Neural networks are a type of artificial intelligence modeled after the structure and function of the human brain. They consist of interconnected nodes, called artificial neurons, that process and transmit information. These networks learn by adjusting their connections and weights based on input-output data. Neural networks have proven highly effective in tasks such as image and speech recognition, natural language processing, and others. They continue to evolve and advance, leading to exciting advancements in fields like self-driving cars and virtual personal assistants. In simple words, neural networks are computer systems that mimic the way the human brain thinks and solves problems, allowing machines to learn and improve on their own.
Neural networks come in different types, each designed for specific tasks. Common types include: Feedforward Networks, Convolutional Networks (CNNs), Recurrent Networks (RNNs), Generative Adversarial Networks (GANs), and Autoencoders. They have varying structures and functions, such as processing image data, sequences of data, or unsupervised learning. The right type of neural network to use depends on the problem and data being analyzed.
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Speech recognition is the technology that enables computers to recognize and transcribe human speech so that people can interact with computers using spoken language.
Deep Learning (DL)
Deep learning is a subfield of AI that uses algorithms inspired by the structure and function of the brain, called neural networks, to process and analyze even bigger amounts of data.