A Layman’s Guide to Deep Learning and its benefits

by Zazz August 30, 2019 Time to Read Blog: 6 minutes

Deep learning is a technology that is based on a network of neural connections, but, you may wonder: How does this connection occur?

The brain is the vital organ of any human being and it is estimated that it contains between about fifty to more than one hundred thousand lodged neurons. Of these neurons, about ten billion correspond to the so-called cortical cells that are responsible for sending signals to the rest of the body through synaptic connections.

After knowing all this information, more and more machines are trying to imitate the functioning of the human brain with the help of a network of artificial neurons, with which the dynamics would be more or less the following:

There are the green neurons that are responsible for the inputs and therefore receive the information sent, while the blue ones are the ones that are hidden and contain the intermediate calculations of the neural network. These neurons are found in layers.

Then, there are the yellow ones that are responsible for the outputs that carry the result of the processed information.

On the other hand, it is normal that there is an input layer, an output layer, and other hidden ones.

This leads to the fact that the more hidden layers, the more complex and intelligent the neural network is and the better the predictions will have, but it is much more complicated to devise such a model.

All these neurons, of course, are connected with a number called Bias, which indicates the importance of the network, but the weight of the neurons is what indicates the relevance of their connection.

The activation is carried out by adding the numbers made in previous operations, this is transformed into a formula and converted into a new number.

However, there are different types of neuron network dynamics, among which we can highlight:

  • The restricted Boltzmann machine (RBM).
  • Recurrent neuron networks (RNN).
  • The deep belief network (DBN).
  • The convolutive neuron networks (CNN).

In any case, this is all about algorithms that can be divided into four main ones: object recognition, voice recognition, and text processing and image recognition.

Is this familiar to you? Well, possibly yes, because Facebook uses it to detect faces in photographs and Google Translate uses it to process texts in another language that you send them.

Here is where we enter fully into the subject that catches us today that is deep learning, so if the subject is of your interest, we ask you to continue with us.

What is deep learning?

Deep learning is a technological model of this new algorithm that we talked about in the introduction, and that is based on the connections made by neurons as they do in the human brain.

All this based on the fact that machines have tried to imitate the best machine in the world: the brain; they have followed a linear learning algorithm, while deep learning becomes increasingly complex. This means a revolution in the world of machines, as the hierarchies of neurons with deep learning are increasingly complex. In a nutshell, with deep learning, it is intended to learn after examples, which is natural for the human brain.

However, with deep learning, a computer model manages to learn to carry out direct classification activities after viewing images, text or sound. These models can achieve precision in the results that sometimes even exceeds the performance of the human brain. So, in synthesis, these models carry out training through a wide set of labeled and dynamic data of neural networks that contain different layers, as we explained previously. But how does this whole thing benefit?

How does deep learning benefit?

The main benefit of deep learning is the precision with which it operates, from what we mentioned before, it is incredible that it surpasses even humans. With such precision, the results obtained are impressive, so that satisfaction and going beyond the expectations of users are completely covered.

This is positive in the case of minimizing some tasks and helping in critical applications for user safety. On the other hand, we leave you two great reasons to think that deep learning is reliable and important for this new technological era:

  • Deep learning requires large numbers of tagged data, so, for example, to develop a car without a driver, millions of images and more than thousands of hours of audiovisual material are required.
  • A relevant computing power is necessary, and in this way, the high-performance GPUs have a parallel dynamic that is positive for deep learning. By combining this with clusters or with cloud computing, you can reduce the amount of time for training a deep learning network.

Differences between deep learning and machine learning

Many people can confuse deep learning with automatic learning, but the truth is that they are not the same and respond to different characteristics and operations that we will see next.

  • While in machine learning aspects are selected manually with a classifier to classify them, in deep learning these processes are automatic.
  • Within a machine learning workflow it starts with the manual extraction of the most relevant aspects of the images but, with a deep learning workflow, these relevant aspects are extracted directly from these images.
  • Deep learning is about complete learning, that is, the data is unprocessed and the machine must learn by itself or automatically.
  • Deep learning algorithms increase in scale with the data, but for the superficial, there must be a convergence. The latter indicates that machine learning methods may reach a stalemate by having some performance, such as adding more training data to the neural network.

