Artificial intelligence (AI), a field of computer science, seeks to develop robots capable of doing activities that frequently need human ability, such as comprehending spoken language, recognising objects, and making decisions.
Deep learning is a technique for teaching deep neural networks (DNNs) or multi-layered artificial neural networks (ANNs) to execute a variety of tasks.
In order to achieve state-of-the-art performance on a variety of tasks, such as image and speech recognition, natural language processing, and game playing, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been developed using methods for training very large neural networks.
Another important area of deep learning research has been the development of new architectures and techniques for semi-supervised and unsupervised learning, which enable neural networks to learn from significant amounts of unlabeled data. New neural network designs, such as autoencoders and generative adversarial networks (GANs), have been developed as a result. They may be used for things like producing pictures and sounds and detecting anomalies.
Using methods like quantization, pruning, and model compression, recent developments in deep learning have also made it feasible to do real-time, on-device AI on limited-resource platforms, such mobile phones and embedded systems.
All things considered, deep learning has transformed the area of artificial intelligence and significantly enhanced the performance of a variety of AI-based applications.
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