Deep learning can be considered a subset of machine learning, and it is a field based on learning and improving itself by testing computer algorithms. While machine learning uses simple concepts, deep learning works with artificial neural networks, which are planned to mimic the way humans reason and learn.
Until recently, neural networks were limited by computing power and therefore had limited complexity. However, advances in big data analytics have enabled more extensive and more sophisticated neural networks, allowing computers to observe, learn, and react to complex situations faster than humans. Deep learning can help in image classification, language translation, and speech recognition, and it can be use to solve any pattern recognition problem without human intervention.
Deep learning networks learn by discovering complex structures in the data they experience. By building computational models that consist of multiple layers of processing, networks can create various levels of abstraction to represent data.
For example, a deep learning model known as a convolutional neural network can be train using a large number (as in millions) of images, such as those of cats. This kind of neural network typically learns from the pixels in captured images. You can classify groups of pixels representing cat features; groups of features such as paws, ears, and eyes indicate the presence of a cat in the image.
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Machine learning
Artificial neural networks
Deep neural networks
Deep reinforcement learning
Machine translation
Natural language
Bioinformatics
Speech recognition
Biological systems
Generative models
Machine learning algorithms
Supervised learning
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