, and make workflow charts more reliable. The data entered in these data-sets comes from a continuous flow of customer requests, which turn the customer leads to rapid and accurate forecasts. Artificial Intelligence is an important aspect of the business world and it is predicted by the industry analysts that the most practical business related Artificial Intelligence applications will be in the field of customer service.
We look forward to see more innovative applications for deep learning in the near future, as deep learning is developing and getting refined rapidly. Expectations are that the machines will deliver even more personalized and better support to customer service sector.
The easiest way to understand the difference between machine learning and deep learning is to know that all deep learning is included in machine learning but all the machine learning algorithms are not considered as deep learning.