Now we can see that with a huge amount of computing power, machines can now recognize objects and translate speech in real time, with this we can say that artificial intelligence is improving and becoming smarter. The adaptations of machines and software to learn in a very real way to recognize patterns in the digital representation of sounds, images or any type of data. when machine learning includes many techniques, deep learning stands out as very important. There are many definitions of deep learning, my favorite is Analytics Vidhya's formal definition of deep learning is defined as “Deep learning is a particular type of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts , with each concept defined in relation to simpler concepts and more abstract representations computed in terms of less abstract concepts “Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay Deep learning is part of a machine learning family, let's see how deep learning came into being and how it became more popular. As we know, machine learning uses algorithms to analyze data, store it and learn from it, and then decide or predict what we are feeding the data into. It is very complex to manually code software with a specific set of instructions to execute. a task in machine learning, then the machine is trained using large amounts of data and algorithms that give it the ability to learn on its own how to perform the task. The human brain contains billions of neurons that communicate with each other to share information. With the same idea, artificial neurons are created so that the machine can think and act like our brain. Using neurons, neural networks were created, the logic was to replicate the human brain in a machine. The representation of the biological neuron is given in the following figure: Machine learning directly rooted from the minds that dreamed and gave birth to artificial intelligence. The logic was to replicate the functionality of the human brain in the machine, thus the concepts of neurons were born. Artificial neurons are represented as shown below: Algorithmic approaches over the years have included decision tree learning, inductive logic programming being the initial one. And there are algorithms like clustering, reinforcement learning, and Bayesian networks. These algorithms have failed to achieve the goal of general AI. As we go deeper, we know that one of the most suitable application areas for machine learning for many years has been computer vision, and although it still required a large amount of manual coding to accomplish the task. Somewhere even precision was missing in the example of reading and object recognition during a sunny day and a foggy day. These layers learn through models called "neural networks", which are structured in layers one after the other. Deep learning is called artificial neural network (ANN), which is a network trained after biological neural networks that can be used to approximate functions that have a huge number of rarely known inputs. Simple neural networks lacked the precision, to make the system more robust and stronger in the aspect of human brain came to the multiple hidden layered networks called deep learning, which is an excellent till proven technique to implement. 6848-6856).