Neural Networks and Machine Learning

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have become so deeply embedded in our daily lives in such a short time that we use these technologies without thinking about their meaning. But how are neural networks and machine learning related?

For most people, AI, ML, and DL are the same or very similar concepts. However, despite the interconnection of these technologies, they have significant differences.

In this article, we will try to shed light on one such source of confusion: neural networks and machine learning. After briefly learning about each of these concepts, we will find out how they differ from each other and how they relate.

This is certainly an interesting field that can be appealing for learning more and becoming a specialized professional after undergoing specific training in machine learning and artificial intelligence.

What is Machine Learning?

Machine learning, or automatic learning, is a part of the broader field of artificial intelligence. Machine learning aims to create intelligent systems or machines that can learn from experience without being explicitly programmed to do so and without requiring human intervention.

Consequently, this field is constantly evolving. Machine learning aims to understand data structures (which are then fed into a human-programmed algorithm) in order to transform them into models that can be used by companies and organizations for various purposes.

Machine learning involves two main methods for AI systems: supervised learning and unsupervised learning.

The difference between these two systems is that in supervised learning, algorithms have a set of objective variables with specific values that are used to train the model. On the other hand, in unsupervised models, machines learn and classify variables automatically to make predictions.

What is a Neural Network?

Artificial neural networks are inspired by the structure of the human brain. Essentially, a neural network is a model for machine learning, but it is a much more complex system used only in deep learning, not in machine learning, because for the latter both algorithms and systems are simpler, and machines do not learn on their own but only based on models implemented by humans.

Thus, it can be concluded that neural networks and machine learning are not the same, although these two concepts are related and can be included in the broader category of artificial intelligence.

A neural network is a network of interconnected entities called nodes, where each node is responsible for a single simple computation.

Thus, a neural network works similarly to neurons in the human brain: connections are established between nodes, information is transmitted, and based on the initially input data, certain conclusions and predictions are made.

Machine Learning and Neural Networks: Key Differences

Since machine learning models are adaptive, they constantly evolve by learning from new experiences and sample data. Therefore, they are able to identify patterns in the data that comes to them, which represents a single input layer. However, when it comes to neural networks, even a simple model consists of multiple layers.

This is just one of the differences between these two models. Now we will look at other fundamental differences between neural networks and machine learning:

• Machine learning uses advanced algorithms to analyze data, learn from it, and detect meaningful patterns of interest. Whereas a neural network uses a wide range of algorithms to model data and make predictions.
• If a machine learning model makes decisions based on what it has learned from data, then a neural network organizes algorithms in such a way that it can make accurate decisions on its own.
• Neural networks do not require human intervention because their embedded layers pass data through nodes and make their own conclusions. In the long run, this enables them to learn from their own mistakes.

As we already mentioned, machine learning models can be divided into two types: supervised and unsupervised; on the other hand, neural networks can be divided into feedforward, recurrent, convolutional, and modular neural networks.

They differ not only in their method of learning or data transmission, but also in their classification method, and, as we will now see, in their operating principles and practical applications.

Performance Comparison of Neural Networks and Machine Learning

A machine learning model works on a simple scheme: it learns from the data fed to it. In contrast, the structure of a neural network is quite complex. In it, data passes through several layers of interconnected nodes. Each node sorts features and information received from the previous layer before results proceed to subsequent layers.

In neural networks, the first layer is the input layer, followed by a hidden layer and finally an output layer. Each layer contains one or more neurons. The more layers and the more artificial neurons the system has, the more effectively it will measure and produce results.

Application Areas of Neural Networks and Machine Learning

Machine learning is applied in areas such as:

• Medicine
• Retail
• E-commerce (recommendation systems)
• Self-driving cars
• Online video streaming
• Internet of Things
• Transportation and logistics

Neural networks, on the other hand, are used for solving numerous business problems, including:

• Sales forecasting
• Data validation
• Customer research
• Risk management
• Speech recognition
• Character recognition

Skills for Neural Networks and Machine Learning

Before delving into neural networks and machine learning, it is necessary to consider a number of points, as well as the skills and knowledge required for each of these fields.

Skills required for machine learning include:

• Programming
• Probability and statistics
• Big data
• Knowledge of ML frameworks
• Data structures
• Algorithms

For neural networks, it is ideal to have skills such as:

• Data modeling
• Mathematics
• Linear algebra
• Programming
• Probability and statistics