The Deep Neural Network imitates the functioning of the human brain. Find out everything you need to know about it: definition, functioning, use cases, training
A neural network is a set of algorithms inspired by the human brain. The goal of this technology is to simulate the activity of the human brain, and more specifically the recognition of patterns and the transmission of information between the different layers of neural connections.
A Deep Neural Network has one particularity: it is composed of at least two layers. This allows it to process data in a complex way, using advanced mathematical models.
How does a Deep Neural Network work?
In general, a Deep Neural Network has an input layer, an output layer and at least one layer in between. The higher the number of layers, the deeper the network. Each of these layers performs different types of specific sorting and categorization in a process called “feature hierarchy“.
To better understand how a Deep Neural Network works, we only need to observe how the human brain works. Rather than learning the structure of the face to identify people, our brains learn from the deviation of a basic face that serves as a model.
When we see a face, the brain tries to determine how it differs from this reference model. Features such as eyes, ears or eyebrows are thus reviewed in a fraction of a second.
The differences between the perceived face and the “baseline” face model are quantified by an electrical signal of varying strength. All deviations are combined to produce a result.
The individual nodes in the system are similar to neurons in the human brain. Each layer is composed of several nodes. As soon as they are touched by stimuli, a process is triggered.
The neural network interprets the data collected by sensors or directly injected by a programmer. This data can be images, texts or even sounds that will then be converted into numerical values.
The different data between the input layer and the output layer must be processed progressively to solve a task or make a prediction. The first layer of the network receives the data and performs an activation function calculation to produce a result. This may be a probability prediction, for example.
This result is transmitted to the next layer of neurons. The connection between two successive layers is associated with a “weight”. This weight defines the influence of the data on the result produced by the next layer and eventually on the final result.
Deep Learning: learning from deep neural networks
In order to be able to imitate the functioning of the human brain to classify the data provided to it, a neural network must be trained beforehand. This is Deep Learning: a specific form of Machine Learning.
During the training phase, the AI is provided with example data. It learns to recognize patterns and features.
When it receives new, unlabeled data, the artificial neural network tries to categorize it. Its prediction may be correct or incorrect, and it is up to the programmer to correct it if it is wrong. As the system learns from its mistakes, its success rate improves until its predictions become infallible.
What is a Deep Neural Network used for?
One of the main use cases of these advanced neural networks is the processing of unstructured data. Deep neural networks can cluster and classify data stored in a database. This is very useful for organizing data without labels or structure.
A deep neural network is very useful to automate some human work tasks. It is used in the field of video surveillance for facial recognition technology. Autonomous cars also depend on this technology.
The same goes for virtual assistants like Siri and Alexa, or even the recommendation engines on Netflix, Spotify or Amazon. So without even knowing it, you probably use products based on deep neural networks on a daily basis.
Moreover, Deep Learning is making its way into every industry. It is being used in the health field to detect cancer or retinopathy. In the aviation field, it is used to optimize airline fleets.
The oil and gas industry is turning to deep artificial neural networks for predictive maintenance of machines. Banks and financial services, for their part, are adopting it for fraud detection. Little by little, Deep Learning is transforming all sectors of activity.
Deep Neural Networks are the last link in the evolution of artificial intelligence. Originally, Machine Learning was used to automate statistical models through algorithms to make better predictions.
A Machine Learning model is able to make predictions for a single task. It simply learns by modifying its weights with each erroneous prediction to gain accuracy.
Subsequently, artificial neural networks have emerged. These networks use a hidden layer to store and evaluate the impact of each input on the final output. Information about the impact of each input is stored and hidden, as well as associations between the data.
Finally, deep neural networks have been invented. Rather than being satisfied with a single hidden layer, Deep Neural Networks go even further by combining multiple hidden layers for even more benefits.
What are the Deep Learning frameworks?
There are Deep Learning frameworks dedicated to training Deep Neural Networks. Several large companies and startups have launched open source projects to facilitate the training of Deep Neural Networks.
These tools deliver reusable code blocks, allowing the abstraction of Deep Learning logic blocks. They also offer very useful modules for model development.
Among the most popular Deep Learning frameworks, we can mention the open source project TensorFlow from Google, MxNet or the PyTorch library for Python created by Facebook. There are other, more advanced frameworks, like Keras based on TensorFlow or Gluon based on MxNet.
How to learn to work with Deep Neural Networks?
Deep Neural Networks and Deep Learning offer many opportunities for companies in all industries. However, it is a complex technology that requires advanced knowledge and skills.
To learn how to create and use such deep neural networks, you can turn to DataScientest’s Data Scientist training. This program allows you to learn all aspects of being a data scientist.
In the “Deep Learning” module, you will learn about the TensorFlow and Keras frameworks. You will learn all about the different types of neural networks such as CNNs, RNNs and GANs (generative adversarial networks).
At the end of this course, you will have all the skills required to become a Data Scientist and will receive a degree certified by the Sorbonne University. Among our alumni, 90% have found a job immediately after the training.
All of our courses adopt an innovative Blended Learning approach, combining classroom and distance learning. It is possible to achieve this learning in 6 months in Continuing Education, or in 9 weeks in BootCamp. Don’t wait any longer and discover the Data Scientist training.
