We will share some information so that you can get to know more about AI. Artificial Intelligence is composed of many categories; in this article, we will explain the difference between Machine and Deep Learning in detail. First, to understand Machine and Deep Learning, it is necessary to define…
What is an algorithm?
Algorithms are mathematical rules that show the step steps necessary to perform a problem. Through a logical, defined, and finite sequence of instructions, they determine the path to follow to execute a task.
Algorithms have evolved over time to analyze and obtain better results; some examples are decision trees, inductive logic programming (ILP), clustering, Bayesian networks, among others. In this way, both Machine Learning and Deep Learning are algorithmic techniques within Artificial Intelligence to teach systems to learn on their own.
Machine Learning is a branch within Artificial Intelligence designed to emulate human intelligence by learning from the environment. But, specifically, what is Machine Learning?
It is the use of algorithms to organize data, recognize patterns, and make computers learn with those models, creating intelligent insights without the need for pre-programming. Machine learning is divided into 3 categories:
- Supervised: The algorithm is given a data input with labels in this stage. The algorithm only learns the association between the data and its label. For example, if you have a database with images of fruits with their names, by using this training, the algorithm will be able to predict data similar to the training it received.
- Unsupervised: This model involves providing the algorithm with unlabeled data input, and it must learn to categorize the information on its own. This model is complicated, but it may be more convenient since most data is not structured.
- Reinforced: This category does not receive any type of feedback until it achieves some objective, a common example is learning to beat a human in chess, the algorithm will only receive feedback when winning games, in this way, it learns the series of steps it must follow to achieve the goal.
Machine Learning algorithms learn from the entered data. In this way, the machines are trained to learn to perform different tasks autonomously.
A much more advanced method is deep learning, which is a subcategory of machine learning. Deep Learning began with the emergence of new technologies and is inspired by machine learning by mimicking the human brain’s neural network. This model uses several capable neural networks to make predictions and is popular because it is one of the models that makes the most accurate predictions. The objective of Deep Learning is to recognize images, analyze natural language, and predict problems with the extraction of behavioral patterns.