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13 things to know about neural networks

13 things to know about neural networks

Neural networks could open doors to endless possibilities on how we solve problems in our daily lives. These types of networks are designed having in mind how our brain works and operates as a computing system with interconnected nodes just like our neurons. Based on certain data, neural networks can recognize patterns, correlate and improve the received information, leading to continuous innovation. 

We described below 13 things you should know about neural networks and how they can transform in better our lives: 

1. Neural networks are not Machine Learning 

Neural networks are inspired by the structure of our brain, having many highly interconnected entities, while Machine Learning is a set of algorithms that parses data and learns from it in order to discover patterns of interest. Neural networks use one set of these algorithms, used in Machine Learning, but are able through deep learning models to determine on their own whether their predictions are accurate or not. 

2. Neural networks solve complex problems

Complex problems, with various data sets, could be hard to solve by simple Machine Learning systems. This is when neural networks come into the discussion through understanding many different variables and being able easily to make sense out of them. One example could be the use of neural networks in the recognition of health problems through identifying speech patterns. 

3. Neural networks can figure out the correct features

Based on their deep learning capabilities, neural networks are able to figure out the correct features to ascribe to a problem. This type of ability is called feature engineering and permits neural networks to create new categories for consideration and apply them afterward to their work.

4. Neural networks are flexible

The core function of neural networks is to learn and understand patterns, so if they are capable of it in a certain domain, they could also apply this to any other domain. This gives neural networks a high power of flexibility, enabling us to use them in very different domains, from healthcare to air traffic, even sports or finances. 

5. Neural networks need a learning period

Neural networks require a training period similar to the development of a child’s mind. In this period they learn how to use data and how to adapt it according to the changing input. Neural networks usually evolve through three learning paradigms: supervised learning, reinforcement learning, and unsupervised learning. 

6. Neural networks are enabling deep learning

Inspired by our biological neural networks, these kinds of systems are made to adapt themselves according to the need. Compared to other types of learning systems (task-based algorithms), neural networks are enabling deep learning and can create their own unique set of features to be used in the identification of certain problems or solutions. This process enables neural networks to work with incomplete data once trained. 

7. Neural networks are evolving 

Training allows neural networks to reach new levels of complexity. We can scale up or down its capabilities, and easily imagine a near future in which neural networks will be able to draw reliable solutions without needing many pattern examples, and in a shorter period of time.

8. Neural networks bring new opportunities

Wider acceptance and wider availability could make neural networks to transform many areas of our lives. Neural networks are not expanding just vertically, bringing new complex structures, but also could expand horizontally, improving various industries through bringing new opportunities and perspectives.

9. Neural networks have long-term potential

Neural networks are here to stay, but more to evolve with us. Therefore, these systems hold long-term potentials, and future research could create many more ways in which we could use them.

10. Neural networks are still expensive 

The challenge of cost-effective neural networks represents a real struggle for researchers in the domain of Artificial Intelligence. On the one hand, it is well known that at least for now, large neural networks are able to provide better results than smaller ones, but on the other hand, these large learning models can come at an enormous cost. Wider availability and research could improve this aspect, as more and more businesses start to consider neural networks in their work. 

11. Neural networks can be integrated into (almost) any work 

The use of neural networks can be integrated into very different fields and aspects of our lives. As these networks are inspired by our biological neural networks, they can help us out in solving problems without being previously programmed with the problem-specific rules and conditions.

12. Neural networks have real-life applications 

The real-life applications of neural networks demonstrated their importance and adaptability to various situations that require deep problem-solving. Life sciences use it to predict diagnostics and health monitoring; manufacturing companies use neural networks to optimize supply chains, while in public services we can see neural networks being used to support the development of smart cities. 

13. Neural networks can be the future of work 

Neural networks are changing how we interact with systems or how we attempt to solve problems in our daily lives. The adaptiveness of neural networks and their ability to discover anomalies is transforming the way we work and could provide new paths to follow in reaching strategies for improving various industries and lines of business.

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