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).
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.
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.
Neural Networks are incredibly versatile and have been adapted into various specialized architectures. Here are two key examples:
Computer Vision (CV): Convolutional Neural Networks (CNNs) are the dominant force in computer vision.
Natural Language Processing (NLP): NNs, including Recurrent Neural Networks (RNNs) and Transformers, have revolutionized how machines process language.
Developing NNs is made accessible by powerful tools and frameworks.