Deep Learning: Powering Advanced AI

Glossary/D/Deep Learning

What is Deep Learning?

Deep Learning is a subfield of Machine Learning (ML) that focuses on developing and training artificial neural networks with multiple layers to learn and understand complex patterns in data. It is inspired by the structure and function of the human brain, with each layer of the neural network processing information and passing it to the next layer.

The key advantage of deep learning is its ability to automatically learn hierarchical representations of data, enabling the network to recognize intricate patterns and make accurate predictions. Deep learning architectures, such as Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data, have shown remarkable success in various domains.

Related terms

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a specialized type of neural network designed to process an...

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are a type of neural network commonly used for sequential data proc...

Not to be confused with:

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a specialized type of neural network designed to process an...

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are a type of neural network commonly used for sequential data proc...

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a specialized type of neural network designed to process an...

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are a type of neural network commonly used for sequential data proc...

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a specialized type of neural network designed to process an...

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are a type of neural network commonly used for sequential data proc...

In deep learning, the network learns by adjusting the weights and biases of its interconnected nodes, known as neurons, during a process called training. This training involves presenting the network with a large labeled dataset and optimizing an objective function, typically using an algorithm called backpropagation. The network iteratively adjusts its parameters to minimize the difference between its predictions and the true labels in the training data.

Even though training deep neural networks requires substantial computational resources and extensive labeled data, its ability to extract complex representations from data has unlocked new possibilities for solving intricate problems and has the potential to drive further advancements in AI.

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