How deep learning works

All machines that carry algorithms work with neural networks, only that deep learning goes further and works with “deep neural networks”. This is due to the amount of hidden or hidden layers with which this technological model operates.

This amount has been estimated at more than one hundred and fifty layers, and there is its complexity in operation. The way to operate deep learning is to train models through data sets that are labeled and a dynamic in the neural network in which they learn from the data without the manual requirement to achieve it.

On the other hand, one of the most popular types of deep neural networks are ones called convolutional neural networks (CNN or ConvNet). It’s way of operating is that a CNN convolves the aspects learned with the input information and uses 2D convolutional layers, which causes this dynamic to be conducive to process 2D data as images.

For their part, these CNNs suppress the need for a feature extraction manually, so the identification of the aspects used in the classification of 2D material is required. Therefore, CNN extracts the most relevant features directly from the images. These features have not been trained before, as they are learned while the network trains with a collection of images.

This makes the deep learning model as precise or specific for missions as artificial vision, as is the classification of things.

How to train the models?

To classify objects, training of deep learning models is necessary, and to achieve this there are three ways to do it, and we will detail them below:

Right from the start

Although less common, it is a positive way of training in the sense that it works for new applications or for those that have a large number of output categories. What is necessary for this is to collect many labeled data while establishing a neural network dynamics in which you learn the most relevant characteristics.

Learning transfer

Its great benefit is that less data is recorded than in the previous case and the time is reduced from hours to minutes. It is an adjustment to a model that had already been trained, that is, what has been learned is transferred.

Therefore, the model is given new data that includes classes that would previously be unknown. So the transfer of learning needs an interface with the internal factors of the previously existing network so that it is possible to adjust and improve very specifically and following the new assignment.

Aspect Extraction

This process is also unusual, but it consists in extracting from the network the characteristics already learned by the model and by the layers of neurons at any time during the training process. Then, it is possible to use these aspects or features as input for a machine learning model, such as support vector machines (SVM).

Examples of deep learning

To give you a clearer idea, we leave you the sectors in which deep learning models are used, which will lead you to imagine the magnitude of their benefits.

Defense sector

With the support of deep learning objects can be identified on satellites as well as safe or unsafe areas are detected in favor of the troops.


Deep learning applications can detect cancer cells automatically. With the help of a microscope, multiple multi-dimensional data are produced that can identify these cells and be developed by UCLA.

Autonomous driving

The automotive sector has also benefited from deep learning, because it is possible to automatically detect traffic lights, stop signs and even know if a pedestrian is passing to avoid traffic accidents.

Industrial automation

In the case of the industry sector, it is possible to work with heavy machinery and detect if there are people or objects nearby or areas that are not safe.


Most people will recognize this example on devices that translate speech or that users ask something and questions are answered.


Years, even decades ago, developers and experts hoped to one day be able to imitate the human brain with their machines almost perfectly.

But there were limits to technological advances, there were no appropriate tools to be able to realize the full potential of Artificial Intelligence. But the advances in technology have been remarkable, and this has given rise to researchers to mimic the neural processes to develop the machines.

Every day we live with deep learning, and this is only intended to respond to human needs and solve everyday problems. It is a matter of time before we see robots in every other coffee shops serving us, but do you feel prepared for this? If not, feel free to contact the Appstudio team. We will not only realize your project but will also guide you regarding the opportunities you have according to your business needs.


Zazz has since been committed to facilitating services that go beyond excellence. We create apps to meet the needs of an evolving digital landscape. We are a leading mobile app development company . We design & develop web & mobile apps that drive today's businesses. Backed by research and development, Zazz uses technology, software, mobile, and customer service to create new revenue-generating opportunities. So contact Zazz to see your dream idea as the next big thing. We Offer Top Services iOS App Development , Android Application Development Services, React development , Flutter development , IoT Development , AR / VR development , Digital Marketing Services - SEO , SMO , PPC , Ecommerce App Development, cloud app development, front end development, education app development services, Mean Stack Web Development, Content Marketing, etc.

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