Glossary

Neural Network (NN)

Discover the power of Neural Networks—key to AI and ML innovations like computer vision, NLP, and deep learning breakthroughs.

A Neural Network (NN) is a computational model inspired by the structure and function of the human brain. It forms the backbone of most deep learning (DL) models and is a fundamental concept in modern Artificial Intelligence (AI). NNs are designed to recognize patterns in data by processing information through interconnected layers of nodes, or "neurons." This structure allows them to learn from vast amounts of data, making them incredibly powerful for complex tasks like image recognition and natural language processing (NLP).

How Do Neural Networks Work?

A neural network consists of three main types of layers: an input layer, one or more hidden layers, and an output layer. Each layer contains neurons that are connected to neurons in the subsequent layer.

  1. Input Layer: This layer receives the initial data, such as the pixels of an image or the words in a sentence.
  2. Hidden Layers: These are the intermediate layers between the input and output. This is where most of the computation occurs. Each neuron applies a mathematical transformation to its inputs, which involves learning model weights and an activation function like ReLU or Sigmoid to determine its output. Networks with multiple hidden layers are known as "deep" neural networks.
  3. Output Layer: This final layer produces the result, such as a classification label or a predicted value.

The learning process, known as training, involves feeding the network large datasets. The network makes a prediction, compares it to the actual outcome, and calculates an error using a loss function. It then uses an algorithm called backpropagation to adjust the weights of its connections to minimize this error over many iterations, or epochs. This process is guided by an optimization algorithm like Adam.

Types and Applications of Neural Networks

Neural Networks are incredibly versatile and have been adapted into various specialized architectures. Here are two key examples:

  1. Computer Vision (CV): Convolutional Neural Networks (CNNs) are the dominant force in computer vision.

  2. Natural Language Processing (NLP): NNs, including Recurrent Neural Networks (RNNs) and Transformers, have revolutionized how machines process language.

Tools and Frameworks

Developing NNs is made accessible by powerful tools and frameworks.

  • Libraries: Frameworks like PyTorch and TensorFlow provide the essential building blocks for creating and training NNs. You can learn more at the official PyTorch and TensorFlow websites.
  • Platforms: Ultralytics HUB offers an integrated platform for training YOLO models, managing datasets, and simplifying the model deployment process.
  • Pre-trained Models: Many researchers and developers start with pre-trained models available from hubs like Hugging Face or within the Ultralytics ecosystem. These models often only require fine-tuning on a specific dataset, saving significant time and computational resources. You can find comparisons between different YOLO models in our documentation.

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