What are Neural Networks?

Neural Networks: The Brain of Artificial Intelligence

Neural Networks are one of the main building blocks of artificial intelligence and machine learning. Inspired by the functioning of the human brain, these structures are used to solve complex problems and process data. Especially in today's artificial intelligence revolution, neural networks form the basis of deep learning, generative AI and many other models. In this article, we will look for answers to the questions of what neural networks are, how they work and in which areas they are used.

Neural networks are a structure inspired by the nerve cells in the human brain. In the human brain, billions of neurons interact with each other through synapses and transmit information. Similarly, an artificial neural network processes information through a series of “neurons” (artificial neurons) or “layers”.

These networks receive data inputs, process that data and eventually produce an output. Neural networks produce results based on inputs by optimizing weights and coupling strengths during the learning process. These networks are capable of learning complex relationships and patterns, especially when trained with large data sets.

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How Neural Networks Work

Neural networks are organized in layers. These layers are analyzed under three main titles:

1. Input Layer: This layer receives the data entering the model. For example, in an image classification problem, the pixel values of the image are received by this layer.
2. Hidden Layers: The data received from the input layer passes through one or more hidden layers. These layers process the data and extract the features that the model needs to learn. In deeper neural networks (e.g. Deep Neural Networks), there are many hidden layers.
3. Output Layer: The information from the hidden layers is processed in the output layer, where the final decision is made. For example, an image recognition system determines whether an image is a “cat” or a “dog” at the output layer.

Neural networks learn by backpropagation. That is, the errors produced by the model are fed back to the weights and these weights are updated. Thus, the model learns to produce more accurate results over time.

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Types of Neural Networks

According to different problem types, neural networks can take different structures. Here are the most common types of neural networks:

1. Feedforward Neural Networks (FFNN)

It is the most basic neural network structure. Data travels from the input layer to the output layer and does not return. This type of network is often used in simple classification and regression problems. It can be used in structures that do not require sequential data, such as autoregressive models.

2. Convolutional Neural Networks (CNNs)

CNNs are widely used, especially in image processing problems. An inter-layer convolution process is applied to extract regional features of the data. Visual data generating structures such as Generative Adversarial Networks (GANs) utilize CNN structures.

3. Recurrent Neural Networks (RNNs)

RNNs are used in problems such as sequential data processing and time series prediction. They are especially used in language models, where the order of a word is important. RNNs have a structure that can remember past data and make predictions based on this data. Models such as GPT (Generative Pre-trained Transformer) are based on improved versions of such networks.

4. Transformer Neural Networks

Transformer neural networks are among the newest structures, especially in areas such as language processing and text generation. They learn the relationships between data using a structure called the Attention mechanism. These models exhibit superior performance in applications such as Few-shot learning and Zero-shot learning.

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Neural Networks Applications

Neural networks have a wide range of applications. Here are some important use cases:

1. Image Recognition

Neural networks are used in image recognition systems. For example, a CNN can be trained to recognize objects in images. With self-supervised learning and unsupervised learning methods, neural networks can extract meaningful features from large data sets.

2. Natural Language Processing (NLP)

Neural networks have revolutionized language models. Large Language Models (LLMs) work with neural networks in text generation, language translation and dialog systems. In particular, processes such as prompt engineering and tokenization play a critical role in the success of these networks.

3. Autonomous Systems

Neural networks are also widely used in autonomous vehicles and robotic systems. With methods such as Reinforcement Learning from Human Feedback (RLHF), these systems can make better decisions by learning from their environment.

4. Health and Medicine

Neural networks also play an important role in the diagnosis and treatment of diseases. For example, a CNN model can help doctors diagnose disease by analyzing MRI or X-ray images.

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Neural Networks: The Technology Shaping the Future

Neural networks are at the center of modern artificial intelligence research and applications. Thanks to evolving algorithms and more powerful computing power, the potential of neural networks is growing every day. As new and complex problems are solved, these structures will be used in more fields and industries.

The power of neural networks lies not only in their ability to be trained with large data sets, but also in their ability to mimic the learning process of the human brain. Therefore, neural networks have an important place in both current applications and future innovations.

Conclusion

Neural networks are a powerful tool that underpins artificial intelligence and machine learning. Different types of neural networks can be used to solve various problems. As Komtaş, we offer you expert support to use neural networks technology in the most efficient way in your projects. You can contact us to learn how you can achieve success by using neural networks in your artificial intelligence projects.