the current world of commerce, it is not unlikely to come across these two terms; what is deep learning and machine learning. Despite that, the terms are somehow challenging to differentiate from the majority of the population. Although used interchangeably quite often, they are very distinct from each other. Both times are connected to artificial intelligence, which is the concept that involves coming up with intelligent machines which are self-reliant with the ability to act like human beings. Thus machine learning is considered a subset of artificial intelligence and deep learning a subset of machine learning to deep learning.
That is a computer science discipline that uses analytics and algorithms able to learn just from data with no reliance on explicit programming. The computers are given both structured and unstructured data enabling them to learn to evaluate and act on data while becoming better. It accesses past data, learns from it using multiple techniques and algorithms, and predicts what will happen. Machine learning and deep learning algorithms are classified into three categories: supervised learning, unsupervised learning, and reinforcement learning.
- Supervised learning
Involves data that has been labelled already. Systems can predict outcomes of the future using past data. The model requires being fed at least an input variable and output variable to be able to learn. Examples include; algorithm regression, linear regression, decision tree and naïve Bayes.
- Unsupervised learning
The algorithm employs unlabeled data. The system can find patterns and associations independently. It can find and identify hidden features from the provided input data. Examples include; anomaly detection, k-means clustering and hierarchical clustering.
- Reinforcement learning
This type of learning involves reinforcement that helps the systems master complex operations coming with highly flexible, unpredictable and large databases. It is conditioned to receive observations and get rewarded in an uncertain environment. The reward is a measure of success. Examples include; deep Q–learning and Q- learning.
A subset of machine learning dealing with algorithms modelled specifically to function like the human brain, with a deep artificial neural matrix multi-layered and complex structures. It imitates ways that human beings obtain specific knowledge. It can also be defined as a way to make predictive analysis automatic. Unlike linear machine algorithms, deep learning is the subset of machine learning and algorithms are assembled in a hierarchy of increasing complexity.
How it works
There are two ways to get a program to perform what they want, first, by a programmed approach that is specifically guided, instructing it what to do. Secondly, one can use neural networks whereby; one introduces their network the inputs and what is required from the outputs and then allows it to learn independently. Deep learning is currently used in most image recognition, speech recognition, and language processing software tools.
Fields where deep learning is used currently
- Text generation
- Improving customer experience
- Industrial automation
- Medical research
- Color addition
- Enhancement of computer vision
- Detection and identification of objects in military and aerospace
The main difference between machine learning and deep learning is in the presentation of data. Whereas machine learning algorithms require structured data, deep learning utilises layers of artificial neural networks.